Internal and external validation of a machine learning algorithm to detect preeclampsia-related adverse outcomes in high-risk pregnancies.

OBJECTIVES

This study aimed to refine an existing machine learning (ML) algorithm for predicting preeclampsia-related adverse outcomes and to assess its generalizability and predictive performance through internal validation in a German cohort and external validation in a North American cohort.

STUDY

DESIGN

A retrospective analysis was conducted using data from two cohorts: a cohort of 1,634 pregnant women in Germany and a prospective study cohort of 946 in North America, all presenting with clinical suspicion of preeclampsia.

MAIN OUTCOME MEASURES: Gradient-boosted trees and logistic regression were used to predict (1) any adverse maternal or fetal outcome, (2) delivery within 14 days before 34 + 0 weeks, and (3) delivery within 7 days after 34 + 0 weeks. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).

RESULTS

Despite notable differences in baseline characteristics between cohorts, the refined model demonstrated strong and consistent predictive performance. For predicting any adverse outcome, AUROCs were 0.92 (95% CI: 0.87-0.96) in the German cohort and 0.87 (95% CI: 0.82-0.91) in the North American cohort. For delivery within 14 days before 34 + 0 weeks, AUROCs were 0.92 and 0.88, respectively. For delivery within 7 days after 34 + 0 weeks, AUROCs were 0.79 and 0.78.

CONCLUSIONS

The refined ML model maintained high predictive accuracy across two distinct populations, demonstrating its generalizability and potential for integration into clinical decision-making. These findings support the use of machine learning in enhancing the prediction of preeclampsia-related adverse outcomes and improving maternal and neonatal care.