| Summary: | Effectively identifying high-risk patients with de novo hypertensive disorder of pregnancy (HDP) is required to enable timely intervention and to reduce adverse maternal and perinatal outcomes. Electronic medical record of pregnant women with de novo HDP were extracted from a birth cohort in Beijing, China. The adverse outcomes included maternal and fetal morbidities, mortality, or any other adverse complications. A multitude of machine learning statistical methods were employed to develop two prediction models, one for maternal complications and the other for perinatal deteriorations. The maternal model using the random forest algorithm produced an AUC of 0.984 (95% CI (0.978, 0.991)). The strongest predictors variables selected by the model were platelet count, fetal head/abdominal circumference ratio, and gestational age at the diagnosis of de novo HDP; The perinatal model using the boosted tree algorithm yielded an AUC of 0.925 (95% CI (0.907, 0.945]). The strongest predictor variables chosen were gestational age at the diagnosis of de novo HDP, fetal femur length, and fetal head/abdominal circumference ratio. These prediction models can help identify de novo HDP patients at increased risk of complications who might need intense maternal or perinatal care.
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