Lección 9 Regresiones logísticas
9.1 Tablas para las regresiones logísticas
9.1.1 Embarazos
Ca=data.frame(COVID=Casos$COVID,PrimOla=Casos$PRIMERA_OLA...3,SegundOla=Casos$SEGUNDA_OLA...4)
Co=data.frame(COVID=0,PrimOla=Controls$PRIMERA_OLA...4,SegundOla=Controls$SEGUNDA_OLA...5)
#Edades madres
Ca$Edad=Casos$Edad._años_
Co$Edad=Controls$Edad._años_
Ca$Edad.cut=cut(Ca$Edad,breaks=c(0,30,40,100),labels=c("≤30","31-40",">40"))
Co$Edad.cut=cut(Co$Edad,breaks=c(0,30,40,100),labels=c("≤30","31-40",">40"))
#Etnia
Ca$Etnias=as.factor(Casos$Etnia)
Ca$Etnias=relevel(Ca$Etnias,"Blanca")
Co$Etnias=Controls$Etnia
Co$Etnias[Co$Etnias=="Asiática"]="Asia"
Co$Etnias=as.factor(Co$Etnia)
Co$Etnias=relevel(Co$Etnias,"Blanca")
#Tabaco
Ca$Fumadora=Casos$FUMADORA_CAT
Co$Fumadora=Controls$Fumadora_cat
#Obesidad
Ca$Obesidad=as.numeric(as.factor(Casos$Obesidad))-1
Co$Obesidad=as.numeric(as.factor(Controls$Obesidad))-1
#Hipertensión pregestacional
Ca$Hipertension.Pre=as.numeric(as.factor(Casos$Hipertensión.pregestacional))-1
Co$Hipertension.Pre=as.numeric(as.factor(Controls$Hipertensión.pregestacional))-1
#Diabetes
Ca$Diabetes=as.numeric(as.factor(Casos$DIABETES))-1
Co$Diabetes=as.numeric(as.factor(Controls$Diabetes))-1
#ECC
Ca$ECC=as.numeric(as.factor(Casos$Enfermedad.cardiaca.crónica))-1
Co$ECC=as.numeric(as.factor(Controls$ENF.CARDIACA.TODO))-1
#ECP no asma
Ca$ECPnoA=as.numeric(as.factor(Casos$Enfermedad.pulmonar.crónica.no.asma))-1
Co$ECPnoA=as.numeric(as.factor(Controls$Enfermedad.pulmonar.crónica.no.asma))-1
#Asma
Ca$Asma=as.numeric(as.factor(Casos$Diagnóstico.clínico.de.Asma))-1
Co$Asma=as.numeric(as.factor(Controls$Diagnóstico.clínico.de.Asma))-1
#ECP total
INA=Ca$ECPnoA
IA=Ca$Asma
I=rep(NA,length(INA))
for (i in 1:length(INA)){I[i]=max(INA[i],IA[i],na.rm=TRUE)}
Ca$ECP.Tot=I
NINA=Co$ECPnoA
NIA=Co$Asma
NI=rep(NA,length(NINA))
for (i in 1:length(NINA)){NI[i]=max(NINA[i],NIA[i],na.rm=TRUE)}
NI[NI==-Inf]=NA
Ca$ECP.Tot=I
Co$ECP.Tot=NI
#Nulipara
Ca$Nulipara=Casos$NULIPARA
Co$Nulipara=Controls$Nuliparous
#Gestacion multiple
Ca$GM=as.numeric(as.factor(Casos$Gestación.Múltiple))-1
Co$GM=as.numeric(as.factor(Controls$Gestación.Múltiple))-1
#Anomalia congenita
Ca$AnCon=as.numeric(as.factor(Casos$Diagnóstico.de.malformación.ecográfica._.semana.20._))-1
Co$AnCon=as.numeric(as.factor(Controls$Diagnóstico.de.malformación.ecográfica._.semana.20._))-1
#Retraso crecimiento
Ca$RetrasoCF=as.numeric(as.factor(Casos$Defecto.del.crecimiento.fetal..en.tercer.trimestre._.CIR._.))-1
Co$RetrasoCF=as.numeric(as.factor(Controls$Defecto.del.crecimiento.fetal..en.tercer.trimestre._.CIR._.))-1
#Diabetes gestacional
Ca$Diabetes.Gest=as.numeric(as.factor(Casos$Diabetes.gestacional))-1
Co$Diabetes.Gest=as.numeric(as.factor(Controls$Diabetes.gestacional))-1
#Hipertension gestacional
Ca$Hipertension.Gest=as.numeric(as.factor(Casos$Hipertensión.gestacional))-1
Co$Hipertension.Gest=as.numeric(as.factor(Controls$Hipertensión.gestacional))-1
#Preeclampsia
Ca$Preeclampsia=Casos$PREECLAMPSIA_ECLAMPSIA_TOTAL
Co$Preeclampsia=Controls$PREECLAMPSIA
#Preeclampsia grave
Ca$Preeclampsia.grave=as.numeric(as.factor(Casos$Preeclampsia.grave_HELLP_ECLAMPSIA))-1
NI1=Controls$preeclampsia_severa
NI2=Controls$Preeclampsia.grave.No.HELLP
NI=rep(NA,length(NI1))
for (i in 1:length(NI1)){NI[i]=max(NI1[i],NI2[i],na.rm=TRUE)}
Co$Preeclampsia.grave=as.numeric(as.factor(NI))-1
#Rotura prematura de bolsa
Ca$Rotura=as.numeric(as.factor(Casos$Bolsa.rota.anteparto))-1
Co$Rotura=as.numeric(as.factor(Controls$Bolsa.rota.anteparto))-1
#Edad gestacional en el momento del parto
Ca$EG.Parto=Casos$EG_TOTAL_PARTO
Co$EG.Parto=Controls$EDAD_GEST
#UCI madre
Ca$UCI=Casos$UCI
Co$UCI=Controls$UCI...9
#UCI antes del parto
Ca$UCI.antes=abs(as.numeric(as.factor(Casos$UCI_ANTES.DESPUES.DEL.PARTO))-2)
Co$UCI.antes=NA
#Algún feto muerto intraútero
I=Casos$Feto.muerto.intraútero
I[I=="Sí"]=1
I[I=="No"]=0
Ca$FMI=I
NI1=Controls$Feto.vivo...194
NI1[NI1=="Sí"]=0
NI1[NI1=="No"]=1
NI2=Controls$Feto.vivo...245
NI2[NI2=="Sí"]=0
NI2[NI2=="No"]=1
NI=NI1
for (i in 1:length(NI1)){NI[i]=max(NI1[i],NI2[i],na.rm=TRUE)}
Co$FMI=NI
#Inicio de parto
Ca$Inicio.parto=Casos$Inicio.de.parto
Ca$Inicio.parto[Ca$Inicio.parto=="Cesárea"]="Cesárea programada"
Ca$Inicio.parto=ordered(Ca$Inicio.parto,levels=c("Espontáneo", "Inducido","Cesárea programada"))
Co$Inicio.parto=ordered(Controls$Inicio.de.parto,levels=c("Espontáneo", "Inducido","Cesárea programada"))
#Tipo de parto
I.In=Casos$Inicio.de.parto
I.In[I.In=="Cesárea"]="Cesárea programada"
I=Casos$Tipo.de.parto
I[I.In=="Cesárea programada"]="Cesárea programada"
I[I=="Cesárea"]="Cesárea urgente"
Ca$Tipo.parto=ordered(I,levels=c("Eutocico","Instrumental","Cesárea programada", "Cesárea urgente"))
NI.In=Controls$Inicio.de.parto
NI=Controls$Tipo.de.parto
NI[NI.In=="Cesárea programada"]="Cesárea programada"
NI[NI=="Cesárea"]="Cesárea urgente"
Co$Tipo.parto=ordered(NI,levels=c("Eutocico","Instrumental","Cesárea programada", "Cesárea urgente"))
#HPP
Ca$HPP=Casos$Hemorragia.postparto
Ca$HPPsino=Ca$HPP
Ca$HPPsino[!is.na(Ca$HPPsino)& Ca$HPPsino!="No"]="Sí"
Ca$HPP=ordered(Ca$HPP,levels=names(table(Ca$HPP))[c(4,2,3,1)])
Co$HPP=Controls$Hemorragia.postparto
Co$HPPsino=Co$HPP
Co$HPPsino[!is.na(Co$HPPsino)& Co$HPPsino!="No"]="Sí"
Co$HPP=ordered(Co$HPP,levels=names(table(Co$HPP))[c(4,2,3,1)])
#Prematurez
Ca$Prematuro=Casos$PREMATURO
Co$Prematuro=Controls$Preterm.deliveries
#Eventos trombóticos
Ca$Event.Tromb=Casos$EVENTOS_TROMBO_TOTALES
NI1=Controls$DVT
NI2=Controls$PE
NI=rep(NA,length(NI1))
for (i in 1:length(NI1)){NI[i]=max(NI1[i],NI2[i],na.rm=TRUE)}
Co$Event.Tromb=NI
#Eventos hemorragicos
Ca$Event.Hem=Casos$EVENTOS_HEMORRAGICOS_TOTAL
Co$Event.Hem=Controls$eventos_hemorragicos
#Hemorragia postparto
I=Casos$Hemorragia.postparto
Ca$Hemo.PP=ordered(I,levels=names(table(I))[c(4,2,3,1)],labels=0:3)
NI=Controls$Hemorragia.postparto
Co$Hemo.PP=ordered(NI,levels=names(table(NI))[c(4,2,3,1)],labels=0:3)
#Algún hijo con APGAR.5<7
I=rep(0,dim(Casos)[1])
for(i in 1:dim(Casos)[1]){I[i]=min(Casos$APGAR.5...126[i],Casos$APGAR.5...150[i],na.rm=TRUE)}
I[I>10]=NA
I.cut=cut(I,breaks=c(-1,6,20),labels=c(1,0))
Ca$APGAR=I.cut
NI=rep(0,dim(Controls)[1])
for(i in 1:dim(Controls)[1]){I[i]=min(Controls$APGAR.5...200[i],Controls$APGAR.5...249[i],na.rm=TRUE)}
NI[NI>10]=NA
NI.cut=cut(NI,breaks=c(-1,6,20),labels=c(1,0))
Co$APGAR=NI.cut
#Algún hijo vivo en UCIN
Casos[Casos$Feto.muerto.intraútero=="Sí",]$Ingreso.en.UCIN=NA
I=rep(0,dim(Casos)[1])
for(i in 1:dim(Casos)[1]){I[i]=max(Casos$Ingreso.en.UCIN[i],Casos$Ingreso.en.UCI[i],na.rm=TRUE)}
Ca$UCIN=as.numeric(as.factor(I))-1
Controls[Controls$Feto.vivo...194=="No",]$Ingreso.en.UCI...213=NA
NI=rep(0,dim(Controls)[1])
for(i in 1:dim(Controls)[1]){NI[i]=max(Controls $Ingreso.en.UCI...213[i], Controls$Ingreso.en.UCI...260[i],na.rm=TRUE)}
Co$UCIN=as.numeric(as.factor(NI))-1
#Robson
Ca$Robson=Casos$Robson
Co$Robson=Controls$Robson
# Cesáreas
Ca$Cesárea=Casos$Cesárea
Co$Cesárea=Controls$Cesárea
#Sintomatología
Ca$Sint=Casos$SINTOMAS_CAT
Co$Sint=0
#Momento del diagnóstico
Ca$PreP=NA
Ca$PreP[round((Casos$EG_TOTAL_PARTO-Casos$EDAD.GEST.TOTAL)*7)>2]="Anteparto"
Ca$PreP[round((Casos$EG_TOTAL_PARTO-Casos$EDAD.GEST.TOTAL)*7)<=2]="Periparto"
Co$PreP="No"
#Sintomatología anteparto
Ca$SintPre=Ca$Sint
Ca$SintPre[Ca$PreP=="Periparto"]=NA
Co$SintPre=0
#Sintomatología periparto
Ca$SintPeri=Ca$Sint
Ca$SintPeri[Ca$PreP=="Anteparto"]=NA
Co$SintPeri=0
#Tabla global
DFL.madres=rbind(Ca,Co)
DFL.madres$PreP=factor(DFL.madres$PreP)
DFL.madres$SintPre=factor(DFL.madres$SintPre)
DFL.madres$SintPeri=factor(DFL.madres$SintPeri)
DFL.madres$PreP=relevel(DFL.madres$PreP,"No")
DFL.madres$Sint=factor(DFL.madres$Sint)
DFL.madres$Cesárea=factor(DFL.madres$Cesárea)
DFL.madres$HPPsino=as.numeric(plyr::revalue(DFL.madres$HPPsino, c("No"=0, "Sí"=1)))9.1.2 Hijos
CaN=data.frame(COVID=1,PrimOla=c(Casos$PRIMERA_OLA...3,CasosGM$PRIMERA_OLA...3),SegundOla=c(Casos$SEGUNDA_OLA...4,CasosGM$SEGUNDA_OLA...4))
CoN=data.frame(COVID=0,PrimOla=c(Controls$PRIMERA_OLA...4,ControlsGM$PRIMERA_OLA...4),SegundOla=c(Controls$SEGUNDA_OLA...5,ControlsGM$SEGUNDA_OLA...5))
#Edades madres
CaN$Edad=c(Casos$Edad._años_,CasosGM$Edad._años_)
CoN$Edad=c(Controls$Edad._años_,ControlsGM$Edad._años_)
CaN$Edad.cut=cut(CaN$Edad,breaks=c(0,30,40,100),labels=c("≤30","31-40",">40"))
CoN$Edad.cut=cut(CoN$Edad,breaks=c(0,30,40,100),labels=c("≤30","31-40",">40"))
#Etnia
CaN$Etnias=as.factor(c(Casos$Etnia,CasosGM$Etnia))
CaN$Etnias=relevel(CaN$Etnias,"Blanca")
CoN$Etnias=c(Controls$Etnia,ControlsGM$Etnia)
CoN$Etnias[CoN$Etnias=="Asiática"]="Asia"
CoN$Etnias=as.factor(CoN$Etnia)
CoN$Etnias=relevel(CoN$Etnias,"Blanca")
#Tabaco
CaN$Fumadora=c(Casos$FUMADORA_CAT,CasosGM$FUMADORA_CAT)
CoN$Fumadora=c(Controls$Fumadora_cat,ControlsGM$Fumadora_cat)
#Obesidad
CaN$Obesidad=as.numeric(as.factor(c(Casos$Obesidad,CasosGM$Obesidad)))-1
CoN$Obesidad=as.numeric(as.factor(c(Controls$Obesidad,ControlsGM$Obesidad)))-1
#Hipertensión pregestacional
CaN$Hipertension.Pre=as.numeric(as.factor(c(Casos$Hipertensión.pregestacional,CasosGM$Hipertensión.pregestacional)))-1
CoN$Hipertension.Pre=as.numeric(as.factor(c(Controls$Hipertensión.pregestacional,ControlsGM$Hipertensión.pregestacional)))-1
#Diabetes
CaN$Diabetes=as.numeric(as.factor(c(Casos$DIABETES,CasosGM$DIABETES)))-1
CoN$Diabetes=as.numeric(as.factor(c(Controls$Diabetes,ControlsGM$Diabetes)))-1
#ECC
CaN$ECC=as.numeric(as.factor(c(Casos$Enfermedad.cardiaca.crónica,CasosGM$Enfermedad.cardiaca.crónica)))-1
CoN$ECC=as.numeric(as.factor(c(Controls$ENF.CARDIACA.TODO,ControlsGM$ENF.CARDIACA.TODO)))-1
#ECP no asma
CaN$ECPnoA=as.numeric(as.factor(c(Casos$Enfermedad.pulmonar.crónica.no.asma,CasosGM$Enfermedad.pulmonar.crónica.no.asma)))-1
CoN$ECPnoA=as.numeric(as.factor(c(Controls$Enfermedad.pulmonar.crónica.no.asma,ControlsGM$Enfermedad.pulmonar.crónica.no.asma)))-1
#Asma
CaN$Asma=as.numeric(as.factor(c(Casos$Diagnóstico.clínico.de.Asma,CasosGM$Diagnóstico.clínico.de.Asma)))-1
CoN$Asma=as.numeric(as.factor(c(Controls$Diagnóstico.clínico.de.Asma,ControlsGM$Diagnóstico.clínico.de.Asma)))-1
#ECP total
INA=CaN$ECPnoA
IA=CaN$Asma
I=rep(NA,length(INA))
for (i in 1:length(INA)){I[i]=max(INA[i],IA[i],na.rm=TRUE)}
CaN$ECP.Tot=I
NINA=CoN$ECPnoA
NIA=CoN$Asma
NI=rep(NA,length(NINA))
for (i in 1:length(NINA)){NI[i]=max(NINA[i],NIA[i],na.rm=TRUE)}
NI[NI==-Inf]=NA
CoN$ECP.Tot=NI
#Nulipara
CaN$Nulipara=c(Casos$NULIPARA,CasosGM$NULIPARA)
CoN$Nulipara=c(Controls$Nuliparous,ControlsGM$Nuliparous)
#Gestacion multiple
CaN$GM=as.numeric(as.factor(c(Casos$Gestación.Múltiple,CasosGM$Gestación.Múltiple)))-1
CoN$GM=as.numeric(as.factor(c(Controls$Gestación.Múltiple,ControlsGM$Gestación.Múltiple)))-1
#APGAR
I=c(Casos$APGAR.5...126,CasosGM$APGAR.5...150)
I[I==19]=NA
NI=c(Controls$APGAR.5...200,ControlsGM$APGAR.5...249)
I.cut=cut(I,breaks=c(-1,7,20),labels=c(1,0))
NI.cut=cut(NI,breaks=c(-1,7,20),labels=c(1,0))
CaN$APGAR=I.cut
CoN$APGAR=NI.cut
#UCIN
Casos[Casos$Feto.muerto.intraútero=="Sí",]$Ingreso.en.UCIN=NA
Controls[Controls$Feto.vivo...194=="No",]$Ingreso.en.UCI...213=NA
I=c(Casos$Ingreso.en.UCIN,CasosGM$Ingreso.en.UCI)
NI=c(Controls$Ingreso.en.UCI...213,ControlsGM$Ingreso.en.UCI...260)
CaN$UCIN=as.factor(I)
CoN$UCIN=as.factor(NI)
#Pesos
CaN$Pesos=c(Casos$Peso._gramos_...125,CasosGM$Peso._gramos_...148)
CoN$Pesos=c(Controls$Peso._gramos_...198,ControlsGM$Peso._gramos_...247)
#Prematurez
CaN$Prematuro=c(Casos$PREMATURO,CasosGM$PREMATURO)
CoN$Prematuro=c(Controls$Preterm.deliveries,ControlsGM$Preterm.deliveries)
#Anomalía congénita
CaN$AnCon=as.numeric(as.factor(c(Casos$Diagnóstico.de.malformación.ecográfica._.semana.20._,CasosGM$Diagnóstico.de.malformación.ecográfica._.semana.20._)))-1
CoN$AnCon=as.numeric(as.factor(c(Controls$Diagnóstico.de.malformación.ecográfica._.semana.20._,ControlsGM$Diagnóstico.de.malformación.ecográfica._.semana.20._)))-1
#Retraso
CaN$RetrasoCF=as.numeric(as.factor(c(Casos$Defecto.del.crecimiento.fetal..en.tercer.trimestre._.CIR._.,CasosGM$Defecto.del.crecimiento.fetal..en.tercer.trimestre._.CIR._.)))-1
CoN$RetrasoCF=as.numeric(as.factor(c(Controls$Defecto.del.crecimiento.fetal..en.tercer.trimestre._.CIR._.,ControlsGM$Defecto.del.crecimiento.fetal..en.tercer.trimestre._.CIR._.)))-1
#Preeclampsia
CaN$Preeclampsia=c(Casos$PREECLAMPSIA_ECLAMPSIA_TOTAL,CasosGM$PREECLAMPSIA_ECLAMPSIA_TOTAL)
CoN$Preeclampsia=c(Controls$PREECLAMPSIA,ControlsGM$PREECLAMPSIA)
#Preeclampsia grave
CaN$Preeclampsia.grave=as.numeric(as.factor(c(Casos$Preeclampsia.grave_HELLP_ECLAMPSIA,CasosGM$Preeclampsia.grave_HELLP_ECLAMPSIA)))-1
NI1=c(Controls$preeclampsia_severa,ControlsGM$preeclampsia_severa)
NI2=c(Controls$Preeclampsia.grave.No.HELLP,ControlsGM$Preeclampsia.grave.No.HELLP)
NI=rep(NA,length(NI1))
for (i in 1:length(NI1)){NI[i]=max(NI1[i],NI2[i],na.rm=TRUE)}
CoN$Preeclampsia.grave=as.numeric(as.factor(NI))-1
#Sintomatología
CaN$Sint=c(Casos$SINTOMAS_CAT,CasosGM$SINTOMAS_CAT)
CoN$Sint=0
#Momento del diagnóstico
Dif=round(c((Casos$EG_TOTAL_PARTO-Casos$EDAD.GEST.TOTAL)*7,(CasosGM$EG_TOTAL_PARTO-CasosGM$EDAD.GEST.TOTAL)*7))
CaN$PreP=NA
CaN$PreP[Dif>2]="Anteparto"
CaN$PreP[Dif<=2]="Periparto"
CoN$PreP="No"
#Sintomatología anteparto
CaN$SintPre=CaN$Sint
CaN$SintPre[CaN$PreP=="Periparto"]=NA
CoN$SintPre=0
#Sintomatología periparto
CaN$SintPeri=CaN$Sint
CaN$SintPeri[CaN$PreP=="Anteparto"]=NA
CoN$SintPeri=0
#Tabla global
DFL.niños=rbind(CaN,CoN)
DFL.niños$PreP=factor(DFL.niños$PreP)
DFL.niños$PreP=relevel(DFL.niños$PreP,"No")
DFL.niños$Sint=factor(DFL.niños$Sint)
DFL.niños$CovGrave=1
DFL.niños$CovGrave[DFL.niños$Sint!=3]=0
DFL.niños$SintPre=factor(DFL.niños$SintPre)
DFL.niños$SintPeri=factor(DFL.niños$SintPeri)9.2 Regresiones
9.2.1 Influencia de características previas en la COVID
9.2.1.1 COVID sí o no
Cuando la variable dependiente es binaria (por ejemplo, COVID Sí o No), en las tablas como la que sigue:
- ORa: Odds ratio ajustada de tener COVID.
- IC 95%: Intervalo de confianza del 95% para la ORa
- p-valor: p-valor del contraste bilateral si ORa=1 o no
- “rel. a”: En la variables independientes politómicas, respecto de qué nivel se calculan las ORa
mod.COVID=glm(COVID~Edad.cut+Etnias+Fumadora+Obesidad+Hipertension.Pre+Diabetes+ECP.Tot+ECC+Nulipara+GM,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"HTA",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Edad: 31-40 años rel. a 18-30 | 1.068 | 0.905 | 1.261 | 0.4370047 |
| Edad: >40 años rel. a 18-30 | 1.534 | 1.099 | 2.142 | 0.01191801 |
| Etnia: Árabe rel. a blanca | 1.400 | 1.072 | 1.828 | 0.01360259 |
| Etnia: Asiática rel. a blanca | 1.792 | 1.139 | 2.817 | 0.01157591 |
| Etnia: Latinoamericana rel. a blanca | 4.624 | 3.715 | 5.754 | <1e-08 |
| Etnia: Negra rel. a blanca | 2.412 | 1.410 | 4.125 | 0.00129946 |
| Tabaquismo | 0.916 | 0.720 | 1.165 | 0.47520105 |
| Obesidad | 1.065 | 0.869 | 1.305 | 0.54410479 |
| HTA | 1.769 | 0.783 | 3.999 | 0.17028827 |
| DM | 1.084 | 0.633 | 1.856 | 0.76846399 |
| EPC | 1.246 | 0.849 | 1.828 | 0.26110956 |
| ECC | 0.469 | 0.218 | 1.008 | 0.05240528 |
| Nulípara | 0.924 | 0.789 | 1.082 | 0.32591786 |
| Gestación múltiple | 1.153 | 0.669 | 1.986 | 0.60925035 |
- Valores VIF: todos entre 1.0042 y 1.2729.
