The clinical value of nomogram combined with machine learning models in predicting the progression of hypertensive disorders in pregnancy to severe preeclampsia

This study demonstrates that a family history of hypertension, urine protein, umbilical artery S/D ratio, WBC, TBIL, UA, LDL, TG, CRP, and blood Ca are predictors of HDP progression to SPE. A nomogram model for predicting the progression of HDP to SPE was constructed using these predictors. The model exhibited good discrimination, calibration, and clinical utility in both the training and validation cohorts. Additionally, a ML model was developed, with the XGBoost model identified as the optimal one, which can be applied clinically in conjunction with the nomogram prediction model.