Development and external validation of a model for predicting adverse outcomes in women with preeclampsia: A retrospective study from two trans-regional centers in China.

Abstract

Preeclampsia is a common complication of pregnancy that causes health problems for both the mother and her fetus. This study aimed to develop and externally validate a model to predict adverse outcomes in preeclampsia in a trans-regional two-center retrospective cohort of Chinese women. To generate a model for the risk of women with adverse outcomes, we incorporated candidate variables in the development set in univariate, least absolute shrinkage and selection operator analysis and multivariable logistic regression. The performance of the model was evaluated for the receiver operating characteristic (ROC) curve, calibration and decision curve analysis. Further, we externally validated the model in an independent dataset. Composite adverse outcomes within 48h of admission. There were 1 783 and 116 preeclampsia women in the development and validation set, respectively. The model included 10 predictors: gestational age at admission, irregular prenatal care, number of symptoms, mean arterial pressure, hematocrit, platelet count, fibrinogen, albumin, total bilirubin, and serum urea. The area under the ROC curve of the model was 0.867 in the development set and 0.841 in the external validation set. The calibration plots for the probability of adverse outcomes demonstrated a good correlation. Decision curve analysis further showed that our model had clinical application value. The nomogram and a software-based calculator (https://sdfyyfck.shinyapps.io/preeclampsia/) were constructed for convenient clinical use. Such a model could be used as a useful tool for the assessment of hypertensive-related complications in Chinese preeclampsia patients.