9.2.1.2 COVID según gravedad
Cuando la variable dependiente es politómica (por ejemplo, COVID No, asintómatica, leve, pulmonía), en las tablas como la que sigue:
- La primera columna es el nivel que se compara contra el nivel basal (en este caso, el nivel de gravedad contra no COVID)
- ORa: La odds ratio ajustada \[ \frac{\text{Odds(Nivel de interés|Variable sí)}/\text{Odds(Nivel basal|Variable sí)}}{\text{Odds(Nivel de interés|Variable no)}/\text{Odds(Nivel basal|Variable no)}} \]
- IC 95%: Intervalo de confianza del 95% para la ORa
- p-valor: el p-valor del contraste bilateral con hipótesis nula ORa=1
- “rel. a”: En la variables independientes politómicas, respecto de qué nivel se calculan las ORa (a qué corresponde “Variable no”)
mod.COVID=multinom(Sint~Edad.cut+Etnias+Fumadora+Obesidad+Hipertension.Pre+Diabetes+ECP.Tot+ECC+Nulipara+GM,data=DFL.madres,family=binomial(link="logit"))## # weights: 64 (45 variable)
## initial value 4149.179023
## iter 10 value 3545.807480
## iter 20 value 3505.449388
## iter 30 value 3501.745288
## iter 40 value 3501.587703
## final value 3501.587484
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado$nivel[Resultado$nivel=="1"]="Asintomática"
Resultado$nivel[Resultado$nivel=="2"]="Leve"
Resultado$nivel[Resultado$nivel=="3"]="Pulmonía"
Resultado=Resultado[!is.na(Resultado$OR),]
rownames(Resultado)=NULL
Resultado$variable=c( "Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"HTA",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple"
)
colnames(Resultado)=c("COVID","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
Resultado %>%
kbl(col.names = NULL) %>%
kable_styling()| Asintomática | Edad: 31-40 años rel. a 18-30 | 1.051 | 0.858 | 1.288 | 0.6283085 |
| Asintomática | Edad: >40 años rel. a 18-30 | 1.310 | 0.866 | 1.980 | 0.2006256 |
| Asintomática | Etnia: Árabe rel. a blanca | 1.617 | 1.180 | 2.216 | 0.0027901 |
| Asintomática | Etnia: Asiática rel. a blanca | 1.924 | 1.135 | 3.260 | 0.0150689 |
| Asintomática | Etnia: Latinoamericana rel. a blanca | 2.575 | 1.960 | 3.383 | 0.0000000 |
| Asintomática | Etnia: Negra rel. a blanca | 2.823 | 1.548 | 5.147 | 0.0007109 |
| Asintomática | Tabaquismo | 0.953 | 0.707 | 1.283 | 0.7506902 |
| Asintomática | Obesidad | 0.936 | 0.726 | 1.207 | 0.6104844 |
| Asintomática | HTA | 2.298 | 0.876 | 6.028 | 0.0909850 |
| Asintomática | DM | 0.876 | 0.434 | 1.767 | 0.7109273 |
| Asintomática | EPC | 0.823 | 0.485 | 1.398 | 0.4713399 |
| Asintomática | ECC | 0.256 | 0.079 | 0.829 | 0.0230560 |
| Asintomática | Nulípara | 0.937 | 0.772 | 1.137 | 0.5073544 |
| Asintomática | Gestación múltiple | 0.636 | 0.284 | 1.421 | 0.2697456 |
| Leve | Edad: 31-40 años rel. a 18-30 | 0.954 | 0.768 | 1.185 | 0.6690876 |
| Leve | Edad: >40 años rel. a 18-30 | 1.527 | 0.999 | 2.334 | 0.0507387 |
| Leve | Etnia: Árabe rel. a blanca | 1.190 | 0.819 | 1.729 | 0.3626122 |
| Leve | Etnia: Asiática rel. a blanca | 1.084 | 0.540 | 2.174 | 0.8213602 |
| Leve | Etnia: Latinoamericana rel. a blanca | 5.667 | 4.377 | 7.338 | 0.0000000 |
| Leve | Etnia: Negra rel. a blanca | 2.274 | 1.150 | 4.498 | 0.0182413 |
| Leve | Tabaquismo | 0.903 | 0.652 | 1.250 | 0.5379419 |
| Leve | Obesidad | 1.102 | 0.848 | 1.430 | 0.4677785 |
| Leve | HTA | 1.229 | 0.426 | 3.548 | 0.7026114 |
| Leve | DM | 1.251 | 0.639 | 2.452 | 0.5134259 |
| Leve | EPC | 1.729 | 1.102 | 2.714 | 0.0172388 |
| Leve | ECC | 0.609 | 0.228 | 1.626 | 0.3224511 |
| Leve | Nulípara | 0.992 | 0.807 | 1.220 | 0.9374265 |
| Leve | Gestación múltiple | 1.756 | 0.925 | 3.333 | 0.0851636 |
| Pulmonía | Edad: 31-40 años rel. a 18-30 | 1.527 | 1.114 | 2.092 | 0.0084593 |
| Pulmonía | Edad: >40 años rel. a 18-30 | 2.540 | 1.437 | 4.492 | 0.0013463 |
| Pulmonía | Etnia: Árabe rel. a blanca | 1.155 | 0.643 | 2.077 | 0.6292243 |
| Pulmonía | Etnia: Asiática rel. a blanca | 3.443 | 1.673 | 7.083 | 0.0007829 |
| Pulmonía | Etnia: Latinoamericana rel. a blanca | 9.866 | 7.078 | 13.753 | 0.0000000 |
| Pulmonía | Etnia: Negra rel. a blanca | 1.242 | 0.359 | 4.291 | 0.7324071 |
| Pulmonía | Tabaquismo | 0.811 | 0.490 | 1.343 | 0.4158349 |
| Pulmonía | Obesidad | 1.400 | 0.997 | 1.966 | 0.0521000 |
| Pulmonía | HTA | 1.783 | 0.526 | 6.043 | 0.3532842 |
| Pulmonía | DM | 1.327 | 0.561 | 3.139 | 0.5201653 |
| Pulmonía | EPC | 1.377 | 0.715 | 2.652 | 0.3387431 |
| Pulmonía | ECC | 0.844 | 0.250 | 2.844 | 0.7845423 |
| Pulmonía | Nulípara | 0.727 | 0.534 | 0.990 | 0.0429400 |
| Pulmonía | Gestación múltiple | 1.484 | 0.583 | 3.775 | 0.4075894 |
9.2.2 RCIU
9.2.2.1 COVID Sí o No
mod.COVID=glm(RetrasoCF~COVID+Etnias+Fumadora+Obesidad+Diabetes+GM+AnCon+Preeclampsia.grave
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Anomalías congénitas",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 1.333 | 0.872 | 2.040 | 0.18477486 |
| Etnia: Árabe rel. a blanca | 0.906 | 0.423 | 1.938 | 0.79846113 |
| Etnia: Asiática rel. a blanca | 0.355 | 0.048 | 2.617 | 0.30932907 |
| Etnia: Latinoamericana rel. a blanca | 0.710 | 0.399 | 1.263 | 0.24379986 |
| Etnia: Negra rel. a blanca | 0.444 | 0.060 | 3.306 | 0.42761518 |
| Tabaquismo | 2.063 | 1.214 | 3.506 | 0.00742718 |
| Obesidad | 0.656 | 0.354 | 1.213 | 0.17874134 |
| DM | 1.884 | 0.562 | 6.314 | 0.30491939 |
| Gestación múltiple | 3.565 | 1.530 | 8.305 | 0.00322332 |
| Anomalías congénitas | 2.389 | 0.710 | 8.041 | 0.15971288 |
| PE con CG | 4.831 | 1.754 | 13.304 | 0.00230862 |
9.2.2.2 COVID asintomática, leve o grave contra No COVID
mod.COVID=glm(RetrasoCF~Sint+Etnias+Fumadora+Obesidad+Diabetes+GM+AnCon+Preeclampsia.grave
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Anomalías congénitas",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 1.656 | 1.016 | 2.698 | 0.04301432 |
| COVID leve | 1.124 | 0.638 | 1.980 | 0.68522746 |
| COVID grave | 0.870 | 0.354 | 2.140 | 0.76238715 |
| Etnia: Árabe rel. a blanca | 0.886 | 0.414 | 1.899 | 0.75637772 |
| Etnia: Asiática rel. a blanca | 0.352 | 0.048 | 2.601 | 0.30594834 |
| Etnia: Latinoamericana rel. a blanca | 0.775 | 0.433 | 1.388 | 0.39114031 |
| Etnia: Negra rel. a blanca | 0.418 | 0.056 | 3.125 | 0.39515685 |
| Tabaquismo | 2.073 | 1.220 | 3.522 | 0.00706117 |
| Obesidad | 0.668 | 0.361 | 1.236 | 0.19857824 |
| DM | 1.973 | 0.588 | 6.618 | 0.27097622 |
| Gestación múltiple | 3.775 | 1.613 | 8.838 | 0.00220294 |
| Anomalías congénitas | 2.350 | 0.695 | 7.947 | 0.16933445 |
| PE con CG | 5.194 | 1.874 | 14.391 | 0.00153295 |
9.2.2.3 COVID ante o periparto contra No COVID
mod.COVID=glm(RetrasoCF~PreP+Etnias+Fumadora+Obesidad+Diabetes+GM+AnCon+Preeclampsia.grave
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Anomalías congénitas",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 1.657 | 1.034 | 2.656 | 0.03576678 |
| COVID periparto | 0.967 | 0.547 | 1.710 | 0.90792972 |
| Etnia: Árabe rel. a blanca | 0.910 | 0.425 | 1.946 | 0.80754908 |
| Etnia: Asiática rel. a blanca | 0.352 | 0.048 | 2.598 | 0.30604986 |
| Etnia: Latinoamericana rel. a blanca | 0.668 | 0.374 | 1.194 | 0.17340423 |
| Etnia: Negra rel. a blanca | 0.431 | 0.058 | 3.223 | 0.4122197 |
| Tabaquismo | 2.058 | 1.210 | 3.500 | 0.0077302 |
| Obesidad | 0.660 | 0.357 | 1.221 | 0.1853894 |
| DM | 1.761 | 0.524 | 5.921 | 0.36042301 |
| Gestación múltiple | 3.405 | 1.452 | 7.986 | 0.00483646 |
| Anomalías congénitas | 2.403 | 0.713 | 8.100 | 0.1574483 |
| PE con CG | 4.909 | 1.768 | 13.632 | 0.00226132 |
9.2.2.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(RetrasoCF~SintPre+Etnias+Fumadora+Obesidad+Diabetes+GM+AnCon+Preeclampsia.grave
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Anomalías congénitas",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 3.627 | 2.013 | 6.534 | 1.791e-05 |
| COVID anteparto leve | 1.138 | 0.605 | 2.140 | 0.68932665 |
| COVID anteparto grave | 1.119 | 0.423 | 2.964 | 0.82037127 |
| Etnia: Árabe rel. a blanca | 0.647 | 0.251 | 1.669 | 0.36815973 |
| Etnia: Asiática rel. a blanca | 0.415 | 0.055 | 3.120 | 0.39270429 |
| Etnia: Latinoamericana rel. a blanca | 0.634 | 0.317 | 1.267 | 0.19719362 |
| Etnia: Negra rel. a blanca | 0.455 | 0.059 | 3.503 | 0.44982948 |
| Tabaquismo | 1.992 | 1.111 | 3.574 | 0.0207961 |
| Obesidad | 0.593 | 0.287 | 1.226 | 0.15854743 |
| DM | 1.315 | 0.301 | 5.737 | 0.71556237 |
| Gestación múltiple | 4.746 | 1.967 | 11.455 | 0.00053078 |
| Anomalías congénitas | 2.030 | 0.452 | 9.124 | 0.35562901 |
| PE con CG | 3.973 | 1.047 | 15.082 | 0.04265145 |
9.2.2.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(RetrasoCF~SintPeri+Etnias+Fumadora+Obesidad+Diabetes+GM+AnCon+Preeclampsia.grave
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Anomalías congénitas",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 0.916 | 0.470 | 1.784 | 0.79588496 |
| COVID periparto leve | 1.238 | 0.471 | 3.253 | 0.66511747 |
| COVID periparto grave | 0.422 | 0.054 | 3.320 | 0.41234998 |
| Etnia: Árabe rel. a blanca | 1.412 | 0.574 | 3.472 | 0.45205966 |
| Etnia: Asiática rel. a blanca | 0.659 | 0.085 | 5.139 | 0.69063745 |
| Etnia: Latinoamericana rel. a blanca | 1.041 | 0.450 | 2.408 | 0.92531309 |
| Etnia: Negra rel. a blanca | 1.101 | 0.141 | 8.573 | 0.92694402 |
| Tabaquismo | 3.733 | 2.025 | 6.882 | 2.432e-05 |
| Obesidad | 0.625 | 0.284 | 1.375 | 0.2424685 |
| DM | 4.863 | 1.375 | 17.202 | 0.01412284 |
| Gestación múltiple | 2.696 | 0.767 | 9.480 | 0.12214316 |
| Anomalías congénitas | 1.494 | 0.191 | 11.685 | 0.70178367 |
| PE con CG | 8.084 | 2.178 | 30.003 | 0.00178725 |
9.2.2.6 Ola
mod.COVID=glm(RetrasoCF~SegundOla+Etnias+Fumadora+Obesidad+Diabetes+GM+AnCon+Preeclampsia.grave
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Anomalías congénitas",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 1.110 | 0.727 | 1.695 | 0.6275549 |
| Etnia: Árabe rel. a blanca | 0.921 | 0.431 | 1.967 | 0.83082203 |
| Etnia: Asiática rel. a blanca | 0.361 | 0.049 | 2.669 | 0.31846111 |
| Etnia: Latinoamericana rel. a blanca | 0.766 | 0.436 | 1.347 | 0.35449122 |
| Etnia: Negra rel. a blanca | 0.465 | 0.062 | 3.461 | 0.45434637 |
| Tabaquismo | 2.068 | 1.217 | 3.515 | 0.00723186 |
| Obesidad | 0.663 | 0.359 | 1.224 | 0.18892544 |
| DM | 1.883 | 0.563 | 6.293 | 0.30408396 |
| Gestación múltiple | 3.597 | 1.543 | 8.386 | 0.00302944 |
| Anomalías congénitas | 2.466 | 0.734 | 8.290 | 0.14440856 |
| PE con CG | 5.061 | 1.838 | 13.938 | 0.00170237 |
9.2.2.7 Infección y ola
mod.COVID=glm(RetrasoCF~SegundOla+Etnias+Fumadora+Obesidad+Diabetes+GM+AnCon+Preeclampsia.grave
,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Anomalías congénitas",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 1.105 | 0.641 | 1.903 | 0.7196443 |
| Etnia: Árabe rel. a blanca | 0.822 | 0.309 | 2.187 | 0.69417536 |
| Etnia: Asiática rel. a blanca | 0.000 | 0.000 | Inf | 0.98691955 |
| Etnia: Latinoamericana rel. a blanca | 0.662 | 0.348 | 1.257 | 0.20680448 |
| Etnia: Negra rel. a blanca | 0.000 | 0.000 | Inf | 0.98731076 |
| Tabaquismo | 0.827 | 0.317 | 2.157 | 0.69816104 |
| Obesidad | 0.824 | 0.387 | 1.757 | 0.61716274 |
| DM | 0.971 | 0.123 | 7.665 | 0.9775845 |
| Gestación múltiple | 3.294 | 1.067 | 10.175 | 0.0382417 |
| Anomalías congénitas | 3.732 | 1.070 | 13.021 | 0.03884643 |
| PE con CG | 5.147 | 1.628 | 16.272 | 0.00527114 |
9.2.3 Preeclampsia
9.2.3.1 COVID Sí o No
mod.COVID=glm(Preeclampsia~COVID+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple" )
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 1.188 | 0.835 | 1.689 | 0.33849175 |
| Edad: 31-40 años rel. a 18-30 | 1.110 | 0.766 | 1.608 | 0.58267359 |
| Edad: >40 años rel. a 18-30 | 1.382 | 0.707 | 2.702 | 0.34408982 |
| Etnia: Árabe rel. a blanca | 1.233 | 0.672 | 2.262 | 0.49823388 |
| Etnia: Asiática rel. a blanca | 2.757 | 1.277 | 5.952 | 0.00981981 |
| Etnia: Latinoamericana rel. a blanca | 1.376 | 0.897 | 2.110 | 0.14378942 |
| Etnia: Negra rel. a blanca | 1.245 | 0.426 | 3.633 | 0.68875433 |
| HTA | 4.819 | 2.112 | 10.996 | 0.00018645 |
| DM | 0.237 | 0.032 | 1.775 | 0.16117453 |
| Gestación múltiple | 4.289 | 2.100 | 8.760 | 6.461e-05 |
9.2.3.2 COVID asintomática, leve o grave contra No COVID
mod.COVID=glm(Preeclampsia~Sint+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 1.151 | 0.749 | 1.768 | 0.52237103 |
| COVID leve | 1.254 | 0.803 | 1.957 | 0.31965703 |
| COVID grave | 1.139 | 0.617 | 2.104 | 0.67735406 |
| Edad: 31-40 años rel. a 18-30 | 1.113 | 0.767 | 1.614 | 0.5733751 |
| Edad: >40 años rel. a 18-30 | 1.388 | 0.709 | 2.718 | 0.33849689 |
| Etnia: Árabe rel. a blanca | 1.238 | 0.675 | 2.271 | 0.49093093 |
| Etnia: Asiática rel. a blanca | 2.781 | 1.286 | 6.015 | 0.00934569 |
| Etnia: Latinoamericana rel. a blanca | 1.372 | 0.886 | 2.123 | 0.15609094 |
| Etnia: Negra rel. a blanca | 1.249 | 0.428 | 3.645 | 0.68460331 |
| HTA | 4.842 | 2.120 | 11.055 | 0.00018084 |
| DM | 0.239 | 0.032 | 1.787 | 0.16309796 |
| Gestación múltiple | 4.249 | 2.075 | 8.701 | 7.613e-05 |
9.2.3.3 COVID ante o periparto contra No COVID
mod.COVID=glm(Preeclampsia~PreP+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 1.165 | 0.775 | 1.751 | 0.46146761 |
| COVID periparto | 1.215 | 0.792 | 1.863 | 0.37161943 |
| Edad: 31-40 años rel. a 18-30 | 1.110 | 0.766 | 1.608 | 0.5819414 |
| Edad: >40 años rel. a 18-30 | 1.381 | 0.706 | 2.700 | 0.34539267 |
| Etnia: Árabe rel. a blanca | 1.233 | 0.672 | 2.262 | 0.49868793 |
| Etnia: Asiática rel. a blanca | 2.758 | 1.277 | 5.956 | 0.00978822 |
| Etnia: Latinoamericana rel. a blanca | 1.382 | 0.899 | 2.126 | 0.14064774 |
| Etnia: Negra rel. a blanca | 1.244 | 0.426 | 3.633 | 0.68966999 |
| HTA | 4.843 | 2.119 | 11.068 | 0.0001838 |
| DM | 0.238 | 0.032 | 1.782 | 0.1622719 |
| Gestación múltiple | 4.302 | 2.105 | 8.794 | 6.341e-05 |
9.2.3.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Preeclampsia~SintPre+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 1.416 | 0.766 | 2.617 | 0.26699058 |
| COVID anteparto leve | 1.159 | 0.706 | 1.901 | 0.55957854 |
| COVID anteparto grave | 0.718 | 0.323 | 1.595 | 0.41628172 |
| Edad: 31-40 años rel. a 18-30 | 1.043 | 0.680 | 1.599 | 0.84780342 |
| Edad: >40 años rel. a 18-30 | 1.481 | 0.680 | 3.223 | 0.32263885 |
| Etnia: Árabe rel. a blanca | 1.709 | 0.893 | 3.272 | 0.10575646 |
| Etnia: Asiática rel. a blanca | 3.892 | 1.676 | 9.037 | 0.00156591 |
| Etnia: Latinoamericana rel. a blanca | 1.641 | 0.981 | 2.745 | 0.05937672 |
| Etnia: Negra rel. a blanca | 1.548 | 0.454 | 5.277 | 0.4849468 |
| HTA | 5.351 | 2.196 | 13.039 | 0.00022347 |
| DM | 0.271 | 0.035 | 2.093 | 0.21071669 |
| Gestación múltiple | 4.917 | 2.273 | 10.638 | 5.231e-05 |
9.2.3.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Preeclampsia~SintPeri+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 1.020 | 0.614 | 1.694 | 0.9379907 |
| COVID periparto leve | 1.456 | 0.674 | 3.146 | 0.3386743 |
| COVID periparto grave | 2.366 | 1.008 | 5.551 | 0.04782967 |
| Edad: 31-40 años rel. a 18-30 | 1.267 | 0.798 | 2.012 | 0.31536521 |
| Edad: >40 años rel. a 18-30 | 1.359 | 0.571 | 3.237 | 0.48796509 |
| Etnia: Árabe rel. a blanca | 1.213 | 0.588 | 2.504 | 0.60078689 |
| Etnia: Asiática rel. a blanca | 2.051 | 0.713 | 5.903 | 0.18298878 |
| Etnia: Latinoamericana rel. a blanca | 1.396 | 0.786 | 2.480 | 0.25443109 |
| Etnia: Negra rel. a blanca | 0.473 | 0.061 | 3.689 | 0.47493594 |
| HTA | 5.165 | 1.643 | 16.231 | 0.0049491 |
| DM | 0.000 | 0.000 | Inf | 0.98153619 |
| Gestación múltiple | 3.086 | 1.157 | 8.232 | 0.02442185 |
9.2.3.6 Ola
mod.COVID=glm(Preeclampsia~SegundOla+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 0.793 | 0.553 | 1.138 | 0.20821856 |
| Edad: 31-40 años rel. a 18-30 | 1.097 | 0.756 | 1.591 | 0.6256845 |
| Edad: >40 años rel. a 18-30 | 1.371 | 0.700 | 2.684 | 0.35750181 |
| Etnia: Árabe rel. a blanca | 1.271 | 0.693 | 2.334 | 0.43823392 |
| Etnia: Asiática rel. a blanca | 2.851 | 1.321 | 6.154 | 0.00760279 |
| Etnia: Latinoamericana rel. a blanca | 1.494 | 0.987 | 2.262 | 0.05766935 |
| Etnia: Negra rel. a blanca | 1.358 | 0.468 | 3.940 | 0.57368381 |
| HTA | 4.804 | 2.106 | 10.958 | 0.00019084 |
| DM | 0.244 | 0.033 | 1.832 | 0.17037318 |
| Gestación múltiple | 4.250 | 2.081 | 8.678 | 7.108e-05 |
9.2.3.7 Infección y ola
mod.COVID=glm(Preeclampsia~SegundOla+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM
,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 0.988 | 0.632 | 1.544 | 0.95865419 |
| Edad: 31-40 años rel. a 18-30 | 1.059 | 0.649 | 1.728 | 0.81777977 |
| Edad: >40 años rel. a 18-30 | 1.359 | 0.589 | 3.134 | 0.4721318 |
| Etnia: Árabe rel. a blanca | 0.755 | 0.289 | 1.970 | 0.5655468 |
| Etnia: Asiática rel. a blanca | 2.630 | 0.979 | 7.063 | 0.0551004 |
| Etnia: Latinoamericana rel. a blanca | 1.183 | 0.709 | 1.972 | 0.51998395 |
| Etnia: Negra rel. a blanca | 1.664 | 0.538 | 5.147 | 0.37705053 |
| HTA | 4.282 | 1.477 | 12.419 | 0.00742034 |
| DM | 0.414 | 0.054 | 3.185 | 0.39696183 |
| Gestación múltiple | 5.039 | 1.963 | 12.937 | 0.00077385 |
9.2.4 Preeclampsia grave en preeclámpsicas
9.2.4.1 COVID Sí o No
mod.COVID=glm(Preeclampsia.grave~COVID+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres.Pree,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 2.257 | 0.958 | 5.318 | 0.06252001 |
| Edad: 31-40 años rel. a 18-30 | 2.037 | 0.755 | 5.494 | 0.15977415 |
| Edad: >40 años rel. a 18-30 | 2.931 | 0.632 | 13.586 | 0.16933506 |
| Etnia: Árabe rel. a blanca | 1.411 | 0.320 | 6.217 | 0.64879127 |
| Etnia: Asiática rel. a blanca | 1.645 | 0.261 | 10.352 | 0.59613672 |
| Etnia: Latinoamericana rel. a blanca | 1.978 | 0.711 | 5.504 | 0.19142565 |
| Etnia: Negra rel. a blanca | 7.878 | 0.591 | 104.962 | 0.11822356 |
| HTA | 0.833 | 0.115 | 6.065 | 0.8571504 |
| DM | 5807312.261 | 0.000 | Inf | 0.99146177 |
| Gestación múltiple | 2.375 | 0.541 | 10.427 | 0.25192632 |
9.2.4.2 COVID asintomática, leve o grave contra No COVID
mod.COVID=glm(Preeclampsia.grave~Sint+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres.Pree,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 1.370 | 0.458 | 4.093 | 0.57337498 |
| COVID leve | 2.641 | 0.941 | 7.408 | 0.06498177 |
| COVID grave | 4.737 | 1.274 | 17.611 | 0.02023868 |
| Edad: 31-40 años rel. a 18-30 | 2.046 | 0.747 | 5.609 | 0.16399116 |
| Edad: >40 años rel. a 18-30 | 2.252 | 0.465 | 10.913 | 0.31342001 |
| Etnia: Árabe rel. a blanca | 1.615 | 0.364 | 7.171 | 0.52835036 |
| Etnia: Asiática rel. a blanca | 1.794 | 0.283 | 11.378 | 0.53506311 |
| Etnia: Latinoamericana rel. a blanca | 1.781 | 0.621 | 5.104 | 0.28281905 |
| Etnia: Negra rel. a blanca | 11.085 | 0.750 | 163.802 | 0.0799775 |
| HTA | 0.825 | 0.109 | 6.261 | 0.85279728 |
| DM | 3947674.903 | 0.000 | Inf | 0.99167337 |
| Gestación múltiple | 2.103 | 0.457 | 9.686 | 0.34018386 |
9.2.4.3 COVID ante o periparto contra No COVID
mod.COVID=glm(Preeclampsia.grave~PreP+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres.Pree,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 1.505 | 0.550 | 4.119 | 0.42562858 |
| COVID periparto | 3.455 | 1.280 | 9.326 | 0.0144125 |
| Edad: 31-40 años rel. a 18-30 | 2.015 | 0.737 | 5.507 | 0.17222161 |
| Edad: >40 años rel. a 18-30 | 2.992 | 0.632 | 14.170 | 0.16711865 |
| Etnia: Árabe rel. a blanca | 1.709 | 0.377 | 7.755 | 0.48721718 |
| Etnia: Asiática rel. a blanca | 2.052 | 0.303 | 13.924 | 0.46172997 |
