JMIR Publications

ABSTRACT

Background:

Preeclampsia is a potentially fatal complication during pregnancy, characterized by high blood pressure and the presence of excessive proteins in the urine. Due to its complexity, the prediction of preeclampsia onset is often difficult and inaccurate.

Objective:

This study aims to create quantitative models to predict the onset gestational age of preeclampsia using electronic health records.

Methods:

We retrospectively collected 1178 preeclamptic pregnancy records from the University of Michigan Health System as the discovery cohort, and 881 records from the University of Florida Health System as the validation cohort. We constructed two Cox-proportional hazards models: one baseline model utilizing maternal and pregnancy characteristics, and the other full model with additional labs, vitals, and medications. We built the models using 80% of the discovery data and tested the remaining 20% of discovery data and validated with the University of Florida data. We further stratified the patients into high and low-risk groups for preeclampsia onset risk assessment.

Results:

The baseline model reached C-indices of 0.64 and 0.61 in the 20% testing data and the validation data, respectively, while the full model increased these C-indices to 0.69 and 0.61 respectively. Both models contain five selective features, among which the number of fetuses in the pregnancy, hypertension, and parity are shared between the two models with similar hazard ratios and significant p-values. In the full model, maximum diastolic blood pressure in early pregnancy was the predominant feature.

Conclusions:

Electronic health record data provide useful information to predict the gestational age of preeclampsia onset. Stratification of the cohorts using five-predictor Cox-proportional hazards models provide clinicians with convenient tools to assess the patients’ onset time of preeclampsia.