Development and Validation of Multi-Stage Prediction Models for Pre-eclampsia: A Retrospective Cohort Study on Chinese Women

Abstract

Objective: The aim of this study is to develop multistage prediction models for pre-eclampsia (PE) covering almost the entire pregnancy period based on routine antenatal measurements and to propose a risk screening strategy.

Methods: This was a retrospective cohort study that included 20582 singleton pregnant women with the last menstruation between January 1, 2013 and December 31, 2019. Of the 20582 women, 717 (3.48%) developed pre-eclampsia, including 46 (0.22%) with early-onset pre-eclampsia and 119 (0.58%) preterm pre-eclampsia. We randomly divided the dataset into the training set (N = 15665), the testing set (N = 3917), and the validation set (N = 1000). Least Absolute Shrinkage And Selection Operator (LASSO) was used to do variable selection from demographic characteristics, blood pressure, blood routine examination and biochemical tests. Logistic regression was used to develop prediction models at eight periods: 5-10 weeks, 11-13 weeks, 14-18 weeks, 19-23 weeks, 24-27 weeks, 28-31 weeks, 32-35 weeks, and 36-39 weeks of gestation. We calculated the AUROC (Area Under the Receiver Operating Characteristic Curve) on the test set and validated the screening strategy on the validation set.

Results: We found that uric acid tested from 5-10 weeks of gestation, platelets tested at 18-23 and 24-31 weeks of gestation, and alkaline phosphatase tested at 28-31, 32-35 and 36-39 weeks of gestation can further improve the prediction performance of models. The AUROC of the optimal prediction models on the test set gradually increased from 0.71 at 5-10 weeks to 0.80 at 24-27 weeks, and then gradually increased to 0.95 at 36-39 weeks of gestation. At sensitivity level of 0.98, our screening strategy can identify about 94.8% of women who will develop pre-eclampsia and reduce about 40% of the healthy women to be screened by 28-31 weeks of pregnancy.

Conclusion: We developed multistage prediction models and a risk screening strategy, biomarkers of which were part of routine test items and did not need extra costs. The prediction window has been advanced to 5-10 weeks, which has allowed time for aspirin intervention and other means for PE high-risk groups.

Keywords: LASSO; logistic regression; multi-stage prediction model; pre-eclampsia; pregnancy; screening strategy.