| Etnia: Latinoamericana rel. a blanca | 2.362 | 0.816 | 6.837 | 0.11312208 |
| Etnia: Negra rel. a blanca | 10.657 | 0.793 | 143.290 | 0.07431942 |
| HTA | 0.872 | 0.119 | 6.395 | 0.8929631 |
| DM | 7481002.609 | 0.000 | Inf | 0.99132294 |
| Gestación múltiple | 3.104 | 0.679 | 14.191 | 0.14416697 |
9.2.4.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Preeclampsia.grave~SintPre+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres.Pree,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 0.417 | 0.045 | 3.872 | 0.44149135 |
| COVID anteparto leve | 1.982 | 0.617 | 6.371 | 0.25075298 |
| COVID anteparto grave | 1.543 | 0.215 | 11.068 | 0.6662279 |
| Edad: 31-40 años rel. a 18-30 | 2.295 | 0.658 | 8.002 | 0.19252163 |
| Edad: >40 años rel. a 18-30 | 2.137 | 0.260 | 17.582 | 0.47992453 |
| Etnia: Árabe rel. a blanca | 1.676 | 0.269 | 10.453 | 0.58048874 |
| Etnia: Asiática rel. a blanca | 1.921 | 0.165 | 22.349 | 0.60220862 |
| Etnia: Latinoamericana rel. a blanca | 3.086 | 0.814 | 11.699 | 0.0974574 |
| Etnia: Negra rel. a blanca | 20.391 | 0.784 | 530.529 | 0.0697655 |
| HTA | 0.817 | 0.097 | 6.858 | 0.85259005 |
| DM | 9425752.878 | 0.000 | Inf | 0.99119627 |
| Gestación múltiple | 3.752 | 0.690 | 20.408 | 0.12591965 |
9.2.4.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Preeclampsia.grave~SintPeri+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres.Pree,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 2.617 | 0.737 | 9.294 | 0.13667584 |
| COVID periparto leve | 5.449 | 0.964 | 30.791 | 0.05498003 |
| COVID periparto grave | 18.158 | 2.614 | 126.134 | 0.00337102 |
| Edad: 31-40 años rel. a 18-30 | 3.876 | 0.852 | 17.642 | 0.07975019 |
| Edad: >40 años rel. a 18-30 | 1.815 | 0.164 | 20.059 | 0.62683503 |
| Etnia: Árabe rel. a blanca | 2.988 | 0.446 | 20.036 | 0.25949714 |
| Etnia: Asiática rel. a blanca | 3.785 | 0.275 | 52.127 | 0.31984582 |
| Etnia: Latinoamericana rel. a blanca | 3.319 | 0.672 | 16.400 | 0.1411306 |
| Etnia: Negra rel. a blanca | 1088827.774 | 0.000 | Inf | 0.99237946 |
| HTA | 14.027 | 0.834 | 236.038 | 0.06671561 |
| DM | ||||
| Gestación múltiple | 2.698 | 0.281 | 25.874 | 0.38956579 |
9.2.4.6 Olas
mod.COVID=glm(Preeclampsia.grave~SegundOla+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres.Pree,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"1a ola rel. a 2a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 1a ola rel. a 2a ola | 0.743 | 0.304 | 1.811 | 0.51290191 |
| Edad: 31-40 años rel. a 18-30 | 2.069 | 0.772 | 5.545 | 0.14844182 |
| Edad: >40 años rel. a 18-30 | 3.232 | 0.710 | 14.708 | 0.12911391 |
| Etnia: Árabe rel. a blanca | 1.230 | 0.286 | 5.298 | 0.78081105 |
| Etnia: Asiática rel. a blanca | 1.660 | 0.269 | 10.248 | 0.58504721 |
| Etnia: Latinoamericana rel. a blanca | 2.577 | 0.905 | 7.336 | 0.07622479 |
| Etnia: Negra rel. a blanca | 14.293 | 1.076 | 189.831 | 0.04384007 |
| HTA | 0.705 | 0.104 | 4.792 | 0.72074709 |
| DM | 6457294.035 | 0.000 | Inf | 0.99140361 |
| Gestación múltiple | 2.823 | 0.655 | 12.174 | 0.16400182 |
9.2.4.7 Infección y ola
mod.COVID=glm(Preeclampsia.grave~SegundOla+Edad.cut+Etnias+Hipertension.Pre+Diabetes+GM,data=DFL.madres.Pree[DFL.madres.Pree$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"HTA",
"DM",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 5.130000e-01 | 0.181 | 1.459 | 0.21086971 |
| Edad: 31-40 años rel. a 18-30 | 1.149000e+00 | 0.350 | 3.775 | 0.81928186 |
| Edad: >40 años rel. a 18-30 | 3.904000e+00 | 0.553 | 27.537 | 0.17184282 |
| Etnia: Árabe rel. a blanca | 1.331000e+00 | 0.170 | 10.385 | 0.78527868 |
| Etnia: Asiática rel. a blanca | 1.094000e+00 | 0.127 | 9.430 | 0.93472474 |
| Etnia: Latinoamericana rel. a blanca | 1.349000e+00 | 0.396 | 4.593 | 0.63221603 |
| Etnia: Negra rel. a blanca | 1.402288e+08 | 0.000 | Inf | 0.99240783 |
| HTA | 0.000000e+00 | 0.000 | Inf | 0.99280157 |
| DM | 9.327589e+14 | 0.000 | Inf | 0.99377801 |
| Gestación múltiple | 1.867000e+00 | 0.256 | 13.597 | 0.53763828 |
9.2.5 Efectos trombóticos
9.2.5.1 COVID Sí o No
mod.COVID=glm(Event.Tromb~COVID+Edad.cut+Fumadora+Hipertension.Pre+Diabetes+Obesidad+FMI
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"HTA",
"DM",
"Obesidad",
"Feto muestro anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 4.764 | 1.054 | 21.521 | 0.04246431 |
| Edad: 31-40 años rel. a 18-30 | 0.912 | 0.299 | 2.778 | 0.87112711 |
| Edad: >40 años rel. a 18-30 | 0.978 | 0.114 | 8.367 | 0.98404758 |
| Tabaquismo | 0.000 | 0.000 | Inf | 0.99157438 |
| HTA | 0.000 | 0.000 | Inf | 0.99700758 |
| DM | 0.000 | 0.000 | Inf | 0.99633732 |
| Obesidad | 2.020 | 0.625 | 6.526 | 0.24000365 |
| Feto muestro anteparto | 9.322 | 1.073 | 80.966 | 0.04295795 |
9.2.5.2 COVID asintomática, leve o grave contra No COVID
mod.COVID=glm(Event.Tromb~Sint+Edad.cut+Fumadora+Hipertension.Pre+Diabetes+Obesidad+FMI,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"HTA",
"DM",
"Obesidad",
"Feto muestro anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 0.841 | 0.074 | 9.556 | 0.88866693 |
| COVID leve | 0.000 | 0.000 | Inf | 0.99300722 |
| COVID grave | 26.926 | 5.877 | 123.356 | 2.227e-05 |
| Edad: 31-40 años rel. a 18-30 | 0.752 | 0.243 | 2.326 | 0.62089139 |
| Edad: >40 años rel. a 18-30 | 1.038 | 0.118 | 9.113 | 0.97344822 |
| Tabaquismo | 0.000 | 0.000 | Inf | 0.99427777 |
| HTA | 0.000 | 0.000 | Inf | 0.99793304 |
| DM | 0.000 | 0.000 | Inf | 0.9973614 |
| Obesidad | 1.717 | 0.514 | 5.741 | 0.38001322 |
| Feto muestro anteparto | 16.703 | 1.370 | 203.701 | 0.02735391 |
9.2.5.3 COVID ante o periparto contra No COVID
mod.COVID=glm(Event.Tromb~PreP+Edad.cut+Fumadora+Hipertension.Pre+Diabetes+Obesidad+FMI,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"HTA",
"DM",
"Obesidad",
"Feto muestro anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 2.895 | 0.527 | 15.895 | 0.22118903 |
| COVID periparto | 7.263 | 1.515 | 34.825 | 0.01317335 |
| Edad: 31-40 años rel. a 18-30 | 0.889 | 0.291 | 2.710 | 0.83584295 |
| Edad: >40 años rel. a 18-30 | 0.928 | 0.108 | 7.937 | 0.94555833 |
| Tabaquismo | 0.000 | 0.000 | Inf | 0.99150243 |
| HTA | 0.000 | 0.000 | Inf | 0.99705767 |
| DM | 0.000 | 0.000 | Inf | 0.99632533 |
| Obesidad | 2.030 | 0.628 | 6.569 | 0.23708293 |
| Feto muestro anteparto | 7.288 | 0.820 | 64.798 | 0.07481035 |
9.2.5.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Event.Tromb~SintPre+Edad.cut+Fumadora+Hipertension.Pre+Diabetes+Obesidad+FMI,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"HTA",
"DM",
"Obesidad",
"Feto muestro anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 0.000 | 0.000 | Inf | 0.99736341 |
| COVID anteparto leve | 0.000 | 0.000 | Inf | 0.99600867 |
| COVID anteparto grave | 12.352 | 2.211 | 68.991 | 0.00418039 |
| Edad: 31-40 años rel. a 18-30 | 1.416 | 0.252 | 7.971 | 0.69306854 |
| Edad: >40 años rel. a 18-30 | 0.000 | 0.000 | Inf | 0.99765289 |
| Tabaquismo | 0.000 | 0.000 | Inf | 0.99679628 |
| HTA | 0.000 | 0.000 | Inf | 0.99876582 |
| DM | 0.000 | 0.000 | Inf | 0.99856107 |
| Obesidad | 4.280 | 0.832 | 22.015 | 0.08183238 |
| Feto muestro anteparto | 0.000 | 0.000 | Inf | 0.99943468 |
9.2.5.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Event.Tromb~SintPeri+Edad.cut+Fumadora+Hipertension.Pre+Diabetes+Obesidad+FMI,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"HTA",
"DM",
"Obesidad",
"Feto muestro anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 0.954 | 0.074 | 12.345 | 0.97148355 |
| COVID periparto leve | 0.000 | 0.000 | Inf | 0.99648866 |
| COVID periparto grave | 82.433 | 16.322 | 416.329 | 9e-08 |
| Edad: 31-40 años rel. a 18-30 | 0.348 | 0.083 | 1.465 | 0.15012824 |
| Edad: >40 años rel. a 18-30 | 0.922 | 0.091 | 9.290 | 0.94502211 |
| Tabaquismo | 0.000 | 0.000 | Inf | 0.99497502 |
| HTA | 0.000 | 0.000 | Inf | 0.99873125 |
| DM | 0.000 | 0.000 | Inf | 0.99788765 |
| Obesidad | 1.267 | 0.242 | 6.636 | 0.77962175 |
| Feto muestro anteparto | 24.549 | 1.354 | 445.230 | 0.03040527 |
9.2.5.6 Ola
mod.COVID=glm(Event.Tromb~SegundOla+Edad.cut+Fumadora+Hipertension.Pre+Diabetes+Obesidad+FMI,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"HTA",
"DM",
"Obesidad",
"Feto muestro anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 1.601 | 0.555 | 4.622 | 0.38392295 |
| Edad: 31-40 años rel. a 18-30 | 0.886 | 0.290 | 2.703 | 0.83158021 |
| Edad: >40 años rel. a 18-30 | 1.065 | 0.124 | 9.115 | 0.95449857 |
| Tabaquismo | 0.000 | 0.000 | Inf | 0.99170117 |
| HTA | 0.000 | 0.000 | Inf | 0.99703056 |
| DM | 0.000 | 0.000 | Inf | 0.99639893 |
| Obesidad | 2.113 | 0.656 | 6.812 | 0.21026286 |
| Feto muestro anteparto | 12.404 | 1.434 | 107.262 | 0.02215009 |
9.2.5.7 Infección y Ola
mod.COVID=glm(Event.Tromb~SegundOla+Edad.cut+Fumadora+Hipertension.Pre+Diabetes+Obesidad+FMI,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"HTA",
"DM",
"Obesidad",
"Feto muestro anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 0.733 | 0.229 | 2.346 | 0.60043549 |
| Edad: 31-40 años rel. a 18-30 | 0.952 | 0.280 | 3.232 | 0.93698988 |
| Edad: >40 años rel. a 18-30 | 1.076 | 0.121 | 9.564 | 0.94728678 |
| Tabaquismo | 0.000 | 0.000 | Inf | 0.99102239 |
| HTA | 0.000 | 0.000 | Inf | 0.99639296 |
| DM | 0.000 | 0.000 | Inf | 0.99568957 |
| Obesidad | 1.670 | 0.444 | 6.271 | 0.44781502 |
| Feto muestro anteparto | 9.715 | 1.103 | 85.575 | 0.04052721 |
9.2.6 Rotura prematura de membranas
9.2.6.1 COVID Sí o No
mod.COVID=glm(Rotura~COVID+Edad.cut+Etnias+Fumadora+Obesidad+Hipertension.Pre+Diabetes+ECP.Tot+ECC+Nulipara+GM
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Árabe rel. a blanca",
"Asiática rel. a blanca",
"Latinoamericana rel. a blanca",
"Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"HTA",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 1.200 | 0.957 | 1.505 | 0.11470395 |
| Edad: 31-40 años rel. a 18-30 | 1.077 | 0.849 | 1.367 | 0.53938762 |
| Edad: >40 años rel. a 18-30 | 0.985 | 0.599 | 1.621 | 0.95320968 |
| Árabe rel. a blanca | 0.851 | 0.545 | 1.328 | 0.47651621 |
| Asiática rel. a blanca | 1.377 | 0.743 | 2.551 | 0.30907319 |
| Latinoamericana rel. a blanca | 1.328 | 1.003 | 1.758 | 0.04742579 |
| Negra rel. a blanca | 1.571 | 0.798 | 3.092 | 0.19139838 |
| Tabaquismo | 1.254 | 0.896 | 1.754 | 0.18707637 |
| Obesidad | 1.027 | 0.771 | 1.368 | 0.85695838 |
| HTA | 0.977 | 0.349 | 2.736 | 0.96413906 |
| DM | 0.763 | 0.324 | 1.798 | 0.53553327 |
| EPC | 1.023 | 0.596 | 1.757 | 0.93446032 |
| ECC | 1.024 | 0.370 | 2.835 | 0.96335965 |
| Nulípara | 1.553 | 1.243 | 1.942 | 0.00011036 |
| Gestación múltiple | 0.804 | 0.341 | 1.897 | 0.61844564 |
9.2.6.2 COVID asintomática, leve o grave contra No COVID
mod.COVID=glm(Rotura~Sint+Edad.cut+Etnias+Fumadora+Obesidad+Hipertension.Pre+Diabetes+ECP.Tot+ECC+Nulipara+GM
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Árabe rel. a blanca",
"Asiática rel. a blanca",
"Latinoamericana rel. a blanca",
"Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"HTA",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 1.201 | 0.913 | 1.579 | 0.19105568 |
| COVID leve | 1.263 | 0.949 | 1.683 | 0.10990058 |
| COVID grave | 1.033 | 0.680 | 1.569 | 0.88050398 |
| Edad: 31-40 años rel. a 18-30 | 1.085 | 0.855 | 1.377 | 0.50348446 |
| Edad: >40 años rel. a 18-30 | 0.994 | 0.604 | 1.637 | 0.98200715 |
| Árabe rel. a blanca | 0.850 | 0.544 | 1.327 | 0.47442861 |
| Asiática rel. a blanca | 1.396 | 0.753 | 2.589 | 0.28909547 |
| Latinoamericana rel. a blanca | 1.344 | 1.010 | 1.787 | 0.0424892 |
| Negra rel. a blanca | 1.561 | 0.792 | 3.074 | 0.19806208 |
| Tabaquismo | 1.251 | 0.894 | 1.751 | 0.19055114 |
| Obesidad | 1.031 | 0.774 | 1.374 | 0.83457733 |
| HTA | 0.982 | 0.350 | 2.756 | 0.97239396 |
| DM | 0.763 | 0.324 | 1.800 | 0.53712452 |
| EPC | 1.019 | 0.593 | 1.752 | 0.94454731 |
| ECC | 1.033 | 0.373 | 2.865 | 0.94984087 |
| Nulípara | 1.548 | 1.238 | 1.935 | 0.0001273 |
| Gestación múltiple | 0.800 | 0.339 | 1.890 | 0.61119519 |
9.2.6.3 COVID ante o periparto contra No COVID
mod.COVID=glm(Rotura~PreP+Edad.cut+Etnias+Fumadora+Obesidad+Hipertension.Pre+Diabetes+ECP.Tot+ECC+Nulipara+GM
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Árabe rel. a blanca",
"Asiática rel. a blanca",
"Latinoamericana rel. a blanca",
"Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"HTA",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 1.096 | 0.839 | 1.432 | 0.50038422 |
| COVID periparto | 1.325 | 1.012 | 1.735 | 0.04066368 |
| Edad: 31-40 años rel. a 18-30 | 1.075 | 0.847 | 1.364 | 0.55038755 |
| Edad: >40 años rel. a 18-30 | 0.978 | 0.594 | 1.611 | 0.93146531 |
| Árabe rel. a blanca | 0.846 | 0.542 | 1.321 | 0.46145213 |
| Asiática rel. a blanca | 1.380 | 0.745 | 2.557 | 0.30641063 |
| Latinoamericana rel. a blanca | 1.355 | 1.022 | 1.797 | 0.03484864 |
| Negra rel. a blanca | 1.574 | 0.799 | 3.100 | 0.18983767 |
| Tabaquismo | 1.251 | 0.895 | 1.751 | 0.19033227 |
| Obesidad | 1.029 | 0.772 | 1.372 | 0.8434369 |
| HTA | 0.992 | 0.353 | 2.788 | 0.98747948 |
| DM | 0.777 | 0.330 | 1.833 | 0.56452405 |
| EPC | 1.038 | 0.604 | 1.784 | 0.89327824 |
| ECC | 1.028 | 0.371 | 2.852 | 0.95757457 |
| Nulípara | 1.540 | 1.231 | 1.926 | 0.00015604 |
| Gestación múltiple | 0.814 | 0.345 | 1.922 | 0.63878735 |
9.2.6.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Rotura~SintPre+Edad.cut+Etnias+Fumadora+Obesidad+Hipertension.Pre+Diabetes+ECP.Tot+ECC+Nulipara+GM
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Árabe rel. a blanca",
"Asiática rel. a blanca",
"Latinoamericana rel. a blanca",
"Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"HTA",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 0.863 | 0.535 | 1.391 | 0.54511139 |
| COVID anteparto leve | 1.210 | 0.878 | 1.667 | 0.24422042 |
| COVID anteparto grave | 1.120 | 0.700 | 1.790 | 0.6364248 |
| Edad: 31-40 años rel. a 18-30 | 0.953 | 0.725 | 1.253 | 0.73066313 |
| Edad: >40 años rel. a 18-30 | 0.840 | 0.452 | 1.560 | 0.58101301 |
| Árabe rel. a blanca | 0.989 | 0.599 | 1.635 | 0.96664549 |
| Asiática rel. a blanca | 1.356 | 0.648 | 2.836 | 0.41906758 |
| Latinoamericana rel. a blanca | 1.295 | 0.922 | 1.817 | 0.13535787 |
| Negra rel. a blanca | 1.563 | 0.677 | 3.609 | 0.29581388 |
| Tabaquismo | 1.113 | 0.746 | 1.661 | 0.59861391 |
| Obesidad | 1.046 | 0.749 | 1.461 | 0.79261957 |
| HTA | 0.802 | 0.213 | 3.025 | 0.74480558 |
| DM | 0.806 | 0.314 | 2.068 | 0.65402356 |
| EPC | 1.173 | 0.654 | 2.106 | 0.59268876 |
| ECC | 1.036 | 0.323 | 3.323 | 0.9531577 |
| Nulípara | 1.658 | 1.277 | 2.153 | 0.00014528 |
| Gestación múltiple | 0.678 | 0.239 | 1.920 | 0.46431929 |
9.2.6.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Rotura~SintPeri+Edad.cut+Etnias+Fumadora+Obesidad+Hipertension.Pre+Diabetes+ECP.Tot+ECC+Nulipara+GM
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Árabe rel. a blanca",
"Asiática rel. a blanca",
"Latinoamericana rel. a blanca",
"Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"HTA",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 1.346 | 0.995 | 1.821 | 0.05424502 |
| COVID periparto leve | 1.471 | 0.902 | 2.400 | 0.12221853 |
| COVID periparto grave | 0.832 | 0.367 | 1.888 | 0.6604425 |
| Edad: 31-40 años rel. a 18-30 | 1.111 | 0.833 | 1.483 | 0.47347972 |
| Edad: >40 años rel. a 18-30 | 0.962 | 0.521 | 1.774 | 0.90055061 |
| Árabe rel. a blanca | 0.818 | 0.483 | 1.385 | 0.45542805 |
| Asiática rel. a blanca | 1.373 | 0.654 | 2.883 | 0.40250877 |
| Latinoamericana rel. a blanca | 1.390 | 0.955 | 2.025 | 0.08582577 |
| Negra rel. a blanca | 2.159 | 0.984 | 4.738 | 0.05494698 |
| Tabaquismo | 1.312 | 0.890 | 1.932 | 0.17025217 |
| Obesidad | 1.127 | 0.796 | 1.596 | 0.49916544 |
| HTA | 0.848 | 0.191 | 3.766 | 0.82868854 |
| DM | 0.658 | 0.198 | 2.191 | 0.49552564 |
| EPC | 0.383 | 0.138 | 1.066 | 0.06610958 |
| ECC | 1.357 | 0.382 | 4.818 | 0.63696613 |
| Nulípara | 1.609 | 1.232 | 2.102 | 0.00047927 |
| Gestación múltiple | 0.859 | 0.298 | 2.477 | 0.77861139 |
9.2.6.6 Ola
mod.COVID=glm(Rotura~SegundOla+Edad.cut+Etnias+Fumadora+Obesidad+Hipertension.Pre+Diabetes+ECP.Tot+ECC+Nulipara+GM
,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Árabe rel. a blanca",
"Asiática rel. a blanca",
"Latinoamericana rel. a blanca",
"Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"HTA",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 1.088 | 0.871 | 1.359 | 0.45804279 |
| Edad: 31-40 años rel. a 18-30 | 1.085 | 0.855 | 1.376 | 0.50300112 |
| Edad: >40 años rel. a 18-30 | 1.010 | 0.614 | 1.662 | 0.96919914 |
| Árabe rel. a blanca | 0.856 | 0.548 | 1.336 | 0.49382388 |
| Asiática rel. a blanca | 1.403 | 0.758 | 2.597 | 0.28133696 |
| Latinoamericana rel. a blanca | 1.395 | 1.063 | 1.831 | 0.01640372 |
| Negra rel. a blanca | 1.617 | 0.823 | 3.178 | 0.16297 |
| Tabaquismo | 1.252 | 0.895 | 1.751 | 0.18967594 |
| Obesidad | 1.033 | 0.775 | 1.377 | 0.82334985 |
| HTA | 1.002 | 0.355 | 2.826 | 0.99723767 |
| DM | 0.756 | 0.321 | 1.782 | 0.52225549 |
| EPC | 1.032 | 0.601 | 1.773 | 0.90837228 |
| ECC | 0.994 | 0.356 | 2.771 | 0.99052623 |
| Nulípara | 1.544 | 1.235 | 1.930 | 0.0001369 |
| Gestación múltiple | 0.810 | 0.343 | 1.911 | 0.63026185 |
9.2.6.7 Infección y Ola
mod.COVID=glm(Rotura~SegundOla+Edad.cut+Etnias+Fumadora+Obesidad+Hipertension.Pre+Diabetes+ECP.Tot+ECC+Nulipara+GM
,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Árabe rel. a blanca",
"Asiática rel. a blanca",
"Latinoamericana rel. a blanca",
"Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"HTA",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 1.027 | 0.769 | 1.372 | 0.8573219 |
| Edad: 31-40 años rel. a 18-30 | 1.228 | 0.889 | 1.695 | 0.21229396 |
| Edad: >40 años rel. a 18-30 | 1.204 | 0.652 | 2.221 | 0.55350785 |
| Árabe rel. a blanca | 0.698 | 0.368 | 1.324 | 0.27156999 |
| Asiática rel. a blanca | 1.453 | 0.654 | 3.225 | 0.35873347 |
| Latinoamericana rel. a blanca | 1.334 | 0.954 | 1.865 | 0.09250268 |
| Negra rel. a blanca | 1.144 | 0.464 | 2.824 | 0.77007793 |
| Tabaquismo | 1.379 | 0.867 | 2.191 | 0.17429492 |
| Obesidad | 0.907 | 0.619 | 1.327 | 0.61443137 |
| HTA | 1.243 | 0.409 | 3.779 | 0.7015369 |
| DM | 0.846 | 0.290 | 2.470 | 0.76029153 |
| EPC | 1.551 | 0.845 | 2.849 | 0.1567263 |
| ECC | 0.900 | 0.200 | 4.050 | 0.89043446 |
| Nulípara | 1.354 | 1.001 | 1.832 | 0.04939924 |
| Gestación múltiple | 0.951 | 0.327 | 2.768 | 0.92689309 |
9.2.7 Prematuridad
9.2.7.1 COVID Sí o No
mod.COVID=glm(Prematuro~COVID+Edad.cut+Fumadora+Preeclampsia.grave+RetrasoCF+Rotura+FMI+Diabetes ,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"PE con CG",
"RCIU",
"RPM",
"Feto muerto anteparto",
"DM"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 1.563 | 1.177 | 2.075 | 0.00202258 |
| Edad: 31-40 años rel. a 18-30 | 0.930 | 0.697 | 1.242 | 0.62469371 |
| Edad: >40 años rel. a 18-30 | 1.380 | 0.818 | 2.328 | 0.22813073 |
| Tabaquismo | 1.576 | 1.072 | 2.316 | 0.02059906 |
| PE con CG | 11.474 | 5.979 | 22.017 | <1e-08 |
| RCIU | 3.065 | 1.801 | 5.215 | 3.614e-05 |
| RPM | 2.289 | 1.642 | 3.190 | 1.03e-06 |
| Feto muerto anteparto | 5.182 | 1.927 | 13.936 | 0.00111605 |
| DM | 1.213 | 0.497 | 2.956 | 0.67161827 |
9.2.7.2 COVID No, asintomática, leve o grave
mod.COVID=glm(Prematuro~Sint+Edad.cut+Fumadora+Preeclampsia.grave+RetrasoCF+Rotura+FMI+Diabetes,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"PE con CG",
"RCIU",
"RPM",
