Visualizing risk modification of hypertensive disorders of pregnancy: development and validation of prediction model for personalized interpregnancy weight management.

Abstract

The growing recognition of the importance of interpregnancy weight management in reducing hypertensive disorders of pregnancy (HDP) underscores the importance of effective preventive strategies. However, developing effective systems remains a challenge. We aimed to bridge this gap by constructing a prediction model. This study retrospectively analyzed the data of 1746 women who underwent two childbirths across 14 medical facilities, including both tertiary and primary facilities. Data from 2009 to 2019 were used to create a derivation cohort (n = 1746). A separate temporal-validation cohort was constructed by adding data between 2020 and 2024 (n = 365). Furthermore, the external-validation cohort was constructed using the data from another tertiary center between 2017 and 2023 (n = 340). We constructed a prediction model for HDP development in the second pregnancy by applying logistic regression analysis using 5 primary clinical information: maternal age, pre-pregnancy body mass index, and HDP history; and pregnancy interval and weight change velocity between pregnancies. Model performance was assessed across all three cohorts. HDP in the second pregnancy occurred 7.3% in the derivation, 10.1% in the temporal-validation, and 7.9% in the external-validation cohorts. This model demonstrated strong discrimination, with c-statistics of 0.86, 0.88, and 0.86 for the respective cohorts. Precision-recall area under the curve values were 0.90, 0.85, and 0.91, respectively. Calibration showed favorable intercepts (-0.02 to -0.00) and slopes (0.96-1.02) for all cohorts. In conclusion, this externally validated model offers a robust basis for personalized interpregnancy weight management goals for women planning future pregnancies.