Objective: To develop and validate a prediction model for identifying pregnant women at risk of developing pregnancy-induced hypertension (PIH) to guide treatment decision and classification of management. Methods: This study retrospectively enrolled 907 consecutive pregnant women with de novo hypertension from the Antenatal Care Center of Henan Provincial People's Hospital between June 1, 2018 and May 31, 2019. The cohort was randomly divided into two subgroups: the development cohort (n = 635) and validation cohort (n = 272). Univariate analysis and backward elimination of multivariate logistic regression analyses were utilized to identify predictive factors, and a nomogram was established. The performance was assessed using the area under the curve (AUC), the mean AUC of k-fold cross-validation, and calibration plots. Based on the classification and regression tree model, risk classification was performed. Results: The score included five commonly available predictors: body mass index, proteinuria, age, uric acid, and mean arterial pressure (BPAUM score). When applied to internal validation, the score revealed good discrimination with stratified fivefold cross-validation in the development cohort (AUC = 0.91) and validation cohort (AUC: 0.89) at fixed 10% false-positive rates, and the calibration plots showed good calibration. The total score point was divided into three risk classifications: low risk (0 - 179 points), medium risk (179 - 204 points), and high risk (>204 points). Conclusions: This study established a prediction model for predicting PIH, which could be used in clinical decision-making to improve maternal health and birth outcomes.
Keywords: nomograms; prediction model; pregnancy-induced hypertension (PIH); risk classification.