"Feto muerto anteparto",
"DM"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 1.091 | 0.750 | 1.586 | 0.64926074 |
| COVID leve | 1.522 | 1.064 | 2.175 | 0.021326 |
| COVID grave | 3.195 | 2.129 | 4.795 | 2e-08 |
| Edad: 31-40 años rel. a 18-30 | 0.924 | 0.691 | 1.237 | 0.59623762 |
| Edad: >40 años rel. a 18-30 | 1.331 | 0.784 | 2.261 | 0.29001803 |
| Tabaquismo | 1.612 | 1.094 | 2.374 | 0.01574932 |
| PE con CG | 10.958 | 5.638 | 21.297 | <1e-08 |
| RCIU | 3.304 | 1.928 | 5.662 | 1.371e-05 |
| RPM | 2.342 | 1.676 | 3.271 | 6.1e-07 |
| Feto muerto anteparto | 5.385 | 1.975 | 14.681 | 0.00099992 |
| DM | 1.122 | 0.451 | 2.791 | 0.80382867 |
9.2.7.3 COVID ante o periparto contra No
mod.COVID=glm(Prematuro~PreP+Edad.cut+Fumadora+Preeclampsia.grave+RetrasoCF+Rotura+FMI+Diabetes,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"PE con CG",
"RCIU",
"RPM",
"Feto muerto anteparto",
"DM"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 1.469 | 1.061 | 2.035 | 0.02064199 |
| COVID periparto | 1.686 | 1.201 | 2.368 | 0.00256882 |
| Edad: 31-40 años rel. a 18-30 | 0.927 | 0.695 | 1.238 | 0.60940214 |
| Edad: >40 años rel. a 18-30 | 1.375 | 0.815 | 2.320 | 0.23236675 |
| Tabaquismo | 1.569 | 1.067 | 2.307 | 0.02191099 |
| PE con CG | 11.415 | 5.949 | 21.904 | <1e-08 |
| RCIU | 3.096 | 1.818 | 5.275 | 3.204e-05 |
| RPM | 2.281 | 1.636 | 3.180 | 1.16e-06 |
| Feto muerto anteparto | 5.028 | 1.862 | 13.577 | 0.00143854 |
| DM | 1.231 | 0.505 | 3.003 | 0.6471731 |
9.2.7.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Prematuro~SintPre+Edad.cut+Fumadora+Preeclampsia.grave+RetrasoCF+Rotura+FMI+Diabetes,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"PE con CG",
"RCIU",
"RPM",
"Feto muerto anteparto",
"DM"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 1.524 | 0.903 | 2.573 | 0.11498964 |
| COVID anteparto leve | 0.987 | 0.637 | 1.528 | 0.95321 |
| COVID anteparto grave | 2.618 | 1.625 | 4.216 | 7.558e-05 |
| Edad: 31-40 años rel. a 18-30 | 0.872 | 0.617 | 1.231 | 0.43554148 |
| Edad: >40 años rel. a 18-30 | 1.247 | 0.656 | 2.370 | 0.50055797 |
| Tabaquismo | 1.322 | 0.820 | 2.129 | 0.25166178 |
| PE con CG | 10.399 | 4.508 | 23.985 | 4e-08 |
| RCIU | 4.475 | 2.522 | 7.944 | 3.1e-07 |
| RPM | 2.392 | 1.598 | 3.581 | 2.276e-05 |
| Feto muerto anteparto | 14.472 | 3.383 | 61.910 | 0.00031393 |
| DM | 1.315 | 0.483 | 3.581 | 0.59163005 |
9.2.7.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Prematuro~SintPeri+Edad.cut+Fumadora+Preeclampsia.grave+RetrasoCF+Rotura+FMI+Diabetes,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"PE con CG",
"RCIU",
"RPM",
"Feto muerto anteparto",
"DM"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 0.884 | 0.561 | 1.394 | 0.59673378 |
| COVID periparto leve | 3.707 | 2.254 | 6.097 | 2.5e-07 |
| COVID periparto grave | 5.036 | 2.646 | 9.585 | 8.5e-07 |
| Edad: 31-40 años rel. a 18-30 | 0.949 | 0.662 | 1.362 | 0.77810388 |
| Edad: >40 años rel. a 18-30 | 0.954 | 0.461 | 1.976 | 0.89922652 |
| Tabaquismo | 1.932 | 1.241 | 3.008 | 0.00356394 |
| PE con CG | 14.426 | 6.180 | 33.675 | <1e-08 |
| RCIU | 1.921 | 0.853 | 4.331 | 0.11518897 |
| RPM | 2.610 | 1.736 | 3.924 | 3.98e-06 |
| Feto muerto anteparto | 3.655 | 1.092 | 12.235 | 0.03546904 |
| DM | 1.216 | 0.343 | 4.313 | 0.76196208 |
9.2.7.6 Ola
mod.COVID=glm(Prematuro~SegundOla+Edad.cut+Fumadora+Preeclampsia.grave+RetrasoCF+Rotura+FMI+Diabetes,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"PE con CG",
"RCIU",
"RPM",
"Feto muerto anteparto",
"DM"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 0.818 | 0.613 | 1.093 | 0.1737752 |
| Edad: 31-40 años rel. a 18-30 | 0.903 | 0.676 | 1.207 | 0.4917105 |
| Edad: >40 años rel. a 18-30 | 1.373 | 0.814 | 2.316 | 0.23421692 |
| Tabaquismo | 1.482 | 1.009 | 2.177 | 0.04511232 |
| PE con CG | 12.232 | 6.392 | 23.408 | <1e-08 |
| RCIU | 3.130 | 1.841 | 5.320 | 2.486e-05 |
| RPM | 2.365 | 1.698 | 3.295 | 3.6e-07 |
| Feto muerto anteparto | 5.971 | 2.218 | 16.071 | 0.00040468 |
| DM | 1.276 | 0.524 | 3.107 | 0.59165025 |
9.2.7.7 Infección y ola
mod.COVID=glm(Prematuro~SegundOla+Edad.cut+Fumadora+Preeclampsia.grave+RetrasoCF+Rotura+FMI+Diabetes,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Tabaquismo",
"PE con CG",
"RCIU",
"RPM",
"Feto muerto anteparto",
"DM"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 0.724 | 0.508 | 1.030 | 0.07266469 |
| Edad: 31-40 años rel. a 18-30 | 0.923 | 0.637 | 1.337 | 0.67087441 |
| Edad: >40 años rel. a 18-30 | 1.789 | 0.981 | 3.263 | 0.05770499 |
| Tabaquismo | 1.429 | 0.844 | 2.422 | 0.18419497 |
| PE con CG | 9.783 | 4.502 | 21.257 | <1e-08 |
| RCIU | 3.011 | 1.548 | 5.853 | 0.00115749 |
| RPM | 1.960 | 1.283 | 2.995 | 0.00185721 |
| Feto muerto anteparto | 3.510 | 1.107 | 11.126 | 0.03291986 |
| DM | 1.142 | 0.373 | 3.497 | 0.81619406 |
9.2.8 Presencia de fetos muertos
9.2.8.1 COVID Sí o No
DFL.madres$FMI=as.factor(DFL.madres$FMI)
mod.COVID=glm(FMI~COVID+Diabetes+GM+Preeclampsia+AnCon+RetrasoCF,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"DM",
"Gestación múltiple",
"PE",
"Anomalías congénitas",
"RCIU"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 5.473 | 1.598 | 18.740 | 0.00679774 |
| DM | 0.000 | 0.000 | Inf | 0.98660517 |
| Gestación múltiple | 2.673 | 0.338 | 21.117 | 0.35105401 |
| PE | 0.880 | 0.114 | 6.795 | 0.90226924 |
| Anomalías congénitas | 3.001 | 0.386 | 23.317 | 0.29355168 |
| RCIU | 1.306 | 0.168 | 10.162 | 0.79875805 |
9.2.8.2 COVID No, asintomática, leve o grave
mod.COVID=glm(FMI~Sint+Diabetes+GM+Preeclampsia+AnCon+RetrasoCF,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"DM",
"Gestación múltiple",
"PE",
"Anomalías congénitas",
"RCIU"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 4.331 | 1.077 | 17.415 | 0.03897718 |
| COVID leve | 7.330 | 1.974 | 27.221 | 0.00292343 |
| COVID grave | 4.053 | 0.673 | 24.396 | 0.12646731 |
| DM | 0.000 | 0.000 | Inf | 0.9865927 |
| Gestación múltiple | 2.487 | 0.312 | 19.820 | 0.38965798 |
| PE | 0.865 | 0.112 | 6.685 | 0.88928532 |
| Anomalías congénitas | 2.891 | 0.372 | 22.454 | 0.31000279 |
| RCIU | 1.337 | 0.171 | 10.473 | 0.78219309 |
9.2.8.3 COVID ante o periparto contra No
mod.COVID=glm(FMI~PreP+Diabetes+GM+Preeclampsia+AnCon+RetrasoCF,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"DM",
"Gestación múltiple",
"PE",
"Anomalías congénitas",
"RCIU"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 3.414 | 0.850 | 13.718 | 0.08359913 |
| COVID periparto | 8.136 | 2.258 | 29.321 | 0.00135036 |
| DM | 0.000 | 0.000 | Inf | 0.98662281 |
| Gestación múltiple | 2.869 | 0.362 | 22.769 | 0.31851844 |
| PE | 0.892 | 0.115 | 6.901 | 0.91288164 |
| Anomalías congénitas | 2.933 | 0.372 | 23.137 | 0.30724116 |
| RCIU | 1.481 | 0.189 | 11.590 | 0.70803287 |
9.2.8.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(FMI~SintPre+Diabetes+GM+Preeclampsia+AnCon+RetrasoCF,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"DM",
"Gestación múltiple",
"PE",
"Anomalías congénitas",
"RCIU"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 2.725 | 0.280 | 26.519 | 0.38784862 |
| COVID anteparto leve | 4.066 | 0.899 | 18.401 | 0.0685911 |
| COVID anteparto grave | 2.712 | 0.280 | 26.276 | 0.38930317 |
| DM | 0.000 | 0.000 | Inf | 0.99491655 |
| Gestación múltiple | 5.435 | 0.584 | 50.600 | 0.13700001 |
| PE | 1.877 | 0.205 | 17.164 | 0.57721746 |
| Anomalías congénitas | 0.000 | 0.000 | Inf | 0.9959805 |
| RCIU | 0.000 | 0.000 | Inf | 0.9933666 |
9.2.8.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(FMI~SintPeri+Diabetes+GM+Preeclampsia+AnCon+RetrasoCF,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"DM",
"Gestación múltiple",
"PE",
"Anomalías congénitas",
"RCIU"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 5.306 | 1.251 | 22.515 | 0.02361509 |
| COVID periparto leve | 19.972 | 4.618 | 86.375 | 6.127e-05 |
| COVID periparto grave | 9.227 | 0.928 | 91.713 | 0.05788793 |
| DM | 0.000 | 0.000 | Inf | 0.99521149 |
| Gestación múltiple | 4.911 | 0.577 | 41.808 | 0.14522991 |
| PE | 0.000 | 0.000 | Inf | 0.99225506 |
| Anomalías congénitas | 3.179 | 0.342 | 29.518 | 0.30907225 |
| RCIU | 3.383 | 0.383 | 29.846 | 0.27260955 |
9.2.8.6 Ola
mod.COVID=glm(FMI~SegundOla+Diabetes+GM+Preeclampsia+AnCon+RetrasoCF,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"1a ola rel. a 2a ola",
"DM",
"Gestación múltiple",
"PE",
"Anomalías congénitas",
"RCIU"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 1a ola rel. a 2a ola | 1.422 | 0.587 | 3.448 | 0.43577421 |
| DM | 0.000 | 0.000 | Inf | 0.98697035 |
| Gestación múltiple | 2.520 | 0.319 | 19.897 | 0.3807733 |
| PE | 0.962 | 0.125 | 7.422 | 0.96999053 |
| Anomalías congénitas | 3.421 | 0.443 | 26.400 | 0.23814687 |
| RCIU | 1.436 | 0.186 | 11.069 | 0.72835968 |
9.2.8.7 Infección y ola
mod.COVID=glm(FMI~SegundOla+Diabetes+GM+Preeclampsia+AnCon+RetrasoCF,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"DM",
"Gestación múltiple",
"PE",
"Anomalías congénitas",
"RCIU"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 1.251 | 0.480 | 3.264 | 0.64655829 |
| DM | 0.000 | 0.000 | Inf | 0.99355261 |
| Gestación múltiple | 0.000 | 0.000 | Inf | 0.99367493 |
| PE | 1.126 | 0.143 | 8.859 | 0.91050744 |
| Anomalías congénitas | 3.435 | 0.431 | 27.349 | 0.24376574 |
| RCIU | 1.609 | 0.200 | 12.947 | 0.65501264 |
9.2.9 Ingreso materno en UCI
9.2.9.1 COVID Sí o No
mod.COVID=glm(UCI~COVID+Preeclampsia.grave+Event.Hem+Event.Tromb+ECP.Tot+ECC,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"PE con CG",
"Eventos hemorrágicos",
"Eventos trombóticos",
"EPC",
"ECC"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 14.919 | 3.488 | 63.813 | 0.00026751 |
| PE con CG | 23.882 | 9.675 | 58.949 | <1e-08 |
| Eventos hemorrágicos | 2.534 | 0.925 | 6.944 | 0.07061371 |
| Eventos trombóticos | 42.555 | 11.580 | 156.380 | 2e-08 |
| EPC | 3.055 | 0.993 | 9.400 | 0.05147282 |
| ECC | 3.066 | 0.391 | 24.036 | 0.28613012 |
9.2.9.2 COVID No, asintomática, leve o grave
mod.COVID=glm(UCI~Sint+Preeclampsia.grave+Event.Hem+Event.Tromb+ECP.Tot+ECC,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"PE con CG",
"Eventos hemorrágicos",
"Eventos trombóticos",
"EPC",
"ECC"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 3.096 | 0.507 | 18.923 | 0.22102546 |
| COVID leve | 6.762 | 1.354 | 33.782 | 0.01986371 |
| COVID grave | 62.595 | 14.311 | 273.787 | 4e-08 |
| PE con CG | 27.869 | 9.812 | 79.154 | <1e-08 |
| Eventos hemorrágicos | 1.981 | 0.677 | 5.797 | 0.21187848 |
| Eventos trombóticos | 12.881 | 3.322 | 49.941 | 0.00021854 |
| EPC | 3.046 | 0.963 | 9.636 | 0.05797914 |
| ECC | 1.822 | 0.216 | 15.350 | 0.58093904 |
9.2.9.3 COVID ante o periparto contra No
mod.COVID=glm(UCI~PreP+Preeclampsia.grave+Event.Hem+Event.Tromb+ECP.Tot+ECC,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"PE con CG",
"Eventos hemorrágicos",
"Eventos trombóticos",
"EPC",
"ECC"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 14.886 | 3.364 | 65.864 | 0.00037243 |
| COVID periparto | 14.962 | 3.315 | 67.525 | 0.00043356 |
| PE con CG | 23.878 | 9.672 | 58.953 | <1e-08 |
| Eventos hemorrágicos | 2.533 | 0.924 | 6.948 | 0.07091508 |
| Eventos trombóticos | 42.515 | 11.491 | 157.304 | 2e-08 |
| EPC | 3.057 | 0.990 | 9.438 | 0.05204758 |
| ECC | 3.068 | 0.391 | 24.057 | 0.28611231 |
9.2.9.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(UCI~SintPre+Preeclampsia.grave+Event.Hem+Event.Tromb+ECP.Tot+ECC,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"PE con CG",
"Eventos hemorrágicos",
"Eventos trombóticos",
"EPC",
"ECC"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 3.973 | 0.351 | 45.013 | 0.26529801 |
| COVID anteparto leve | 4.031 | 0.647 | 25.097 | 0.13518706 |
| COVID anteparto grave | 55.058 | 12.024 | 252.108 | 2.4e-07 |
| PE con CG | 17.631 | 3.402 | 91.374 | 0.00062972 |
| Eventos hemorrágicos | 2.332 | 0.492 | 11.051 | 0.28601681 |
| Eventos trombóticos | 19.178 | 2.612 | 140.813 | 0.00368548 |
| EPC | 3.486 | 0.908 | 13.389 | 0.06896359 |
| ECC | 3.413 | 0.354 | 32.931 | 0.28857779 |
9.2.9.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(UCI~SintPeri+Preeclampsia.grave+Event.Hem+Event.Tromb+ECP.Tot+ECC,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"PE con CG",
"Eventos hemorrágicos",
"Eventos trombóticos",
"EPC",
"ECC"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 2.523 | 0.347 | 18.360 | 0.36070172 |
| COVID periparto leve | 18.286 | 3.139 | 106.541 | 0.00122903 |
| COVID periparto grave | 78.751 | 15.579 | 398.092 | 1.3e-07 |
| PE con CG | 32.397 | 7.716 | 136.022 | 2.02e-06 |
| Eventos hemorrágicos | 1.830 | 0.411 | 8.157 | 0.42783536 |
| Eventos trombóticos | 7.907 | 1.150 | 54.388 | 0.03558472 |
| EPC | 1.487 | 0.132 | 16.764 | 0.74796638 |
| ECC | 0.000 | 0.000 | Inf | 0.98883473 |
9.2.9.6 Ola
mod.COVID=glm(UCI~SegundOla+Preeclampsia.grave+Event.Hem+Event.Tromb+ECP.Tot+ECC,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"PE con CG",
"Eventos hemorrágicos",
"Eventos trombóticos",
"EPC",
"ECC"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 0.834 | 0.406 | 1.712 | 0.62095815 |
| PE con CG | 29.181 | 12.007 | 70.921 | <1e-08 |
| Eventos hemorrágicos | 2.447 | 0.919 | 6.511 | 0.07315894 |
| Eventos trombóticos | 60.080 | 17.043 | 211.795 | <1e-08 |
| EPC | 3.081 | 0.996 | 9.531 | 0.0507923 |
| ECC | 1.231 | 0.145 | 10.423 | 0.84908511 |
9.2.9.7 Infección y ola
mod.COVID=glm(UCI~SegundOla+Preeclampsia.grave+Event.Hem+Event.Tromb+ECP.Tot+ECC,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"PE con CG",
"Eventos hemorrágicos",
"Eventos trombóticos",
"EPC",
"ECC"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 0.671 | 0.322 | 1.398 | 0.28628109 |
| PE con CG | 25.820 | 10.158 | 65.630 | <1e-08 |
| Eventos hemorrágicos | 2.602 | 0.910 | 7.436 | 0.07428893 |
| Eventos trombóticos | 46.595 | 11.911 | 182.277 | 3e-08 |
| EPC | 3.395 | 1.082 | 10.648 | 0.03613628 |
| ECC | 4.282 | 0.542 | 33.844 | 0.16794714 |
9.2.10 HPP sí o no
9.2.10.1 COVID Sí o No
mod.COVID=glm(HPPsino~COVID+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 1.018 | 0.721 | 1.438 | 0.91731094 |
| PE con CG | 3.592 | 1.465 | 8.808 | 0.0052072 |
| Nulípara | 1.217 | 0.859 | 1.724 | 0.27030835 |
| GM | 1.674 | 0.651 | 4.304 | 0.28462929 |
| DG | 1.757 | 1.049 | 2.944 | 0.03228615 |
9.2.10.2 COVID No, asintomática, leve o grave
mod.COVID=glm(HPPsino~Sint+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 0.648 | 0.389 | 1.078 | 0.09473163 |
| COVID leve | 1.089 | 0.698 | 1.699 | 0.70835411 |
| COVID grave | 1.910 | 1.141 | 3.198 | 0.01389127 |
| PE con CG | 3.301 | 1.337 | 8.149 | 0.00958966 |
| Nulípara | 1.242 | 0.875 | 1.764 | 0.2245752 |
| GM | 1.613 | 0.626 | 4.152 | 0.32202843 |
| DG | 1.745 | 1.040 | 2.927 | 0.03487043 |
9.2.10.3 COVID ante o periparto contra No
mod.COVID=glm(HPPsino~PreP+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 0.887 | 0.582 | 1.352 | 0.57840473 |
| COVID periparto | 1.189 | 0.782 | 1.807 | 0.41930152 |
| PE con CG | 3.573 | 1.458 | 8.756 | 0.00536093 |
| Nulípara | 1.199 | 0.845 | 1.701 | 0.30865408 |
| GM | 1.707 | 0.664 | 4.388 | 0.26686557 |
| DG | 1.764 | 1.053 | 2.956 | 0.03112976 |
9.2.10.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(HPPsino~SintPre+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 0.543 | 0.215 | 1.369 | 0.19566154 |
| COVID anteparto leve | 0.949 | 0.570 | 1.581 | 0.84204839 |
| COVID anteparto grave | 1.110 | 0.541 | 2.281 | 0.77539157 |
| PE con CG | 4.923 | 1.761 | 13.764 | 0.00237418 |
| Nulípara | 1.413 | 0.943 | 2.119 | 0.09420041 |
| GM | 2.028 | 0.764 | 5.387 | 0.15598273 |
| DG | 1.959 | 1.104 | 3.475 | 0.02145811 |
9.2.10.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(HPPsino~SintPeri+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 0.703 | 0.397 | 1.245 | 0.22693586 |
| COVID periparto leve | 1.575 | 0.763 | 3.252 | 0.21956979 |
| COVID periparto grave | 4.745 | 2.381 | 9.453 | 9.55e-06 |
| PE con CG | 1.844 | 0.509 | 6.675 | 0.35138832 |
| Nulípara | 1.246 | 0.831 | 1.870 | 0.28757532 |
| GM | 1.809 | 0.614 | 5.332 | 0.28219757 |
| DG | 1.960 | 1.097 | 3.503 | 0.02306686 |
9.2.10.6 Ola
mod.COVID=glm(HPPsino~SegundOla+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 0.995 | 0.697 | 1.419 | 0.97624673 |
| PE con CG | 3.608 | 1.477 | 8.814 | 0.00487616 |
| Nulípara | 1.216 | 0.858 | 1.723 | 0.2715483 |
| GM | 1.672 | 0.650 | 4.300 | 0.28578956 |
| DG | 1.756 | 1.048 | 2.942 | 0.03240835 |
9.2.10.7 Infección y ola
mod.COVID=glm(HPPsino~SegundOla+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 0.899 | 0.557 | 1.453 | 0.6646482 |
| PE con CG | 3.630 | 1.215 | 10.849 | 0.02097164 |
| Nulípara | 0.888 | 0.536 | 1.472 | 0.64576545 |
| GM | 0.642 | 0.085 | 4.841 | 0.66696926 |
| DG | 1.090 | 0.460 | 2.583 | 0.84427044 |
9.2.11 Tipo de HPP en gestantes con HPP
El nivel de referencia de “Tipo de HPP” es “Tratamiento médico”
9.2.11.1 COVID Sí o No
mod.COVID=multinom(HPP~COVID+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres[DFL.madres$HPPsino==1,],family=binomial(link="logit"))## # weights: 21 (12 variable)
## initial value 150.509884
## iter 10 value 74.042360
## iter 20 value 73.854432
## final value 73.854089
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(Resultado)=c("Tipo de HPP","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo de HPP | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor |
|---|---|---|---|---|---|
| HPP tratamiento quirúrgico conservador | COVID | 0.764 | 0.276 | 2.112 | 0.6032812 |
| HPP tratamiento quirúrgico conservador | PE con CG | 4.899 | 0.703 | 34.111 | 0.1085469 |
| HPP tratamiento quirúrgico conservador | Nulípara | 0.509 | 0.173 | 1.496 | 0.2194324 |
| HPP tratamiento quirúrgico conservador | GM | 1.246 | 0.117 | 13.259 | 0.8555882 |
| HPP tratamiento quirúrgico conservador | DG | 1.394 | 0.350 | 5.557 | 0.6380623 |
| Histerectomía obstétrica | COVID | 4.225 | 0.462 | 38.664 | 0.2020008 |
| Histerectomía obstétrica | PE con CG | 7.799 | 0.605 | 100.479 | 0.1152614 |
| Histerectomía obstétrica | Nulípara | 1.519 | 0.278 | 8.304 | 0.6293535 |
| Histerectomía obstétrica | GM | 0.000 | 0.000 | Inf | 0.9925390 |
| Histerectomía obstétrica | DG | 0.000 | 0.000 | Inf | 0.9765838 |
9.2.11.2 COVID No, asintomática, leve o grave
mod.COVID=multinom(HPP~Sint+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres[DFL.madres$HPPsino==1,],family=binomial(link="logit"))## # weights: 27 (16 variable)
## initial value 150.509884
## iter 10 value 72.411596
## iter 20 value 72.075081
## iter 30 value 72.072516
## final value 72.072510
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c( "COVID asintomática",
"COVID leve",
"COVID grave",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(Resultado)=c("Tipo de HPP","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo de HPP | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | HPP tratamiento quirúrgico conservador | COVID asintomática | 0.634 | 0.123 | 3.280 | 0.5869787 |
| 4 | HPP tratamiento quirúrgico conservador | COVID leve | 0.915 | 0.251 | 3.335 | 0.8926958 |
| 5 | HPP tratamiento quirúrgico conservador | COVID grave | 0.699 | 0.152 | 3.218 | 0.6460593 |
| 6 | HPP tratamiento quirúrgico conservador | PE con CG | 4.970 | 0.665 | 37.145 | 0.1181522 |
| 7 | HPP tratamiento quirúrgico conservador | Nulípara | 0.503 | 0.169 | 1.496 | 0.2165433 |
| 8 | HPP tratamiento quirúrgico conservador | GM | 1.180 | 0.107 | 13.035 | 0.8923824 |
| 9 | HPP tratamiento quirúrgico conservador | DG | 1.426 | 0.347 | 5.856 | 0.6222855 |
| 12 | Histerectomía obstétrica | COVID asintomática | 0.000 | 0.000 | Inf | 0.9923372 |
| 13 | Histerectomía obstétrica | COVID leve | 6.840 | 0.641 | 72.977 | 0.1114153 |
| 14 | Histerectomía obstétrica | COVID grave | 5.229 | 0.378 | 72.273 | 0.2169639 |
| 15 | Histerectomía obstétrica | PE con CG | 6.284 | 0.354 | 111.524 | 0.2104072 |
| 16 | Histerectomía obstétrica | Nulípara | 1.617 | 0.285 | 9.167 | 0.5873933 |
| 17 | Histerectomía obstétrica | GM | 0.000 | 0.000 | 0.000 | 0.0000000 |
| 18 | Histerectomía obstétrica | DG | 0.000 | 0.000 | 0.000 | 0.0000000 |
9.2.11.3 COVID ante o periparto contra No
mod.COVID=multinom(HPP~PreP+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres[DFL.madres$HPPsino==1,],family=binomial(link="logit"))## # weights: 24 (14 variable)
## initial value 150.509884
## iter 10 value 72.813291
## iter 20 value 72.571912
## iter 30 value 72.569739
## iter 30 value 72.569739
## final value 72.569739
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID anteparto",
"COVID periparto",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(Resultado)=c("Tipo de HPP","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo de HPP | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | HPP tratamiento quirúrgico conservador | COVID anteparto | 0.577 | 0.154 | 2.159 | 0.4142426 |
| 4 | HPP tratamiento quirúrgico conservador | COVID periparto | 0.985 | 0.295 | 3.288 | 0.9801960 |
| 5 | HPP tratamiento quirúrgico conservador | PE con CG | 5.454 | 0.756 | 39.333 | 0.0924108 |
| 6 | HPP tratamiento quirúrgico conservador | Nulípara | 0.511 | 0.173 | 1.503 | 0.2223116 |
| 7 | HPP tratamiento quirúrgico conservador | GM | 1.319 | 0.125 | 13.918 | 0.8179854 |
| 8 | HPP tratamiento quirúrgico conservador | DG | 1.401 | 0.350 | 5.606 | 0.6333114 |
| 11 | Histerectomía obstétrica | COVID anteparto | 1.481 | 0.083 | 26.291 | 0.7891638 |
| 12 | Histerectomía obstétrica | COVID periparto | 7.461 | 0.750 | 74.266 | 0.0865040 |
| 13 | Histerectomía obstétrica | PE con CG | 10.433 | 0.672 | 161.907 | 0.0937188 |
| 14 | Histerectomía obstétrica | Nulípara | 1.320 | 0.231 | 7.532 | 0.7544274 |
| 15 | Histerectomía obstétrica | GM | 0.000 | 0.000 | Inf | 0.9844456 |
| 16 | Histerectomía obstétrica | DG | 0.000 | 0.000 | Inf | 0.9903825 |
9.2.11.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=multinom(HPP~SintPre+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres[DFL.madres$HPPsino==1,],family=binomial(link="logit"))## # weights: 27 (16 variable)
## initial value 110.959841
## iter 10 value 45.516932
## iter 20 value 45.228238
## iter 30 value 45.219858
## final value 45.219839
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(Resultado)=c("Tipo de HPP","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo de HPP | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | HPP tratamiento quirúrgico conservador | COVID anteparto asintomática | 1.328 | 0.131 | 1.347800e+01 | 0.8105303 |
| 4 | HPP tratamiento quirúrgico conservador | COVID anteparto leve | 0.516 | 0.098 | 2.716000e+00 | 0.4344704 |
| 5 | HPP tratamiento quirúrgico conservador | COVID anteparto grave | 0.379 | 0.029 | 4.949000e+00 | 0.4589858 |
| 6 | HPP tratamiento quirúrgico conservador | PE con CG | 5.833 | 0.667 | 5.102100e+01 | 0.1110074 |
| 7 | HPP tratamiento quirúrgico conservador | Nulípara | 0.906 | 0.271 | 3.029000e+00 | 0.8725997 |
| 8 | HPP tratamiento quirúrgico conservador | GM | 1.031 | 0.093 | 1.146300e+01 | 0.9799022 |
| 9 | HPP tratamiento quirúrgico conservador | DG | 0.442 | 0.050 | 3.900000e+00 | 0.4626932 |
| 12 | Histerectomía obstétrica | COVID anteparto asintomática | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
| 13 | Histerectomía obstétrica | COVID anteparto leve | 3.243 | 0.175 | 6.021500e+01 | 0.4299270 |
| 14 | Histerectomía obstétrica | COVID anteparto grave | 0.000 | 0.000 | Inf | 0.9892954 |
| 15 | Histerectomía obstétrica | PE con CG | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
| 16 | Histerectomía obstétrica | Nulípara | 78797266.085 | 28817446.867 | 2.154601e+08 | 0.0000000 |
| 17 | Histerectomía obstétrica | GM | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
| 18 | Histerectomía obstétrica | DG | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
9.2.11.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=multinom(HPP~SintPeri+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres[DFL.madres$HPPsino==1,],family=binomial(link="logit"))## # weights: 27 (16 variable)
## initial value 112.058453
## iter 10 value 52.548967
## iter 20 value 52.405560
## iter 30 value 52.374205
## final value 52.374042
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(Resultado)=c("Tipo de HPP","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo de HPP | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | HPP tratamiento quirúrgico conservador | COVID periparto asintomática | 0.438 | 0.049 | 3.905000e+00 | 0.4593436 |
| 4 | HPP tratamiento quirúrgico conservador | COVID periparto leve | 2.876 | 0.447 | 1.850700e+01 | 0.2659824 |
| 5 | HPP tratamiento quirúrgico conservador | COVID periparto grave | 1.134 | 0.194 | 6.634000e+00 | 0.8893395 |
| 6 | HPP tratamiento quirúrgico conservador | PE con CG | 8.082 | 0.426 | 1.533940e+02 | 0.1640630 |
| 7 | HPP tratamiento quirúrgico conservador | Nulípara | 0.447 | 0.130 | 1.533000e+00 | 0.2003075 |
| 8 | HPP tratamiento quirúrgico conservador | GM | 2.452 | 0.212 | 2.829500e+01 | 0.4722938 |
| 9 | HPP tratamiento quirúrgico conservador | DG | 2.053 | 0.451 | 9.337000e+00 | 0.3519104 |
| 12 | Histerectomía obstétrica | COVID periparto asintomática | 0.000 | 0.000 | 9.503735e+226 | 0.9715747 |
| 13 | Histerectomía obstétrica | COVID periparto leve | 25.474 | 1.533 | 4.233610e+02 | 0.0239585 |
| 14 | Histerectomía obstétrica | COVID periparto grave | 11.411 | 0.764 | 1.704460e+02 | 0.0776082 |
| 15 | Histerectomía obstétrica | PE con CG | 27.741 | 0.742 | 1.036450e+03 | 0.0720530 |
| 16 | Histerectomía obstétrica | Nulípara | 0.548 | 0.059 | 5.103000e+00 | 0.5973817 |
| 17 | Histerectomía obstétrica | GM | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
| 18 | Histerectomía obstétrica | DG | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
9.2.11.6 Ola
mod.COVID=multinom(HPP~SegundOla+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres[DFL.madres$HPPsino==1,],family=binomial(link="logit"))## # weights: 21 (12 variable)
## initial value 150.509884
## iter 10 value 72.428692
## iter 20 value 72.111783
## final value 72.111473
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("2a ola rel. a 1a ola",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(Resultado)=c("Tipo de HPP","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo de HPP | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor |
|---|---|---|---|---|---|
| HPP tratamiento quirúrgico conservador | 2a ola rel. a 1a ola | 1.271 | 0.455 | 3.549 | 0.6477657 |
| HPP tratamiento quirúrgico conservador | PE con CG | 4.741 | 0.681 | 32.989 | 0.1158876 |
| HPP tratamiento quirúrgico conservador | Nulípara | 0.541 | 0.187 | 1.564 | 0.2568134 |
| HPP tratamiento quirúrgico conservador | GM | 1.455 | 0.129 | 16.406 | 0.7615251 |
| HPP tratamiento quirúrgico conservador | DG | 1.457 | 0.366 | 5.795 | 0.5928721 |
| Histerectomía obstétrica | 2a ola rel. a 1a ola | 0.000 | 0.000 | Inf | 0.9890714 |
| Histerectomía obstétrica | PE con CG | 5.441 | 0.425 | 69.686 | 0.1928878 |
| Histerectomía obstétrica | Nulípara | 1.336 | 0.243 | 7.361 | 0.7392449 |
| Histerectomía obstétrica | GM | 0.000 | 0.000 | Inf | 0.9898396 |
| Histerectomía obstétrica | DG | 0.000 | 0.000 | Inf | 0.9813302 |
9.2.11.7 Infección y ola
mod.COVID=multinom(HPP~SegundOla+Preeclampsia.grave+Nulipara+GM+Diabetes.Gest,data=DFL.madres[DFL.madres$HPPsino==1 & DFL.madres$COVID==1,],family=binomial(link="logit"))## # weights: 21 (12 variable)
## initial value 78.001472
## iter 10 value 37.467398
## iter 20 value 37.132767
## final value 37.127149
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("Infectadas 2a ola rel. a infectadas 1a ola",
"PE con CG",
"Nulípara",
"GM",
"DG"
)
colnames(Resultado)=c("Tipo de HPP","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo de HPP | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor |
|---|---|---|---|---|---|
| HPP tratamiento quirúrgico conservador | Infectadas 2a ola rel. a infectadas 1a ola | 1.144 | 0.256 | 5.121000e+00 | 0.8601134 |
| HPP tratamiento quirúrgico conservador | PE con CG | 7.913 | 0.402 | 1.559270e+02 | 0.1737979 |
| HPP tratamiento quirúrgico conservador | Nulípara | 0.256 | 0.028 | 2.333000e+00 | 0.2267893 |
| HPP tratamiento quirúrgico conservador | GM | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
| HPP tratamiento quirúrgico conservador | DG | 2.897 | 0.411 | 2.040500e+01 | 0.2854750 |
| Histerectomía obstétrica | Infectadas 2a ola rel. a infectadas 1a ola | 0.000 | 0.000 | 1.117291e+64 | 0.8987216 |
| Histerectomía obstétrica | PE con CG | 11.891 | 0.480 | 2.946990e+02 | 0.1306438 |
| Histerectomía obstétrica | Nulípara | 0.824 | 0.108 | 6.299000e+00 | 0.8520612 |
| Histerectomía obstétrica | GM | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
| Histerectomía obstétrica | DG | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
9.2.12 Inicio de parto
9.2.12.1 COVID Sí o No
mod.COVID=multinom(Inicio.parto~COVID+Edad.cut+ECP.Tot+ECC+GM+RetrasoCF+Preeclampsia.grave+AnCon,data=DFL.madres,family=binomial(link="logit"))## # weights: 33 (20 variable)
## initial value 3399.106421
## iter 10 value 2661.942381
## iter 20 value 2630.741161
## final value 2630.468706
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"EPC",
"ECC",
"Gestación múltiple",
"RCIU",
"PE con CG",
"Anomalías congénitas"
)
colnames(Resultado)=c("Inicio","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Inicio | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 1 | Inducido | COVID | 1.395 | 1.196 | 1.628 | 0.0000230 |
| 4 | Inducido | Edad: 31-40 años rel. a 18-30 | 1.149 | 0.977 | 1.351 | 0.0939708 |
| 5 | Inducido | Edad: >40 años rel. a 18-30 | 1.892 | 1.358 | 2.636 | 0.0001653 |
| 6 | Inducido | EPC | 0.738 | 0.490 | 1.111 | 0.1455213 |
| 7 | Inducido | ECC | 2.601 | 1.341 | 5.044 | 0.0046903 |
| 8 | Inducido | Gestación múltiple | 1.450 | 0.763 | 2.755 | 0.2566658 |
| 9 | Inducido | RCIU | 6.295 | 3.657 | 10.836 | 0.0000000 |
| 10 | Inducido | PE con CG | 3.527 | 1.542 | 8.066 | 0.0028248 |
| 11 | Inducido | Anomalías congénitas | 0.907 | 0.465 | 1.770 | 0.7739791 |
| 12 | Cesárea programada | COVID | 2.263 | 1.683 | 3.044 | 0.0000001 |
| 15 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.306 | 0.960 | 1.776 | 0.0893111 |
| 16 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.388 | 1.384 | 4.118 | 0.0017492 |
| 17 | Cesárea programada | EPC | 1.039 | 0.534 | 2.021 | 0.9111193 |
| 18 | Cesárea programada | ECC | 3.344 | 1.242 | 9.006 | 0.0169430 |
| 19 | Cesárea programada | Gestación múltiple | 7.831 | 4.059 | 15.109 | 0.0000000 |
| 20 | Cesárea programada | RCIU | 7.398 | 3.665 | 14.935 | 0.0000000 |
| 21 | Cesárea programada | PE con CG | 5.984 | 2.237 | 16.006 | 0.0003654 |
| 22 | Cesárea programada | Anomalías congénitas | 0.976 | 0.323 | 2.949 | 0.9652142 |
9.2.12.2 COVID No, asintomática, leve o grave
mod.COVID=multinom(Inicio.parto~Sint+Edad.cut+ECP.Tot+ECC+GM+RetrasoCF+Preeclampsia.grave+AnCon,data=DFL.madres,family=binomial(link="logit"))## # weights: 39 (24 variable)
## initial value 3399.106421
## iter 10 value 2649.547112
## iter 20 value 2616.892379
## iter 30 value 2616.504067
## final value 2616.504014
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c( "COVID asintomática",
"COVID leve",
"COVID grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"EPC",
"ECC",
"Gestación múltiple",
"RCIU",
"PE con CG",
"Anomalías congénitas"
)
colnames(Resultado)=c("Inicio","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Inicio | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | Inducido | COVID asintomática | 1.238 | 1.020 | 1.502 | 0.0306215 |
| 4 | Inducido | COVID leve | 1.446 | 1.180 | 1.773 | 0.0003754 |
| 5 | Inducido | COVID grave | 1.823 | 1.361 | 2.441 | 0.0000571 |
| 8 | Inducido | Edad: 31-40 años rel. a 18-30 | 1.147 | 0.975 | 1.350 | 0.0978766 |
| 9 | Inducido | Edad: >40 años rel. a 18-30 | 1.885 | 1.352 | 2.627 | 0.0001841 |
| 10 | Inducido | EPC | 0.727 | 0.482 | 1.096 | 0.1274529 |
| 11 | Inducido | ECC | 2.542 | 1.309 | 4.937 | 0.0058467 |
| 12 | Inducido | Gestación múltiple | 1.429 | 0.751 | 2.719 | 0.2768260 |
| 13 | Inducido | RCIU | 6.442 | 3.742 | 11.092 | 0.0000000 |
| 14 | Inducido | PE con CG | 3.494 | 1.526 | 7.999 | 0.0030712 |
| 15 | Inducido | Anomalías congénitas | 0.901 | 0.461 | 1.761 | 0.7611547 |
| 18 | Cesárea programada | COVID asintomática | 1.440 | 0.976 | 2.125 | 0.0660573 |
| 19 | Cesárea programada | COVID leve | 2.327 | 1.612 | 3.360 | 0.0000066 |
| 20 | Cesárea programada | COVID grave | 5.041 | 3.291 | 7.723 | 0.0000000 |
| 23 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.294 | 0.949 | 1.763 | 0.1034476 |
| 24 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.300 | 1.326 | 3.987 | 0.0030140 |
| 25 | Cesárea programada | EPC | 0.982 | 0.500 | 1.926 | 0.9570082 |
| 26 | Cesárea programada | ECC | 3.108 | 1.148 | 8.413 | 0.0256341 |
| 27 | Cesárea programada | Gestación múltiple | 7.579 | 3.903 | 14.718 | 0.0000000 |
| 28 | Cesárea programada | RCIU | 8.015 | 3.953 | 16.250 | 0.0000000 |
| 29 | Cesárea programada | PE con CG | 5.509 | 2.037 | 14.897 | 0.0007742 |
| 30 | Cesárea programada | Anomalías congénitas | 1.023 | 0.340 | 3.077 | 0.9683912 |
9.2.12.3 COVID ante o periparto contra No
mod.COVID=multinom(Inicio.parto~PreP+Edad.cut+ECP.Tot+ECC+GM+RetrasoCF+Preeclampsia.grave+AnCon,data=DFL.madres,family=binomial(link="logit"))## # weights: 36 (22 variable)
## initial value 3399.106421
## iter 10 value 2660.177258
## iter 20 value 2628.790313
## iter 30 value 2627.750606
## iter 30 value 2627.750597
## iter 30 value 2627.750597
## final value 2627.750597
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID anteparto",
"COVID periparto",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"EPC",
"ECC",
"Gestación múltiple",
"RCIU",
"PE con CG",
"Anomalías congénitas"
)
colnames(Resultado)=c("Inicio","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Inicio | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | Inducido | COVID anteparto | 1.529 | 1.277 | 1.832 | 0.0000040 |
| 4 | Inducido | COVID periparto | 1.244 | 1.024 | 1.513 | 0.0282584 |
| 7 | Inducido | Edad: 31-40 años rel. a 18-30 | 1.155 | 0.981 | 1.358 | 0.0829933 |
| 8 | Inducido | Edad: >40 años rel. a 18-30 | 1.911 | 1.371 | 2.664 | 0.0001327 |
| 9 | Inducido | EPC | 0.724 | 0.480 | 1.092 | 0.1232309 |
| 10 | Inducido | ECC | 2.585 | 1.332 | 5.017 | 0.0049815 |
| 11 | Inducido | Gestación múltiple | 1.447 | 0.761 | 2.751 | 0.2596341 |
| 12 | Inducido | RCIU | 6.243 | 3.625 | 10.751 | 0.0000000 |
| 13 | Inducido | PE con CG | 3.576 | 1.562 | 8.187 | 0.0025610 |
| 14 | Inducido | Anomalías congénitas | 0.906 | 0.464 | 1.768 | 0.7716360 |
| 17 | Cesárea programada | COVID anteparto | 2.606 | 1.876 | 3.621 | 0.0000000 |
| 18 | Cesárea programada | COVID periparto | 1.871 | 1.296 | 2.702 | 0.0008274 |
| 21 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.316 | 0.967 | 1.790 | 0.0808165 |
| 22 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.422 | 1.403 | 4.181 | 0.0014954 |
| 23 | Cesárea programada | EPC | 1.005 | 0.516 | 1.959 | 0.9879139 |
| 24 | Cesárea programada | ECC | 3.308 | 1.227 | 8.918 | 0.0180385 |
| 25 | Cesárea programada | Gestación múltiple | 7.744 | 4.005 | 14.976 | 0.0000000 |
| 26 | Cesárea programada | RCIU | 7.265 | 3.593 | 14.690 | 0.0000000 |
| 27 | Cesárea programada | PE con CG | 6.107 | 2.276 | 16.382 | 0.0003260 |
| 28 | Cesárea programada | Anomalías congénitas | 0.983 | 0.326 | 2.964 | 0.9750786 |
9.2.12.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=multinom(Inicio.parto~SintPre+Edad.cut+ECP.Tot+ECC+GM+RetrasoCF+Preeclampsia.grave+AnCon,data=DFL.madres,family=binomial(link="logit"))## # weights: 39 (24 variable)
## initial value 2623.486145
## iter 10 value 2021.797154
## iter 20 value 2007.122078
## iter 30 value 2006.765222
## iter 30 value 2006.765220
## iter 30 value 2006.765220
## final value 2006.765220
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"EPC",
"ECC",
"Gestación múltiple",
"RCIU",
"PE con CG",
"Anomalías congénitas"
)
colnames(Resultado)=c("Inicio","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Inicio | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | Inducido | COVID anteparto asintomática | 1.606 | 1.177 | 2.193 | 0.0028489 |
| 4 | Inducido | COVID anteparto leve | 1.439 | 1.153 | 1.796 | 0.0012928 |
| 5 | Inducido | COVID anteparto grave | 1.647 | 1.185 | 2.288 | 0.0029493 |
| 8 | Inducido | Edad: 31-40 años rel. a 18-30 | 1.133 | 0.942 | 1.363 | 0.1853538 |
| 9 | Inducido | Edad: >40 años rel. a 18-30 | 1.938 | 1.312 | 2.860 | 0.0008747 |
| 10 | Inducido | EPC | 0.743 | 0.470 | 1.173 | 0.2021438 |
| 11 | Inducido | ECC | 2.245 | 1.105 | 4.560 | 0.0253333 |
| 12 | Inducido | Gestación múltiple | 1.545 | 0.763 | 3.128 | 0.2270079 |
| 13 | Inducido | RCIU | 5.572 | 3.095 | 10.031 | 0.0000000 |
| 14 | Inducido | PE con CG | 4.203 | 1.511 | 11.691 | 0.0059489 |
| 15 | Inducido | Anomalías congénitas | 1.137 | 0.524 | 2.469 | 0.7450758 |
| 18 | Cesárea programada | COVID anteparto asintomática | 2.323 | 1.343 | 4.019 | 0.0025795 |
| 19 | Cesárea programada | COVID anteparto leve | 2.184 | 1.464 | 3.257 | 0.0001290 |
| 20 | Cesárea programada | COVID anteparto grave | 4.173 | 2.567 | 6.785 | 0.0000000 |
| 23 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.394 | 0.973 | 1.999 | 0.0703246 |
| 24 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.568 | 1.345 | 4.906 | 0.0042777 |
| 25 | Cesárea programada | EPC | 1.308 | 0.656 | 2.606 | 0.4452338 |
| 26 | Cesárea programada | ECC | 2.446 | 0.770 | 7.768 | 0.1292000 |
| 27 | Cesárea programada | Gestación múltiple | 8.151 | 3.901 | 17.032 | 0.0000000 |
| 28 | Cesárea programada | RCIU | 6.557 | 2.993 | 14.363 | 0.0000026 |
| 29 | Cesárea programada | PE con CG | 3.172 | 0.779 | 12.919 | 0.1071117 |
| 30 | Cesárea programada | Anomalías congénitas | 1.218 | 0.338 | 4.395 | 0.7630815 |
9.2.12.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=multinom(Inicio.parto~SintPeri+Edad.cut+ECP.Tot+ECC+GM+RetrasoCF+Preeclampsia.grave+AnCon,data=DFL.madres,family=binomial(link="logit"))## # weights: 39 (24 variable)
## initial value 2407.059524
## iter 10 value 1791.843341
## iter 20 value 1772.457498
## final value 1772.352128
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"EPC",
"ECC",
"Gestación múltiple",
"RCIU",
"PE con CG",
"Anomalías congénitas"
)
colnames(Resultado)=c("Inicio","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Inicio | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | Inducido | COVID periparto asintomática | 1.124 | 0.902 | 1.401 | 0.2974302 |
| 4 | Inducido | COVID periparto leve | 1.469 | 0.997 | 2.164 | 0.0517149 |
| 5 | Inducido | COVID periparto grave | 2.558 | 1.432 | 4.568 | 0.0015015 |
| 8 | Inducido | Edad: 31-40 años rel. a 18-30 | 1.207 | 0.992 | 1.470 | 0.0607394 |
| 9 | Inducido | Edad: >40 años rel. a 18-30 | 2.088 | 1.405 | 3.102 | 0.0002685 |
| 10 | Inducido | EPC | 0.498 | 0.280 | 0.887 | 0.0178375 |
| 11 | Inducido | ECC | 4.406 | 1.891 | 10.264 | 0.0005890 |
| 12 | Inducido | Gestación múltiple | 1.419 | 0.661 | 3.048 | 0.3694495 |
| 13 | Inducido | RCIU | 6.234 | 3.311 | 11.737 | 0.0000000 |
| 14 | Inducido | PE con CG | 1.780 | 0.651 | 4.866 | 0.2612424 |
| 15 | Inducido | Anomalías congénitas | 0.614 | 0.250 | 1.504 | 0.2857478 |
| 18 | Cesárea programada | COVID periparto asintomática | 1.128 | 0.708 | 1.797 | 0.6116350 |
| 19 | Cesárea programada | COVID periparto leve | 3.016 | 1.639 | 5.549 | 0.0003887 |
| 20 | Cesárea programada | COVID periparto grave | 7.684 | 3.731 | 15.826 | 0.0000000 |
| 23 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.242 | 0.830 | 1.859 | 0.2927903 |
| 24 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.508 | 1.247 | 5.042 | 0.0098957 |
| 25 | Cesárea programada | EPC | 0.296 | 0.069 | 1.276 | 0.1024055 |
| 26 | Cesárea programada | ECC | 5.242 | 1.550 | 17.729 | 0.0077110 |
| 27 | Cesárea programada | Gestación múltiple | 7.603 | 3.303 | 17.500 | 0.0000018 |
| 28 | Cesárea programada | RCIU | 4.253 | 1.517 | 11.922 | 0.0059159 |
| 29 | Cesárea programada | PE con CG | 6.074 | 1.981 | 18.624 | 0.0016023 |
| 30 | Cesárea programada | Anomalías congénitas | 0.363 | 0.046 | 2.877 | 0.3370698 |
9.2.12.6 Ola
mod.COVID=multinom(Inicio.parto~SegundOla+Edad.cut+ECP.Tot+ECC+GM+RetrasoCF+Preeclampsia.grave+AnCon,data=DFL.madres,family=binomial(link="logit"))## # weights: 33 (20 variable)
## initial value 3399.106421
## iter 10 value 2684.553121
## iter 20 value 2650.309497
## final value 2649.987204
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("2a ola rel. a 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"EPC",
"ECC",
"Gestación múltiple",
"RCIU",
"PE con CG",
"Anomalías congénitas"
)
colnames(Resultado)=c("Inicio","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Inicio | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 1 | Inducido | 2a ola rel. a 1a ola | 1.065 | 0.908 | 1.248 | 0.4392768 |
| 4 | Inducido | Edad: 31-40 años rel. a 18-30 | 1.139 | 0.968 | 1.339 | 0.1169616 |
| 5 | Inducido | Edad: >40 años rel. a 18-30 | 1.916 | 1.375 | 2.668 | 0.0001199 |
| 6 | Inducido | EPC | 0.756 | 0.503 | 1.137 | 0.1794678 |
| 7 | Inducido | ECC | 2.542 | 1.312 | 4.924 | 0.0056879 |
| 8 | Inducido | Gestación múltiple | 1.411 | 0.745 | 2.675 | 0.2909247 |
| 9 | Inducido | RCIU | 6.319 | 3.675 | 10.864 | 0.0000000 |
| 10 | Inducido | PE con CG | 3.805 | 1.666 | 8.688 | 0.0015139 |
| 11 | Inducido | Anomalías congénitas | 0.942 | 0.484 | 1.835 | 0.8606911 |
| 12 | Cesárea programada | 2a ola rel. a 1a ola | 0.911 | 0.677 | 1.225 | 0.5368571 |
| 15 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.251 | 0.920 | 1.700 | 0.1534436 |
| 16 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.409 | 1.402 | 4.140 | 0.0014578 |
| 17 | Cesárea programada | EPC | 1.098 | 0.566 | 2.129 | 0.7821121 |
| 18 | Cesárea programada | ECC | 2.979 | 1.105 | 8.026 | 0.0309126 |
| 19 | Cesárea programada | Gestación múltiple | 7.319 | 3.832 | 13.977 | 0.0000000 |
| 20 | Cesárea programada | RCIU | 7.502 | 3.736 | 15.064 | 0.0000000 |
| 21 | Cesárea programada | PE con CG | 6.937 | 2.601 | 18.502 | 0.0001091 |
| 22 | Cesárea programada | Anomalías congénitas | 1.064 | 0.352 | 3.215 | 0.9117993 |
9.2.12.7 Infección y ola
mod.COVID=multinom(Inicio.parto~SegundOla+Edad.cut+ECP.Tot+ECC+GM+RetrasoCF+Preeclampsia.grave+AnCon,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))## # weights: 33 (20 variable)
## initial value 1767.667172
## iter 10 value 1460.815666
## iter 20 value 1448.294676
## final value 1448.266866
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("Infectadas 2a ola rel. a infectadas 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"EPC",
"ECC",
"Gestación múltiple",
"RCIU",
"PE con CG",
"Anomalías congénitas"
)
colnames(Resultado)=c("Inicio","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Inicio | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 1 | Inducido | Infectadas 2a ola rel. a infectadas 1a ola | 1.016 | 0.821 | 1.257 | 0.8845099 |
| 4 | Inducido | Edad: 31-40 años rel. a 18-30 | 1.110 | 0.888 | 1.387 | 0.3606774 |
| 5 | Inducido | Edad: >40 años rel. a 18-30 | 1.615 | 1.035 | 2.518 | 0.0345726 |
| 6 | Inducido | EPC | 1.017 | 0.608 | 1.699 | 0.9496087 |
| 7 | Inducido | ECC | 1.515 | 0.564 | 4.068 | 0.4099553 |
| 8 | Inducido | Gestación múltiple | 1.301 | 0.491 | 3.453 | 0.5966512 |
| 9 | Inducido | RCIU | 8.322 | 3.472 | 19.947 | 0.0000020 |
| 10 | Inducido | PE con CG | 5.327 | 1.765 | 16.075 | 0.0029935 |
| 11 | Inducido | Anomalías congénitas | 1.059 | 0.461 | 2.437 | 0.8918627 |
| 12 | Cesárea programada | Infectadas 2a ola rel. a infectadas 1a ola | 0.668 | 0.464 | 0.961 | 0.0298840 |
| 15 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.180 | 0.804 | 1.732 | 0.3970564 |
| 16 | Cesárea programada | Edad: >40 años rel. a 18-30 | 1.886 | 0.957 | 3.715 | 0.0668273 |
| 17 | Cesárea programada | EPC | 1.428 | 0.662 | 3.081 | 0.3637489 |
| 18 | Cesárea programada | ECC | 2.594 | 0.676 | 9.959 | 0.1648872 |
| 19 | Cesárea programada | Gestación múltiple | 8.072 | 3.215 | 20.268 | 0.0000087 |
| 20 | Cesárea programada | RCIU | 13.921 | 5.257 | 36.869 | 0.0000001 |
| 21 | Cesárea programada | PE con CG | 8.624 | 2.465 | 30.175 | 0.0007471 |
| 22 | Cesárea programada | Anomalías congénitas | 1.486 | 0.452 | 4.887 | 0.5140776 |
9.2.13 Tipos de parto
9.2.13.1 COVID Sí o No
mod.COVID=multinom(Tipo.parto~COVID+Edad.cut+Obesidad+Diabetes+ECP.Tot+ECC+Nulipara+GM+FMI+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))## # weights: 52 (36 variable)
## initial value 4321.079524
## iter 10 value 3137.400030
## iter 20 value 3056.371242
## iter 30 value 3054.052961
## final value 3054.017136
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Obesidad",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple",
"Feto muerto anteparto",
"DG"
)
colnames(Resultado)=c("Tipo","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 1 | Instrumental | COVID | 0.793 | 0.634 | 0.992 | 0.0424839 |
| 4 | Instrumental | Edad: 31-40 años rel. a 18-30 | 1.564 | 1.228 | 1.992 | 0.0002944 |
| 5 | Instrumental | Edad: >40 años rel. a 18-30 | 2.259 | 1.368 | 3.730 | 0.0014493 |
| 6 | Instrumental | Obesidad | 1.264 | 0.929 | 1.720 | 0.1352214 |
| 7 | Instrumental | DM | 1.856 | 0.816 | 4.225 | 0.1404585 |
| 8 | Instrumental | EPC | 1.020 | 0.564 | 1.843 | 0.9484305 |
| 9 | Instrumental | ECC | 1.185 | 0.437 | 3.212 | 0.7392380 |
| 10 | Instrumental | Nulípara | 3.273 | 2.599 | 4.121 | 0.0000000 |
| 11 | Instrumental | Gestación múltiple | 1.364 | 0.537 | 3.466 | 0.5135425 |
| 14 | Instrumental | Feto muerto anteparto | 0.295 | 0.038 | 2.278 | 0.2417724 |
| 15 | Instrumental | DG | 0.861 | 0.538 | 1.376 | 0.5307235 |
| 16 | Cesárea programada | COVID | 2.113 | 1.578 | 2.830 | 0.0000005 |
| 19 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.462 | 1.077 | 1.984 | 0.0148977 |
| 20 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.533 | 1.473 | 4.353 | 0.0007723 |
| 21 | Cesárea programada | Obesidad | 1.782 | 1.277 | 2.486 | 0.0006742 |
| 22 | Cesárea programada | DM | 1.424 | 0.584 | 3.470 | 0.4370041 |
| 23 | Cesárea programada | EPC | 1.139 | 0.588 | 2.207 | 0.6994463 |
| 24 | Cesárea programada | ECC | 1.993 | 0.722 | 5.499 | 0.1828629 |
| 25 | Cesárea programada | Nulípara | 1.123 | 0.832 | 1.516 | 0.4470818 |
| 26 | Cesárea programada | Gestación múltiple | 8.973 | 4.728 | 17.030 | 0.0000000 |
| 29 | Cesárea programada | Feto muerto anteparto | 0.384 | 0.049 | 3.014 | 0.3627078 |
| 30 | Cesárea programada | DG | 1.302 | 0.803 | 2.111 | 0.2840427 |
| 31 | Cesárea urgente | COVID | 1.159 | 0.944 | 1.422 | 0.1587958 |
| 34 | Cesárea urgente | Edad: 31-40 años rel. a 18-30 | 1.564 | 1.248 | 1.960 | 0.0001022 |
| 35 | Cesárea urgente | Edad: >40 años rel. a 18-30 | 3.977 | 2.686 | 5.887 | 0.0000000 |
| 36 | Cesárea urgente | Obesidad | 1.917 | 1.491 | 2.466 | 0.0000004 |
| 37 | Cesárea urgente | DM | 1.308 | 0.646 | 2.649 | 0.4552172 |
| 38 | Cesárea urgente | EPC | 1.138 | 0.684 | 1.892 | 0.6195201 |
| 39 | Cesárea urgente | ECC | 2.294 | 1.095 | 4.807 | 0.0278038 |
| 40 | Cesárea urgente | Nulípara | 2.596 | 2.102 | 3.206 | 0.0000000 |
| 41 | Cesárea urgente | Gestación múltiple | 2.144 | 1.021 | 4.505 | 0.0440457 |
| 44 | Cesárea urgente | Feto muerto anteparto | 0.781 | 0.248 | 2.461 | 0.6724153 |
| 45 | Cesárea urgente | DG | 1.570 | 1.105 | 2.231 | 0.0117401 |
9.2.13.2 COVID No, asintomática, leve o grave
mod.COVID=multinom(Tipo.parto~Sint+Edad.cut+Obesidad+Diabetes+ECP.Tot+ECC+Nulipara+GM+FMI+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))## # weights: 60 (42 variable)
## initial value 4321.079524
## iter 10 value 3151.190495
## iter 20 value 3042.488279
## iter 30 value 3038.726853
## iter 40 value 3038.604793
## final value 3038.604436
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c( "COVID asintomática",
"COVID leve",
"COVID grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Obesidad",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple",
"Feto muerto anteparto",
"DG"
)
colnames(Resultado)=c("Tipo","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | Instrumental | COVID asintomática | 0.817 | 0.615 | 1.084 | 0.1611652 |
| 4 | Instrumental | COVID leve | 0.817 | 0.605 | 1.102 | 0.1851534 |
| 5 | Instrumental | COVID grave | 0.645 | 0.392 | 1.060 | 0.0836985 |
| 8 | Instrumental | Edad: 31-40 años rel. a 18-30 | 1.566 | 1.229 | 1.996 | 0.0002831 |
| 9 | Instrumental | Edad: >40 años rel. a 18-30 | 2.272 | 1.376 | 3.753 | 0.0013413 |
| 10 | Instrumental | Obesidad | 1.270 | 0.933 | 1.729 | 0.1283532 |
| 11 | Instrumental | DM | 1.857 | 0.816 | 4.226 | 0.1399518 |
| 12 | Instrumental | EPC | 1.025 | 0.566 | 1.853 | 0.9361571 |
| 13 | Instrumental | ECC | 1.206 | 0.444 | 3.273 | 0.7132519 |
| 14 | Instrumental | Nulípara | 3.262 | 2.590 | 4.108 | 0.0000000 |
| 15 | Instrumental | Gestación múltiple | 1.350 | 0.531 | 3.433 | 0.5284002 |
| 18 | Instrumental | Feto muerto anteparto | 0.290 | 0.037 | 2.239 | 0.2350278 |
| 19 | Instrumental | DG | 0.861 | 0.538 | 1.377 | 0.5318168 |
| 22 | Cesárea programada | COVID asintomática | 1.407 | 0.960 | 2.062 | 0.0798184 |
| 23 | Cesárea programada | COVID leve | 2.019 | 1.407 | 2.896 | 0.0001370 |
| 24 | Cesárea programada | COVID grave | 4.431 | 2.971 | 6.610 | 0.0000000 |
| 27 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.435 | 1.055 | 1.952 | 0.0213884 |
| 28 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.468 | 1.429 | 4.260 | 0.0011848 |
| 29 | Cesárea programada | Obesidad | 1.695 | 1.211 | 2.372 | 0.0020917 |
| 30 | Cesárea programada | DM | 1.351 | 0.545 | 3.349 | 0.5156246 |
| 31 | Cesárea programada | EPC | 1.079 | 0.551 | 2.114 | 0.8236103 |
| 32 | Cesárea programada | ECC | 1.867 | 0.673 | 5.178 | 0.2305346 |
| 33 | Cesárea programada | Nulípara | 1.157 | 0.856 | 1.565 | 0.3426781 |
| 34 | Cesárea programada | Gestación múltiple | 8.840 | 4.621 | 16.910 | 0.0000000 |
| 37 | Cesárea programada | Feto muerto anteparto | 0.403 | 0.051 | 3.166 | 0.3875022 |
| 38 | Cesárea programada | DG | 1.328 | 0.815 | 2.164 | 0.2550511 |
| 41 | Cesárea urgente | COVID asintomática | 1.183 | 0.918 | 1.524 | 0.1943688 |
| 42 | Cesárea urgente | COVID leve | 1.037 | 0.788 | 1.364 | 0.7948118 |
| 43 | Cesárea urgente | COVID grave | 1.413 | 0.981 | 2.035 | 0.0636253 |
| 46 | Cesárea urgente | Edad: 31-40 años rel. a 18-30 | 1.555 | 1.241 | 1.949 | 0.0001281 |
| 47 | Cesárea urgente | Edad: >40 años rel. a 18-30 | 3.954 | 2.670 | 5.857 | 0.0000000 |
| 48 | Cesárea urgente | Obesidad | 1.906 | 1.481 | 2.453 | 0.0000005 |
| 49 | Cesárea urgente | DM | 1.311 | 0.646 | 2.660 | 0.4536585 |
| 50 | Cesárea urgente | EPC | 1.155 | 0.693 | 1.923 | 0.5803742 |
| 51 | Cesárea urgente | ECC | 2.254 | 1.073 | 4.738 | 0.0319391 |
| 52 | Cesárea urgente | Nulípara | 2.610 | 2.113 | 3.224 | 0.0000000 |
| 53 | Cesárea urgente | Gestación múltiple | 2.162 | 1.026 | 4.552 | 0.0424957 |
| 56 | Cesárea urgente | Feto muerto anteparto | 0.795 | 0.252 | 2.509 | 0.6959512 |
| 57 | Cesárea urgente | DG | 1.576 | 1.109 | 2.239 | 0.0111072 |
9.2.13.3 COVID ante o periparto contra No
mod.COVID=multinom(Tipo.parto~PreP+Edad.cut+Obesidad+Diabetes+ECP.Tot+ECC+Nulipara+GM+FMI+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))## # weights: 56 (39 variable)
## initial value 4321.079524
## iter 10 value 3155.575867
## iter 20 value 3056.825719
## iter 30 value 3051.535871
## iter 40 value 3051.291061
## final value 3051.290909
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID anteparto",
"COVID periparto",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Obesidad",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple",
"Feto muerto anteparto",
"DG"
)
colnames(Resultado)=c("Tipo","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | Instrumental | COVID anteparto | 0.744 | 0.567 | 0.976 | 0.0329618 |
| 4 | Instrumental | COVID periparto | 0.857 | 0.645 | 1.140 | 0.2904924 |
| 7 | Instrumental | Edad: 31-40 años rel. a 18-30 | 1.559 | 1.224 | 1.986 | 0.0003278 |
| 8 | Instrumental | Edad: >40 años rel. a 18-30 | 2.246 | 1.360 | 3.709 | 0.0015745 |
| 9 | Instrumental | Obesidad | 1.268 | 0.932 | 1.725 | 0.1311573 |
| 10 | Instrumental | DM | 1.879 | 0.825 | 4.282 | 0.1330773 |
| 11 | Instrumental | EPC | 1.030 | 0.569 | 1.863 | 0.9221676 |
| 12 | Instrumental | ECC | 1.187 | 0.438 | 3.220 | 0.7360314 |
| 13 | Instrumental | Nulípara | 3.255 | 2.584 | 4.100 | 0.0000000 |
| 14 | Instrumental | Gestación múltiple | 1.376 | 0.541 | 3.496 | 0.5026367 |
| 17 | Instrumental | Feto muerto anteparto | 0.288 | 0.037 | 2.223 | 0.2324274 |
| 18 | Instrumental | DG | 0.860 | 0.538 | 1.376 | 0.5299635 |
| 21 | Cesárea programada | COVID anteparto | 2.161 | 1.564 | 2.985 | 0.0000030 |
| 22 | Cesárea programada | COVID periparto | 2.042 | 1.430 | 2.917 | 0.0000852 |
| 25 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.463 | 1.078 | 1.987 | 0.0146382 |
| 26 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.540 | 1.477 | 4.368 | 0.0007528 |
| 27 | Cesárea programada | Obesidad | 1.780 | 1.276 | 2.483 | 0.0006891 |
| 28 | Cesárea programada | DM | 1.419 | 0.583 | 3.455 | 0.4406606 |
| 29 | Cesárea programada | EPC | 1.130 | 0.582 | 2.192 | 0.7182113 |
| 30 | Cesárea programada | ECC | 1.991 | 0.722 | 5.487 | 0.1832700 |
| 31 | Cesárea programada | Nulípara | 1.128 | 0.836 | 1.523 | 0.4312961 |
| 32 | Cesárea programada | Gestación múltiple | 8.933 | 4.704 | 16.963 | 0.0000000 |
| 35 | Cesárea programada | Feto muerto anteparto | 0.389 | 0.049 | 3.059 | 0.3694570 |
| 36 | Cesárea programada | DG | 1.304 | 0.805 | 2.114 | 0.2808788 |
| 39 | Cesárea urgente | COVID anteparto | 1.004 | 0.785 | 1.284 | 0.9742464 |
| 40 | Cesárea urgente | COVID periparto | 1.363 | 1.062 | 1.749 | 0.0149234 |
| 43 | Cesárea urgente | Edad: 31-40 años rel. a 18-30 | 1.553 | 1.239 | 1.946 | 0.0001354 |
| 44 | Cesárea urgente | Edad: >40 años rel. a 18-30 | 3.922 | 2.648 | 5.809 | 0.0000000 |
| 45 | Cesárea urgente | Obesidad | 1.928 | 1.499 | 2.481 | 0.0000003 |
| 46 | Cesárea urgente | DM | 1.349 | 0.666 | 2.734 | 0.4058611 |
| 47 | Cesárea urgente | EPC | 1.169 | 0.702 | 1.946 | 0.5485202 |
| 48 | Cesárea urgente | ECC | 2.304 | 1.098 | 4.835 | 0.0272793 |
| 49 | Cesárea urgente | Nulípara | 2.563 | 2.075 | 3.166 | 0.0000000 |
| 50 | Cesárea urgente | Gestación múltiple | 2.183 | 1.038 | 4.591 | 0.0395479 |
| 53 | Cesárea urgente | Feto muerto anteparto | 0.738 | 0.234 | 2.333 | 0.6053351 |
| 54 | Cesárea urgente | DG | 1.568 | 1.104 | 2.229 | 0.0120855 |
9.2.13.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=multinom(Tipo.parto~SintPre+Edad.cut+Obesidad+Diabetes+ECP.Tot+ECC+Nulipara+GM+FMI+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))## # weights: 60 (42 variable)
## initial value 3321.561289
## iter 10 value 2628.829906
## iter 20 value 2292.397616
## iter 30 value 2282.564940
## iter 40 value 2281.519482
## iter 50 value 2281.390603
## iter 60 value 2281.382250
## final value 2281.382210
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Obesidad",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple",
"Feto muerto anteparto",
"DG"
)
colnames(Resultado)=c("Inicio","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Inicio | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | Instrumental | COVID anteparto asintomática | 0.895 | 0.565 | 1.419000e+00 | 0.6366627 |
| 4 | Instrumental | COVID anteparto leve | 0.814 | 0.587 | 1.130000e+00 | 0.2187026 |
| 5 | Instrumental | COVID anteparto grave | 0.382 | 0.196 | 7.470000e-01 | 0.0048948 |
| 8 | Instrumental | Edad: 31-40 años rel. a 18-30 | 1.549 | 1.178 | 2.037000e+00 | 0.0017549 |
| 9 | Instrumental | Edad: >40 años rel. a 18-30 | 2.816 | 1.600 | 4.958000e+00 | 0.0003323 |
| 10 | Instrumental | Obesidad | 1.329 | 0.942 | 1.874000e+00 | 0.1048977 |
| 11 | Instrumental | DM | 2.009 | 0.822 | 4.912000e+00 | 0.1260201 |
| 12 | Instrumental | EPC | 1.067 | 0.561 | 2.030000e+00 | 0.8430644 |
| 13 | Instrumental | ECC | 1.005 | 0.334 | 3.022000e+00 | 0.9934143 |
| 14 | Instrumental | Nulípara | 3.335 | 2.567 | 4.333000e+00 | 0.0000000 |
| 15 | Instrumental | Gestación múltiple | 1.366 | 0.491 | 3.797000e+00 | 0.5502671 |
| 18 | Instrumental | Feto muerto anteparto | 0.000 | 0.000 | 8.945027e+234 | 0.9662398 |
| 19 | Instrumental | DG | 0.850 | 0.501 | 1.441000e+00 | 0.5456455 |
| 22 | Cesárea programada | COVID anteparto asintomática | 1.981 | 1.162 | 3.378000e+00 | 0.0120710 |
| 23 | Cesárea programada | COVID anteparto leve | 1.822 | 1.229 | 2.699000e+00 | 0.0028084 |
| 24 | Cesárea programada | COVID anteparto grave | 3.287 | 2.070 | 5.218000e+00 | 0.0000005 |
| 27 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.549 | 1.078 | 2.225000e+00 | 0.0179159 |
| 28 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.695 | 1.393 | 5.215000e+00 | 0.0032444 |
| 29 | Cesárea programada | Obesidad | 1.371 | 0.910 | 2.064000e+00 | 0.1310462 |
| 30 | Cesárea programada | DM | 1.494 | 0.545 | 4.099000e+00 | 0.4352785 |
| 31 | Cesárea programada | EPC | 1.402 | 0.707 | 2.779000e+00 | 0.3332314 |
| 32 | Cesárea programada | ECC | 1.296 | 0.367 | 4.572000e+00 | 0.6872025 |
| 33 | Cesárea programada | Nulípara | 1.205 | 0.845 | 1.717000e+00 | 0.3023389 |
| 34 | Cesárea programada | Gestación múltiple | 9.185 | 4.519 | 1.866800e+01 | 0.0000000 |
| 37 | Cesárea programada | Feto muerto anteparto | 0.000 | 0.000 | 0.000000e+00 | 0.0000000 |
| 38 | Cesárea programada | DG | 1.426 | 0.813 | 2.501000e+00 | 0.2159103 |
| 41 | Cesárea urgente | COVID anteparto asintomática | 1.280 | 0.854 | 1.918000e+00 | 0.2317610 |
| 42 | Cesárea urgente | COVID anteparto leve | 0.904 | 0.661 | 1.237000e+00 | 0.5293250 |
| 43 | Cesárea urgente | COVID anteparto grave | 1.043 | 0.671 | 1.621000e+00 | 0.8507875 |
| 46 | Cesárea urgente | Edad: 31-40 años rel. a 18-30 | 1.755 | 1.341 | 2.298000e+00 | 0.0000420 |
| 47 | Cesárea urgente | Edad: >40 años rel. a 18-30 | 4.998 | 3.119 | 8.008000e+00 | 0.0000000 |
| 48 | Cesárea urgente | Obesidad | 1.935 | 1.443 | 2.595000e+00 | 0.0000104 |
| 49 | Cesárea urgente | DM | 1.522 | 0.708 | 3.272000e+00 | 0.2818955 |
| 50 | Cesárea urgente | EPC | 0.900 | 0.480 | 1.689000e+00 | 0.7436451 |
| 51 | Cesárea urgente | ECC | 1.946 | 0.850 | 4.454000e+00 | 0.1152640 |
| 52 | Cesárea urgente | Nulípara | 2.876 | 2.241 | 3.691000e+00 | 0.0000000 |
| 53 | Cesárea urgente | Gestación múltiple | 2.231 | 0.980 | 5.077000e+00 | 0.0558660 |
| 56 | Cesárea urgente | Feto muerto anteparto | 0.822 | 0.154 | 4.401000e+00 | 0.8192596 |
| 57 | Cesárea urgente | DG | 1.783 | 1.200 | 2.648000e+00 | 0.0041919 |
9.2.13.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=multinom(Tipo.parto~SintPeri+Edad.cut+Obesidad+Diabetes+ECP.Tot+ECC+Nulipara+GM+FMI+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))## # weights: 60 (42 variable)
## initial value 3035.984651
## iter 10 value 2346.626952
## iter 20 value 2134.335469
## iter 30 value 2119.792182
## iter 40 value 2119.479040
## iter 40 value 2119.479024
## iter 40 value 2119.479021
## final value 2119.479021
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Obesidad",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple",
"Feto muerto anteparto",
"DG"
)
colnames(Resultado)=c("Inicio","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Inicio | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 3 | Instrumental | COVID periparto asintomática | 0.783 | 0.566 | 1.083 | 0.1390760 |
| 4 | Instrumental | COVID periparto leve | 0.857 | 0.475 | 1.545 | 0.6083124 |
| 5 | Instrumental | COVID periparto grave | 1.928 | 0.894 | 4.158 | 0.0938913 |
| 8 | Instrumental | Edad: 31-40 años rel. a 18-30 | 1.613 | 1.217 | 2.138 | 0.0008693 |
| 9 | Instrumental | Edad: >40 años rel. a 18-30 | 2.141 | 1.190 | 3.852 | 0.0110892 |
| 10 | Instrumental | Obesidad | 1.198 | 0.828 | 1.733 | 0.3371055 |
| 11 | Instrumental | DM | 2.467 | 0.966 | 6.300 | 0.0591431 |
| 12 | Instrumental | EPC | 0.682 | 0.302 | 1.542 | 0.3582708 |
| 13 | Instrumental | ECC | 2.054 | 0.692 | 6.096 | 0.1945401 |
| 14 | Instrumental | Nulípara | 3.079 | 2.360 | 4.017 | 0.0000000 |
| 15 | Instrumental | Gestación múltiple | 1.118 | 0.361 | 3.467 | 0.8466243 |
| 18 | Instrumental | Feto muerto anteparto | 0.430 | 0.053 | 3.451 | 0.4267465 |
| 19 | Instrumental | DG | 0.785 | 0.451 | 1.368 | 0.3933381 |
| 22 | Cesárea programada | COVID periparto asintomática | 1.159 | 0.737 | 1.822 | 0.5237572 |
| 23 | Cesárea programada | COVID periparto leve | 2.995 | 1.632 | 5.498 | 0.0003993 |
| 24 | Cesárea programada | COVID periparto grave | 10.721 | 5.604 | 20.508 | 0.0000000 |
| 27 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.404 | 0.938 | 2.100 | 0.0987784 |
| 28 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.473 | 1.227 | 4.985 | 0.0113813 |
| 29 | Cesárea programada | Obesidad | 1.673 | 1.070 | 2.616 | 0.0239897 |
| 30 | Cesárea programada | DM | 0.459 | 0.083 | 2.537 | 0.3717254 |
| 31 | Cesárea programada | EPC | 0.381 | 0.089 | 1.623 | 0.1917844 |
| 32 | Cesárea programada | ECC | 3.476 | 1.033 | 11.693 | 0.0441033 |
| 33 | Cesárea programada | Nulípara | 1.105 | 0.752 | 1.624 | 0.6093574 |
| 34 | Cesárea programada | Gestación múltiple | 7.824 | 3.354 | 18.254 | 0.0000019 |
| 37 | Cesárea programada | Feto muerto anteparto | 0.598 | 0.072 | 4.973 | 0.6345937 |
| 38 | Cesárea programada | DG | 1.573 | 0.854 | 2.897 | 0.1460717 |
| 41 | Cesárea urgente | COVID periparto asintomática | 1.164 | 0.874 | 1.550 | 0.2995255 |
| 42 | Cesárea urgente | COVID periparto leve | 1.686 | 1.060 | 2.681 | 0.0272967 |
| 43 | Cesárea urgente | COVID periparto grave | 3.347 | 1.768 | 6.335 | 0.0002067 |
| 46 | Cesárea urgente | Edad: 31-40 años rel. a 18-30 | 1.509 | 1.156 | 1.970 | 0.0024680 |
| 47 | Cesárea urgente | Edad: >40 años rel. a 18-30 | 3.286 | 2.045 | 5.280 | 0.0000009 |
| 48 | Cesárea urgente | Obesidad | 2.031 | 1.505 | 2.739 | 0.0000035 |
| 49 | Cesárea urgente | DM | 1.013 | 0.399 | 2.575 | 0.9781716 |
| 50 | Cesárea urgente | EPC | 1.098 | 0.583 | 2.068 | 0.7722111 |
| 51 | Cesárea urgente | ECC | 3.798 | 1.580 | 9.130 | 0.0028648 |
| 52 | Cesárea urgente | Nulípara | 2.374 | 1.852 | 3.044 | 0.0000000 |
| 53 | Cesárea urgente | Gestación múltiple | 2.082 | 0.867 | 4.996 | 0.1006634 |
| 56 | Cesárea urgente | Feto muerto anteparto | 0.736 | 0.192 | 2.831 | 0.6559552 |
| 57 | Cesárea urgente | DG | 1.896 | 1.273 | 2.824 | 0.0016363 |
9.2.13.6 Ola
mod.COVID=multinom(Tipo.parto~SegundOla+Edad.cut+Obesidad+Diabetes+ECP.Tot+ECC+Nulipara+GM+FMI+Diabetes.Gest,data=DFL.madres,family=binomial(link="logit"))## # weights: 52 (36 variable)
## initial value 4321.079524
## iter 10 value 3158.543109
## iter 20 value 3072.021901
## iter 30 value 3068.054845
## final value 3068.005285
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("2a ola rel a 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Obesidad",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple",
"Feto muerto anteparto",
"DG"
)
colnames(Resultado)=c("Tipo","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 1 | Instrumental | 2a ola rel a 1a ola | 0.755 | 0.596 | 0.958 | 0.0205928 |
| 4 | Instrumental | Edad: 31-40 años rel. a 18-30 | 1.542 | 1.210 | 1.966 | 0.0004666 |
| 5 | Instrumental | Edad: >40 años rel. a 18-30 | 2.168 | 1.312 | 3.582 | 0.0025278 |
| 6 | Instrumental | Obesidad | 1.243 | 0.913 | 1.691 | 0.1664016 |
| 7 | Instrumental | DM | 1.933 | 0.850 | 4.399 | 0.1160883 |
| 8 | Instrumental | EPC | 1.004 | 0.555 | 1.815 | 0.9891699 |
| 9 | Instrumental | ECC | 1.186 | 0.437 | 3.217 | 0.7380699 |
| 10 | Instrumental | Nulípara | 3.312 | 2.630 | 4.170 | 0.0000000 |
| 11 | Instrumental | Gestación múltiple | 1.356 | 0.534 | 3.445 | 0.5220520 |
| 14 | Instrumental | Feto muerto anteparto | 0.278 | 0.036 | 2.141 | 0.2188501 |
| 15 | Instrumental | DG | 0.876 | 0.548 | 1.401 | 0.5815872 |
| 16 | Cesárea programada | 2a ola rel a 1a ola | 0.774 | 0.579 | 1.035 | 0.0843443 |
| 19 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.383 | 1.019 | 1.876 | 0.0373082 |
| 20 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.495 | 1.455 | 4.277 | 0.0008872 |
| 21 | Cesárea programada | Obesidad | 1.815 | 1.302 | 2.529 | 0.0004289 |
| 22 | Cesárea programada | DM | 1.581 | 0.652 | 3.832 | 0.3109995 |
| 23 | Cesárea programada | EPC | 1.177 | 0.608 | 2.278 | 0.6282697 |
| 24 | Cesárea programada | ECC | 1.777 | 0.645 | 4.896 | 0.2664643 |
| 25 | Cesárea programada | Nulípara | 1.091 | 0.809 | 1.469 | 0.5685666 |
| 26 | Cesárea programada | Gestación múltiple | 8.544 | 4.536 | 16.091 | 0.0000000 |
| 29 | Cesárea programada | Feto muerto anteparto | 0.445 | 0.054 | 3.642 | 0.4500561 |
| 30 | Cesárea programada | DG | 1.243 | 0.769 | 2.010 | 0.3740360 |
| 31 | Cesárea urgente | 2a ola rel a 1a ola | 0.955 | 0.773 | 1.179 | 0.6667326 |
| 34 | Cesárea urgente | Edad: 31-40 años rel. a 18-30 | 1.549 | 1.235 | 1.942 | 0.0001496 |
| 35 | Cesárea urgente | Edad: >40 años rel. a 18-30 | 3.970 | 2.679 | 5.882 | 0.0000000 |
| 36 | Cesárea urgente | Obesidad | 1.926 | 1.497 | 2.477 | 0.0000003 |
| 37 | Cesárea urgente | DM | 1.325 | 0.653 | 2.688 | 0.4353597 |
| 38 | Cesárea urgente | EPC | 1.149 | 0.691 | 1.910 | 0.5923620 |
| 39 | Cesárea urgente | ECC | 2.253 | 1.074 | 4.723 | 0.0315572 |
| 40 | Cesárea urgente | Nulípara | 2.584 | 2.093 | 3.190 | 0.0000000 |
| 41 | Cesárea urgente | Gestación múltiple | 2.114 | 1.006 | 4.441 | 0.0481191 |
| 44 | Cesárea urgente | Feto muerto anteparto | 0.831 | 0.264 | 2.614 | 0.7512130 |
| 45 | Cesárea urgente | DG | 1.557 | 1.096 | 2.211 | 0.0133750 |
9.2.13.7 Infección y ola
mod.COVID=multinom(Tipo.parto~SegundOla+Edad.cut+Obesidad+Diabetes+ECP.Tot+ECC+Nulipara+GM+FMI+Diabetes.Gest,data=DFL.madres[DFL.madres$COVID==1,],family=binomial(link="logit"))## # weights: 52 (36 variable)
## initial value 2284.613107
## iter 10 value 1722.188773
## iter 20 value 1654.824730
## iter 30 value 1653.358571
## final value 1653.354673
## convergedDFOR=tbl_regression(mod.COVID, exp = TRUE)
Resultado=data.frame(nivel=DFOR$table_body$groupname_col,
variable=DFOR$table_body$term,
OR=round(DFOR$table_body$estimate,3),
IC1=round(DFOR$table_body$conf.low,3),
IC2=round(DFOR$table_body$conf.high,3),
p.valor=DFOR$table_body$p.value)
Resultado=na.omit(Resultado)
Resultado$variable=c("Infectadas 2a ola rel a infectadas 1a ola",
"Edad: 31-40 años rel. a 18-30",
"Edad: >40 años rel. a 18-30",
"Obesidad",
"DM",
"EPC",
"ECC",
"Nulípara",
"Gestación múltiple",
"Feto muerto anteparto",
"DG"
)
colnames(Resultado)=c("Tipo","Variable","ORa","IC 95%: extremo inf.", "IC 95%: extremo sup.", "p-valor")
na.omit(Resultado) %>%
kbl() %>%
kable_styling()| Tipo | Variable | ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|---|---|
| 1 | Instrumental | Infectadas 2a ola rel a infectadas 1a ola | 0.837 | 0.600 | 1.168 | 0.2962668 |
| 4 | Instrumental | Edad: 31-40 años rel. a 18-30 | 1.481 | 1.038 | 2.114 | 0.0305321 |
| 5 | Instrumental | Edad: >40 años rel. a 18-30 | 1.690 | 0.801 | 3.566 | 0.1682450 |
| 6 | Instrumental | Obesidad | 1.265 | 0.815 | 1.964 | 0.2945879 |
| 7 | Instrumental | DM | 0.780 | 0.166 | 3.672 | 0.7534132 |
| 8 | Instrumental | EPC | 1.561 | 0.730 | 3.339 | 0.2509653 |
| 9 | Instrumental | ECC | 0.523 | 0.066 | 4.170 | 0.5404934 |
| 10 | Instrumental | Nulípara | 3.456 | 2.459 | 4.857 | 0.0000000 |
| 11 | Instrumental | Gestación múltiple | 1.908 | 0.497 | 7.327 | 0.3463870 |
| 14 | Instrumental | Feto muerto anteparto | 0.373 | 0.048 | 2.910 | 0.3466553 |
| 15 | Instrumental | DG | 1.074 | 0.548 | 2.106 | 0.8351115 |
| 16 | Cesárea programada | Infectadas 2a ola rel a infectadas 1a ola | 0.552 | 0.388 | 0.783 | 0.0008801 |
| 19 | Cesárea programada | Edad: 31-40 años rel. a 18-30 | 1.303 | 0.897 | 1.893 | 0.1647626 |
| 20 | Cesárea programada | Edad: >40 años rel. a 18-30 | 2.107 | 1.091 | 4.070 | 0.0264729 |
| 21 | Cesárea programada | Obesidad | 2.202 | 1.488 | 3.260 | 0.0000801 |
| 22 | Cesárea programada | DM | 2.125 | 0.791 | 5.709 | 0.1349542 |
| 23 | Cesárea programada | EPC | 1.508 | 0.705 | 3.226 | 0.2899905 |
| 24 | Cesárea programada | ECC | 1.627 | 0.428 | 6.180 | 0.4747120 |
| 25 | Cesárea programada | Nulípara | 1.058 | 0.727 | 1.540 | 0.7687125 |
| 26 | Cesárea programada | Gestación múltiple | 10.634 | 4.392 | 25.748 | 0.0000002 |
| 29 | Cesárea programada | Feto muerto anteparto | 0.550 | 0.071 | 4.260 | 0.5673070 |
| 30 | Cesárea programada | DG | 1.003 | 0.540 | 1.862 | 0.9931627 |
| 31 | Cesárea urgente | Infectadas 2a ola rel a infectadas 1a ola | 0.963 | 0.730 | 1.271 | 0.7908731 |
| 34 | Cesárea urgente | Edad: 31-40 años rel. a 18-30 | 1.374 | 1.015 | 1.861 | 0.0399112 |
| 35 | Cesárea urgente | Edad: >40 años rel. a 18-30 | 3.581 | 2.155 | 5.952 | 0.0000009 |
| 36 | Cesárea urgente | Obesidad | 1.705 | 1.209 | 2.406 | 0.0023715 |
| 37 | Cesárea urgente | DM | 1.564 | 0.594 | 4.121 | 0.3654267 |
| 38 | Cesárea urgente | EPC | 1.594 | 0.850 | 2.990 | 0.1463625 |
| 39 | Cesárea urgente | ECC | 1.328 | 0.404 | 4.373 | 0.6404327 |
| 40 | Cesárea urgente | Nulípara | 2.465 | 1.852 | 3.281 | 0.0000000 |
| 41 | Cesárea urgente | Gestación múltiple | 2.129 | 0.688 | 6.588 | 0.1899883 |
| 44 | Cesárea urgente | Feto muerto anteparto | 0.730 | 0.202 | 2.641 | 0.6316081 |
| 45 | Cesárea urgente | DG | 0.881 | 0.499 | 1.554 | 0.6607478 |
9.2.14 Bajo peso
9.2.14.1 COVID Sí o No
DFL.niños$PesosP=cut(DFL.niños$Pesos,breaks=c(0,2500,10000),labels=c(1,0))
DFL.niños$PesosP=relevel(DFL.niños$PesosP,"0")mod.COVID=glm(PesosP~COVID+Etnias+Fumadora+Obesidad+Diabetes+GM+Prematuro+AnCon+RetrasoCF+Preeclampsia.grave,
data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 1.458 | 0.999 | 2.129 | 0.05084519 |
| Etnia: Árabe rel. a blanca | 1.347 | 0.717 | 2.530 | 0.35487788 |
| Etnia: Asiática rel. a blanca | 1.465 | 0.485 | 4.428 | 0.49838366 |
| Etnia: Latinoamericana rel. a blanca | 0.987 | 0.616 | 1.580 | 0.95637996 |
| Etnia: Negra rel. a blanca | 1.923 | 0.674 | 5.490 | 0.22179142 |
| Tabaquismo | 1.240 | 0.736 | 2.089 | 0.41880997 |
| Obesidad | 1.567 | 0.987 | 2.486 | 0.05667 |
| DM | 0.591 | 0.173 | 2.022 | 0.4020664 |
| Gestación múltiple | 31.881 | 17.664 | 57.543 | <1e-08 |
| Prematuridad | 57.451 | 38.097 | 86.636 | <1e-08 |
| Anomalías congénitas | 0.885 | 0.242 | 3.234 | 0.85353765 |
| RCIU | 45.258 | 26.110 | 78.448 | <1e-08 |
| PE con CG | 2.887 | 1.050 | 7.941 | 0.04001177 |
9.2.14.2 COVID No, asintomática, leve o grave
mod.COVID=glm(PesosP~Sint+Etnias+Fumadora+Obesidad+Diabetes+GM+Prematuro+AnCon+RetrasoCF+Preeclampsia.grave,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 1.725 | 1.090 | 2.729 | 0.0199289 |
| COVID leve | 1.120 | 0.683 | 1.838 | 0.65322916 |
| COVID grave | 1.648 | 0.892 | 3.042 | 0.11060835 |
| Etnia: Árabe rel. a blanca | 1.314 | 0.697 | 2.476 | 0.39788418 |
| Etnia: Asiática rel. a blanca | 1.399 | 0.460 | 4.253 | 0.55397738 |
| Etnia: Latinoamericana rel. a blanca | 1.002 | 0.620 | 1.619 | 0.9935181 |
| Etnia: Negra rel. a blanca | 1.881 | 0.656 | 5.390 | 0.23948424 |
| Tabaquismo | 1.284 | 0.763 | 2.162 | 0.34715605 |
| Obesidad | 1.551 | 0.976 | 2.466 | 0.0632892 |
| DM | 0.606 | 0.175 | 2.098 | 0.42959883 |
| Gestación múltiple | 32.984 | 18.223 | 59.703 | <1e-08 |
| Prematuridad | 58.051 | 38.330 | 87.919 | <1e-08 |
| Anomalías congénitas | 0.981 | 0.269 | 3.580 | 0.97716234 |
| RCIU | 44.520 | 25.624 | 77.350 | <1e-08 |
| PE con CG | 2.964 | 1.074 | 8.177 | 0.03589775 |
9.2.14.3 COVID ante o periparto contra No
mod.COVID=glm(PesosP~PreP+Etnias+Fumadora+Obesidad+Diabetes+GM+Prematuro+AnCon+RetrasoCF+Preeclampsia.grave,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 1.188 | 0.760 | 1.857 | 0.45060141 |
| COVID periparto | 1.806 | 1.159 | 2.814 | 0.00895675 |
| Etnia: Árabe rel. a blanca | 1.300 | 0.689 | 2.451 | 0.41750403 |
| Etnia: Asiática rel. a blanca | 1.485 | 0.489 | 4.509 | 0.48551285 |
| Etnia: Latinoamericana rel. a blanca | 1.011 | 0.629 | 1.622 | 0.9654769 |
| Etnia: Negra rel. a blanca | 1.948 | 0.682 | 5.563 | 0.21311314 |
| Tabaquismo | 1.232 | 0.730 | 2.081 | 0.43429092 |
| Obesidad | 1.573 | 0.990 | 2.499 | 0.05530243 |
| DM | 0.588 | 0.171 | 2.024 | 0.39963264 |
| Gestación múltiple | 32.380 | 17.932 | 58.469 | <1e-08 |
| Prematuridad | 56.604 | 37.520 | 85.394 | <1e-08 |
| Anomalías congénitas | 0.900 | 0.245 | 3.302 | 0.87344659 |
| RCIU | 46.596 | 26.813 | 80.974 | <1e-08 |
| PE con CG | 2.907 | 1.049 | 8.054 | 0.04013429 |
9.2.14.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(PesosP~SintPre+Etnias+Fumadora+Obesidad+Diabetes+GM+Prematuro+AnCon+RetrasoCF+Preeclampsia.grave,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 1.695 | 0.856 | 3.355 | 0.13016101 |
| COVID anteparto leve | 0.917 | 0.502 | 1.678 | 0.77959121 |
| COVID anteparto grave | 1.486 | 0.700 | 3.154 | 0.30231771 |
| Etnia: Árabe rel. a blanca | 1.403 | 0.632 | 3.115 | 0.40582212 |
| Etnia: Asiática rel. a blanca | 1.613 | 0.463 | 5.626 | 0.452929 |
| Etnia: Latinoamericana rel. a blanca | 0.790 | 0.421 | 1.479 | 0.46055907 |
| Etnia: Negra rel. a blanca | 2.941 | 0.935 | 9.252 | 0.06502524 |
| Tabaquismo | 1.520 | 0.841 | 2.748 | 0.16583158 |
| Obesidad | 1.423 | 0.807 | 2.509 | 0.22337801 |
| DM | 0.461 | 0.094 | 2.252 | 0.33868612 |
| Gestación múltiple | 36.947 | 18.528 | 73.680 | <1e-08 |
| Prematuridad | 55.836 | 33.672 | 92.589 | <1e-08 |
| Anomalías congénitas | 1.757 | 0.346 | 8.931 | 0.49685545 |
| RCIU | 40.793 | 21.420 | 77.686 | <1e-08 |
| PE con CG | 7.431 | 2.080 | 26.544 | 0.00201696 |
9.2.14.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(PesosP~SintPeri+Etnias+Fumadora+Obesidad+Diabetes+GM+Prematuro+AnCon+RetrasoCF+Preeclampsia.grave,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 1.725 | 1.030 | 2.891 | 0.03839375 |
| COVID periparto leve | 1.751 | 0.842 | 3.643 | 0.13361497 |
| COVID periparto grave | 2.381 | 0.955 | 5.940 | 0.06286231 |
| Etnia: Árabe rel. a blanca | 1.216 | 0.573 | 2.580 | 0.61068451 |
| Etnia: Asiática rel. a blanca | 1.462 | 0.407 | 5.247 | 0.56006209 |
| Etnia: Latinoamericana rel. a blanca | 1.498 | 0.828 | 2.709 | 0.18137978 |
| Etnia: Negra rel. a blanca | 1.678 | 0.479 | 5.875 | 0.41828838 |
| Tabaquismo | 1.399 | 0.769 | 2.545 | 0.27157523 |
| Obesidad | 1.406 | 0.799 | 2.473 | 0.23718047 |
| DM | 0.906 | 0.216 | 3.800 | 0.89315312 |
| Gestación múltiple | 40.821 | 20.489 | 81.328 | <1e-08 |
| Prematuridad | 46.759 | 28.477 | 76.779 | <1e-08 |
| Anomalías congénitas | 0.388 | 0.057 | 2.669 | 0.3362254 |
| RCIU | 45.879 | 23.294 | 90.360 | <1e-08 |
| PE con CG | 1.363 | 0.404 | 4.600 | 0.61774387 |
9.2.14.6 Ola
mod.COVID=glm(PesosP~SegundOla+Etnias+Fumadora+Obesidad+Diabetes+GM+Prematuro+AnCon+RetrasoCF+Preeclampsia.grave,
data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel a 1a ola",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel a 1a ola | 1.251 | 0.857 | 1.826 | 0.24667854 |
| Etnia: Árabe rel. a blanca | 1.356 | 0.722 | 2.544 | 0.34334613 |
| Etnia: Asiática rel. a blanca | 1.418 | 0.460 | 4.375 | 0.54345319 |
| Etnia: Latinoamericana rel. a blanca | 1.091 | 0.690 | 1.726 | 0.70929133 |
| Etnia: Negra rel. a blanca | 1.854 | 0.634 | 5.422 | 0.25921958 |
| Tabaquismo | 1.210 | 0.719 | 2.038 | 0.47331111 |
| Obesidad | 1.595 | 1.006 | 2.531 | 0.0472776 |
| DM | 0.597 | 0.174 | 2.047 | 0.41225763 |
| Gestación múltiple | 31.809 | 17.606 | 57.470 | <1e-08 |
| Prematuridad | 59.948 | 39.670 | 90.592 | <1e-08 |
| Anomalías congénitas | 0.956 | 0.264 | 3.466 | 0.94509628 |
| RCIU | 45.588 | 26.305 | 79.006 | <1e-08 |
| PE con CG | 3.026 | 1.100 | 8.327 | 0.03200145 |
9.2.14.7 Infección y ola
mod.COVID=glm(PesosP~SegundOla+Etnias+Fumadora+Obesidad+Diabetes+GM+Prematuro+AnCon+RetrasoCF+Preeclampsia.grave,
data=DFL.niños[DFL.niños$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel a infectadas 1a ola",
"Etnia: Árabe rel. a blanca",
"Etnia: Asiática rel. a blanca",
"Etnia: Latinoamericana rel. a blanca",
"Etnia: Negra rel. a blanca",
"Tabaquismo",
"Obesidad",
"DM",
"Gestación múltiple",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"PE con CG"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel a infectadas 1a ola | 1.252 | 0.757 | 2.072 | 0.38148606 |
| Etnia: Árabe rel. a blanca | 1.153 | 0.497 | 2.677 | 0.73994018 |
| Etnia: Asiática rel. a blanca | 0.919 | 0.164 | 5.139 | 0.92373244 |
| Etnia: Latinoamericana rel. a blanca | 0.806 | 0.461 | 1.411 | 0.45054215 |
| Etnia: Negra rel. a blanca | 0.820 | 0.150 | 4.483 | 0.81867576 |
| Tabaquismo | 0.835 | 0.376 | 1.853 | 0.6579766 |
| Obesidad | 2.118 | 1.170 | 3.832 | 0.01312702 |
| DM | 0.488 | 0.091 | 2.628 | 0.40351945 |
| Gestación múltiple | 18.793 | 7.788 | 45.352 | <1e-08 |
| Prematuridad | 78.698 | 45.509 | 136.091 | <1e-08 |
| Anomalías congénitas | 0.784 | 0.193 | 3.178 | 0.73306154 |
| RCIU | 49.176 | 23.347 | 103.580 | <1e-08 |
| PE con CG | 2.857 | 0.820 | 9.963 | 0.09941098 |
9.2.15 UCI neonatal
9.2.15.1 COVID Sí o No
mod.COVID=glm(UCIN~COVID+PesosP+Prematuro+AnCon+RetrasoCF+GM+APGAR,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Bajo peso",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"Gestación múltiple",
"Apgar≤7"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 3.352 | 2.203 | 5.099 | 2e-08 |
| Bajo peso | 3.466 | 1.986 | 6.048 | 1.213e-05 |
| Prematuridad | 9.536 | 5.741 | 15.839 | <1e-08 |
| Anomalías congénitas | 9.937 | 4.260 | 23.177 | 1.1e-07 |
| RCIU | 1.343 | 0.654 | 2.755 | 0.42183132 |
| Gestación múltiple | 0.743 | 0.375 | 1.471 | 0.39427338 |
| Apgar≤7 | 0.071 | 0.034 | 0.150 | <1e-08 |
9.2.15.2 COVID No, asintomática, leve o grave
mod.COVID=glm(UCIN~Sint+PesosP+Prematuro+AnCon+RetrasoCF+GM+APGAR,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Bajo peso",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"Gestación múltiple",
"Apgar≤7"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 2.551 | 1.537 | 4.234 | 0.00029308 |
| COVID leve | 3.191 | 1.931 | 5.271 | 5.89e-06 |
| COVID grave | 5.964 | 3.402 | 10.455 | <1e-08 |
| Bajo peso | 3.522 | 2.008 | 6.177 | 1.12e-05 |
| Prematuridad | 8.986 | 5.393 | 14.972 | <1e-08 |
| Anomalías congénitas | 10.326 | 4.451 | 23.955 | 5e-08 |
| RCIU | 1.422 | 0.689 | 2.938 | 0.34126722 |
| Gestación múltiple | 0.757 | 0.381 | 1.502 | 0.425127 |
| Apgar≤7 | 0.069 | 0.033 | 0.147 | <1e-08 |
9.2.15.3 COVID ante o periparto contra No
mod.COVID=glm(UCIN~PreP+PesosP+Prematuro+AnCon+RetrasoCF+GM+APGAR,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Bajo peso",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"Gestación múltiple",
"Apgar≤7"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 2.648 | 1.646 | 4.261 | 5.979e-05 |
| COVID periparto | 4.297 | 2.689 | 6.866 | <1e-08 |
| Bajo peso | 3.404 | 1.950 | 5.941 | 1.631e-05 |
| Prematuridad | 9.642 | 5.809 | 16.005 | <1e-08 |
| Anomalías congénitas | 10.180 | 4.329 | 23.940 | 1e-07 |
| RCIU | 1.433 | 0.700 | 2.934 | 0.32492412 |
| Gestación múltiple | 0.790 | 0.398 | 1.566 | 0.49920657 |
| Apgar≤7 | 0.075 | 0.036 | 0.159 | <1e-08 |
9.2.15.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(UCIN~SintPre+PesosP+Prematuro+AnCon+RetrasoCF+GM+APGAR,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Bajo peso",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"Gestación múltiple",
"Apgar≤7"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 1.743 | 0.811 | 3.750 | 0.15490716 |
| COVID anteparto leve | 3.073 | 1.775 | 5.319 | 6.07e-05 |
| COVID anteparto grave | 2.832 | 1.388 | 5.781 | 0.00423834 |
| Bajo peso | 3.191 | 1.534 | 6.636 | 0.0018997 |
| Prematuridad | 10.174 | 5.238 | 19.764 | <1e-08 |
| Anomalías congénitas | 11.162 | 4.121 | 30.231 | 2.08e-06 |
| RCIU | 1.219 | 0.513 | 2.894 | 0.65401394 |
| Gestación múltiple | 0.570 | 0.246 | 1.322 | 0.19009128 |
| Apgar≤7 | 0.097 | 0.037 | 0.255 | 2.34e-06 |
9.2.15.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(UCIN~SintPeri+PesosP+Prematuro+AnCon+RetrasoCF+GM+APGAR,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Bajo peso",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"Gestación múltiple",
"Apgar≤7"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 3.502 | 1.967 | 6.233 | 2.043e-05 |
| COVID periparto leve | 3.479 | 1.609 | 7.520 | 0.001527 |
| COVID periparto grave | 18.843 | 8.679 | 40.913 | <1e-08 |
| Bajo peso | 2.828 | 1.408 | 5.680 | 0.00348059 |
| Prematuridad | 11.901 | 6.346 | 22.317 | <1e-08 |
| Anomalías congénitas | 6.344 | 1.645 | 24.464 | 0.00729326 |
| RCIU | 1.897 | 0.699 | 5.150 | 0.20892049 |
| Gestación múltiple | 1.782 | 0.763 | 4.158 | 0.18179115 |
| Apgar≤7 | 0.053 | 0.022 | 0.127 | <1e-08 |
9.2.15.6 Ola
mod.COVID=glm(UCIN~SegundOla+PesosP+Prematuro+AnCon+RetrasoCF+GM+APGAR,data=DFL.niños,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel a 1a ola",
"Bajo peso",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"Gestación múltiple",
"Apgar≤7"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel a 1a ola | 0.998 | 0.683 | 1.459 | 0.99336874 |
| Bajo peso | 3.688 | 2.146 | 6.336 | 2.3e-06 |
| Prematuridad | 9.926 | 6.070 | 16.231 | <1e-08 |
| Anomalías congénitas | 10.731 | 4.710 | 24.452 | 2e-08 |
| RCIU | 1.332 | 0.659 | 2.693 | 0.42491542 |
| Gestación múltiple | 0.682 | 0.353 | 1.320 | 0.25633301 |
| Apgar≤7 | 0.076 | 0.037 | 0.156 | <1e-08 |
9.2.15.7 Infección y ola
mod.COVID=glm(UCIN~SegundOla+PesosP+Prematuro+AnCon+RetrasoCF+GM+APGAR,data=DFL.niños[DFL.niños$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel infectadas a 1a ola",
"Bajo peso",
"Prematuridad",
"Anomalías congénitas",
"RCIU",
"Gestación múltiple",
"Apgar≤7"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel infectadas a 1a ola | 0.760 | 0.491 | 1.175 | 0.21663953 |
| Bajo peso | 4.207 | 2.197 | 8.057 | 1.462e-05 |
| Prematuridad | 7.981 | 4.347 | 14.652 | <1e-08 |
| Anomalías congénitas | 12.680 | 4.971 | 32.349 | 1.1e-07 |
| RCIU | 1.597 | 0.705 | 3.620 | 0.26186573 |
| Gestación múltiple | 0.488 | 0.209 | 1.140 | 0.09763073 |
| Apgar≤7 | 0.074 | 0.029 | 0.189 | 5e-08 |
9.2.16 Cesáreas en Grupo Robson 2
9.2.16.1 COVID Sí o No
mod.COVID=glm(Cesárea~COVID+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson2,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 1.139 | 0.763 | 1.699 | 0.52486793 |
| Obesidad | 1.490 | 0.913 | 2.432 | 0.11075004 |
| DM | 0.924 | 0.271 | 3.143 | 0.89867004 |
| DG | 1.863 | 0.915 | 3.796 | 0.08652773 |
| EPC | 2.698 | 0.708 | 10.273 | 0.14579977 |
| ECC | 1.591 | 0.348 | 7.271 | 0.5489363 |
| PE con CG | 1.433 | 0.188 | 10.937 | 0.72856667 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.97989554 |
9.2.16.2 COVID No, asintomática, leve o grave
mod.COVID=glm(Cesárea~Sint+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson2,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 1.312 | 0.809 | 2.126 | 0.27089657 |
| COVID leve | 0.863 | 0.495 | 1.506 | 0.60457252 |
| COVID grave | 1.337 | 0.648 | 2.758 | 0.431662 |
| Obesidad | 1.478 | 0.904 | 2.417 | 0.11891477 |
| DM | 0.933 | 0.274 | 3.178 | 0.91157375 |
| DG | 1.943 | 0.951 | 3.972 | 0.06844515 |
| EPC | 2.974 | 0.773 | 11.444 | 0.11284827 |
| ECC | 1.463 | 0.313 | 6.838 | 0.62851762 |
| PE con CG | 1.514 | 0.196 | 11.692 | 0.69061045 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.97983548 |
9.2.16.3 COVID ante o periparto contra No
mod.COVID=glm(Cesárea~PreP+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson2,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 1.100 | 0.684 | 1.768 | 0.69459172 |
| COVID periparto | 1.184 | 0.726 | 1.931 | 0.49958505 |
| Obesidad | 1.494 | 0.915 | 2.440 | 0.10854716 |
| DM | 0.929 | 0.273 | 3.160 | 0.90626578 |
| DG | 1.861 | 0.914 | 3.792 | 0.08705061 |
| EPC | 2.693 | 0.707 | 10.253 | 0.14643979 |
| ECC | 1.573 | 0.344 | 7.197 | 0.55964728 |
| PE con CG | 1.454 | 0.190 | 11.125 | 0.71859434 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.97984301 |
9.2.16.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Cesárea~SintPre+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson2,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 1.506 | 0.745 | 3.042 | 0.25398963 |
| COVID anteparto leve | 0.866 | 0.462 | 1.622 | 0.65228462 |
| COVID anteparto grave | 1.305 | 0.542 | 3.138 | 0.55253064 |
| Obesidad | 1.102 | 0.619 | 1.960 | 0.7410678 |
| DM | 1.147 | 0.305 | 4.309 | 0.83924864 |
| DG | 2.212 | 0.982 | 4.983 | 0.05524725 |
| EPC | 2.549 | 0.495 | 13.130 | 0.2631838 |
| ECC | 0.724 | 0.073 | 7.214 | 0.78291576 |
| PE con CG | 0.825 | 0.068 | 10.079 | 0.88053246 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.9821099 |
9.2.16.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Cesárea~SintPeri+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson2,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 1.245 | 0.709 | 2.185 | 0.44570544 |
| COVID periparto leve | 0.905 | 0.352 | 2.324 | 0.83553615 |
| COVID periparto grave | 1.251 | 0.375 | 4.176 | 0.71534839 |
| Obesidad | 1.705 | 0.944 | 3.078 | 0.07673545 |
| DM | 0.816 | 0.187 | 3.563 | 0.78654571 |
| DG | 2.135 | 0.957 | 4.763 | 0.06379964 |
| EPC | 2.319 | 0.453 | 11.877 | 0.31274154 |
| ECC | 2.195 | 0.414 | 11.625 | 0.3553558 |
| PE con CG | 3411554.799 | 0.000 | Inf | 0.98799212 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.98358057 |
9.2.16.6 Ola
mod.COVID=glm(Cesárea~SegundOla+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson2,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 1.259 | 0.839 | 1.887 | 0.26568262 |
| Obesidad | 1.513 | 0.925 | 2.475 | 0.09876814 |
| DM | 0.900 | 0.264 | 3.064 | 0.86604916 |
| DG | 1.865 | 0.915 | 3.800 | 0.08629801 |
| EPC | 2.785 | 0.729 | 10.636 | 0.13415473 |
| ECC | 1.582 | 0.345 | 7.251 | 0.55505351 |
| PE con CG | 1.471 | 0.196 | 11.024 | 0.7075039 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.97986795 |
9.2.16.7 Infección y ola
mod.COVID=glm(Cesárea~SegundOla+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson2[DFL.madres.Robson2$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 1.208 | 0.702 | 2.079 | 0.49554165 |
| Obesidad | 1.898 | 0.976 | 3.691 | 0.05907048 |
| DM | 0.786 | 0.109 | 5.640 | 0.8104706 |
| DG | 1.292 | 0.414 | 4.035 | 0.65881528 |
| EPC | 4.262 | 0.751 | 24.190 | 0.10169302 |
| ECC | 2.087 | 0.282 | 15.463 | 0.47147275 |
| PE con CG | 1.352 | 0.176 | 10.411 | 0.77226533 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.98686321 |
9.2.17 Cesáreas en Grupo Robson 4
9.2.17.1 COVID Sí o No
mod.COVID=glm(Cesárea~COVID+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson4,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 1.510 | 0.963 | 2.367 | 0.072246 |
| Obesidad | 1.452 | 0.903 | 2.334 | 0.12406084 |
| DM | 1.822 | 0.622 | 5.339 | 0.27399022 |
| DG | 1.351 | 0.724 | 2.519 | 0.34474694 |
| EPC | 1.892 | 0.764 | 4.687 | 0.16831108 |
| ECC | 1.520 | 0.350 | 6.608 | 0.57622776 |
| PE con CG | 2.425 | 0.510 | 11.526 | 0.26550975 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.9834856 |
9.2.17.2 COVID No, asintomática, leve o grave
mod.COVID=glm(Cesárea~Sint+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson4,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 1.243 | 0.700 | 2.207 | 0.45735096 |
| COVID leve | 1.500 | 0.860 | 2.618 | 0.15343017 |
| COVID grave | 2.165 | 1.122 | 4.177 | 0.02123966 |
| Obesidad | 1.403 | 0.869 | 2.264 | 0.16594249 |
| DM | 1.776 | 0.596 | 5.287 | 0.30236161 |
| DG | 1.404 | 0.749 | 2.633 | 0.28984069 |
| EPC | 1.874 | 0.753 | 4.669 | 0.17722179 |
| ECC | 1.517 | 0.342 | 6.727 | 0.5832654 |
| PE con CG | 2.330 | 0.481 | 11.283 | 0.29309097 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.9836685 |
9.2.17.3 COVID ante o periparto contra No
mod.COVID=glm(Cesárea~PreP+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson4,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 1.302 | 0.782 | 2.167 | 0.31021832 |
| COVID periparto | 1.861 | 1.080 | 3.206 | 0.02516681 |
| Obesidad | 1.446 | 0.898 | 2.327 | 0.12898515 |
| DM | 1.948 | 0.661 | 5.742 | 0.2264562 |
| DG | 1.327 | 0.710 | 2.481 | 0.37588146 |
| EPC | 2.043 | 0.817 | 5.111 | 0.12677356 |
| ECC | 1.506 | 0.347 | 6.539 | 0.5849891 |
| PE con CG | 2.444 | 0.516 | 11.573 | 0.26002367 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.98323162 |
9.2.17.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Cesárea~SintPre+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson4,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 1.134 | 0.472 | 2.723 | 0.77859439 |
| COVID anteparto leve | 1.513 | 0.833 | 2.749 | 0.17373945 |
| COVID anteparto grave | 1.167 | 0.505 | 2.695 | 0.71791228 |
| Obesidad | 1.500 | 0.858 | 2.626 | 0.15514465 |
| DM | 2.465 | 0.802 | 7.579 | 0.11530105 |
| DG | 2.046 | 1.038 | 4.034 | 0.03866537 |
| EPC | 1.805 | 0.639 | 5.099 | 0.26483538 |
| ECC | 0.945 | 0.164 | 5.432 | 0.94961693 |
| PE con CG | 2.101 | 0.317 | 13.908 | 0.44126847 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.98864879 |
9.2.17.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Cesárea~SintPeri+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson4,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 1.340 | 0.695 | 2.583 | 0.38239166 |
| COVID periparto leve | 1.749 | 0.593 | 5.155 | 0.31097699 |
| COVID periparto grave | 9.315 | 3.077 | 28.193 | 7.834e-05 |
| Obesidad | 1.374 | 0.743 | 2.541 | 0.3117138 |
| DM | 0.654 | 0.072 | 5.930 | 0.70587689 |
| DG | 1.830 | 0.863 | 3.878 | 0.11483453 |
| EPC | 2.127 | 0.401 | 11.278 | 0.37525491 |
| ECC | 2.829 | 0.607 | 13.194 | 0.18553794 |
| PE con CG | 1.177 | 0.068 | 20.444 | 0.91066656 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.98366716 |
9.2.17.6 Ola
mod.COVID=glm(Cesárea~SegundOla+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson4,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 0.658 | 0.416 | 1.043 | 0.07467762 |
| Obesidad | 1.449 | 0.901 | 2.330 | 0.12611101 |
| DM | 2.044 | 0.701 | 5.965 | 0.19065125 |
| DG | 1.311 | 0.705 | 2.439 | 0.39262461 |
| EPC | 2.177 | 0.883 | 5.367 | 0.09110833 |
| ECC | 1.233 | 0.289 | 5.255 | 0.77688451 |
| PE con CG | 2.563 | 0.538 | 12.210 | 0.23719629 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.98334231 |
9.2.17.7 Infección y ola
mod.COVID=glm(Cesárea~SegundOla+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson4[DFL.madres.Robson4$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"infectadas 2a ola rel. a infectadas 1a ola",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| infectadas 2a ola rel. a infectadas 1a ola | 0.665 | 0.387 | 1.142 | 0.13923177 |
| Obesidad | 1.232 | 0.664 | 2.286 | 0.5079757 |
| DM | 3.053 | 0.824 | 11.302 | 0.09473418 |
| DG | 0.497 | 0.172 | 1.432 | 0.19513678 |
| EPC | 1.974 | 0.732 | 5.326 | 0.17934241 |
| ECC | 0.858 | 0.060 | 12.209 | 0.91014268 |
| PE con CG | 2.907 | 0.515 | 16.402 | 0.2266827 |
| Feto muerto anteparto | 0.000 | 0.000 | Inf | 0.98793088 |
9.2.18 Cesáreas en Grupo Robson 10
9.2.18.1 COVID Sí o No
mod.COVID=glm(Cesárea~COVID+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson10,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID | 1.963 | 1.038 | 3.713 | 0.03810572 |
| Obesidad | 1.228 | 0.561 | 2.686 | 0.60734443 |
| DM | 1.957 | 0.232 | 16.540 | 0.53744057 |
| DG | 0.806 | 0.275 | 2.359 | 0.69355027 |
| EPC | 0.599 | 0.127 | 2.821 | 0.51701234 |
| ECC | 18667781.652 | 0.000 | Inf | 0.98756702 |
| PE con CG | 5.341 | 1.417 | 20.141 | 0.01335176 |
| Feto muerto anteparto | 0.886 | 0.199 | 3.937 | 0.87365005 |
9.2.18.2 COVID No, asintomática, leve o grave
mod.COVID=glm(Cesárea~Sint+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson10,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID asintomática",
"COVID leve",
"COVID grave",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID asintomática | 1.033 | 0.449 | 2.380 | 0.9384837 |
| COVID leve | 1.620 | 0.705 | 3.720 | 0.25563923 |
| COVID grave | 4.436 | 1.945 | 10.113 | 0.00039598 |
| Obesidad | 1.217 | 0.543 | 2.723 | 0.63360401 |
| DM | 1.984 | 0.223 | 17.659 | 0.5388954 |
| DG | 0.757 | 0.251 | 2.284 | 0.62109633 |
| EPC | 0.535 | 0.108 | 2.644 | 0.44316947 |
| ECC | 22454092.697 | 0.000 | Inf | 0.98730694 |
| PE con CG | 5.454 | 1.401 | 21.226 | 0.01441857 |
| Feto muerto anteparto | 0.983 | 0.210 | 4.601 | 0.98228128 |
9.2.18.3 COVID ante o periparto contra No
mod.COVID=glm(Cesárea~PreP+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson10,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto",
"COVID periparto",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto | 2.968 | 1.434 | 6.140 | 0.00336511 |
| COVID periparto | 1.270 | 0.605 | 2.667 | 0.52795689 |
| Obesidad | 1.244 | 0.563 | 2.749 | 0.58935298 |
| DM | 2.028 | 0.238 | 17.273 | 0.51771533 |
| DG | 0.777 | 0.261 | 2.312 | 0.65023454 |
| EPC | 0.516 | 0.110 | 2.422 | 0.40160244 |
| ECC | 18534725.324 | 0.000 | Inf | 0.98731042 |
| PE con CG | 6.155 | 1.588 | 23.854 | 0.00856253 |
| Feto muerto anteparto | 0.947 | 0.206 | 4.344 | 0.94411674 |
9.2.18.4 COVID anteparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Cesárea~SintPre+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson10,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID anteparto asintomática",
"COVID anteparto leve",
"COVID anteparto grave",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID anteparto asintomática | 2.966 | 0.978 | 8.994 | 0.05467613 |
| COVID anteparto leve | 1.615 | 0.542 | 4.814 | 0.39004349 |
| COVID anteparto grave | 6.039 | 2.191 | 16.648 | 0.00050888 |
| Obesidad | 0.925 | 0.331 | 2.588 | 0.88199759 |
| DM | 2.506 | 0.151 | 41.491 | 0.5211252 |
| DG | 1.359 | 0.386 | 4.791 | 0.63287773 |
| EPC | 0.615 | 0.082 | 4.623 | 0.63655876 |
| ECC | 18115086.728 | 0.000 | Inf | 0.98755645 |
| PE con CG | 9.027 | 0.970 | 84.038 | 0.05325616 |
| Feto muerto anteparto | 0.552 | 0.048 | 6.286 | 0.6321433 |
9.2.18.5 COVID periparto asintomática, leve o grave contra No COVID
mod.COVID=glm(Cesárea~SintPeri+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson10,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"COVID periparto asintomática",
"COVID periparto leve",
"COVID periparto grave",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| COVID periparto asintomática | 4.55000e-01 | 0.143 | 1.444 | 0.18142109 |
| COVID periparto leve | 1.78300e+00 | 0.621 | 5.122 | 0.2824256 |
| COVID periparto grave | 2.95900e+00 | 0.953 | 9.192 | 0.06061348 |
| Obesidad | 1.31400e+00 | 0.472 | 3.652 | 0.60112014 |
| DM | 1.49500e+00 | 0.109 | 20.506 | 0.76343249 |
| DG | 1.00300e+00 | 0.286 | 3.517 | 0.99627265 |
| EPC | 2.19000e-01 | 0.010 | 4.842 | 0.33652974 |
| ECC | 4.07096e+07 | 0.000 | Inf | 0.98728991 |
| PE con CG | 1.03590e+01 | 1.712 | 62.671 | 0.01091164 |
| Feto muerto anteparto | 1.66900e+00 | 0.220 | 12.683 | 0.6203804 |
9.2.18.6 Ola
mod.COVID=glm(Cesárea~SegundOla+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson10,family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"2a ola rel. a 1a ola",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| 2a ola rel. a 1a ola | 0.914 | 0.494 | 1.693 | 0.77589044 |
| Obesidad | 1.283 | 0.591 | 2.783 | 0.52889591 |
| DM | 2.023 | 0.242 | 16.936 | 0.51589538 |
| DG | 0.692 | 0.242 | 1.982 | 0.49331056 |
| EPC | 0.747 | 0.158 | 3.533 | 0.71339658 |
| ECC | 11575867.977 | 0.000 | Inf | 0.98787245 |
| PE con CG | 5.843 | 1.564 | 21.838 | 0.008679 |
| Feto muerto anteparto | 1.037 | 0.236 | 4.562 | 0.96209729 |
9.2.18.7 Infección y ola
mod.COVID=glm(Cesárea~SegundOla+Obesidad+Diabetes+Diabetes.Gest+ECP.Tot+ECC+Preeclampsia.grave+FMI,
data=DFL.madres.Robson10[DFL.madres.Robson10$COVID==1,],family=binomial(link="logit"))
DFOR=or_model_summary(round_p=8,model = mod.COVID)
rownames(DFOR)=c("Término Indep",
"Infectadas 2a ola rel. a infectadas 1a ola",
"Obesidad",
"DM",
"DG",
"EPC",
"ECC",
"PE con CG",
"Feto muerto anteparto"
)
colnames(DFOR)=Columnes.OR
DFOR[-1,] %>%
kbl() %>%
kable_styling()| ORa | IC 95%: extremo inf. | IC 95%: extremo sup. | p-valor | |
|---|---|---|---|---|
| Infectadas 2a ola rel. a infectadas 1a ola | 0.883 | 0.426 | 1.829 | 0.73686395 |
| Obesidad | 1.604 | 0.614 | 4.188 | 0.33487354 |
| DM | 2.654 | 0.117 | 60.123 | 0.53986683 |
| DG | 0.242 | 0.043 | 1.364 | 0.10789403 |
| EPC | 0.586 | 0.125 | 2.753 | 0.49860328 |
| ECC | 1523130.616 | 0.000 | Inf | 0.98713283 |
| PE con CG | 3.441 | 0.869 | 13.633 | 0.07847789 |
| Feto muerto anteparto | 1.186 | 0.238 | 5.897 | 0.83507185 |