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Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation

Hailey K Ballard1,2, Xiaotong Yang1, Aditya D Mahadevan3,4

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States.

Journal of Medical Internet Research
|August 14, 2024
PubMed
Summary

Predicting preeclampsia onset is now more accurate using electronic health records. Quantitative models incorporating maternal characteristics and lab data improve prediction of gestational age for preeclampsia.

Keywords:
EHRelectronic health recordshealth recordsmachine learningmaternalmortalitypreeclampsiapregnancyprognosisriskrisk predictionsurvivalsurvival analysis

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Area of Science:

  • Obstetrics and Gynecology
  • Medical Informatics
  • Predictive Analytics

Background:

  • Preeclampsia is a dangerous pregnancy complication characterized by high blood pressure and proteinuria.
  • Accurate prediction of preeclampsia onset remains a significant clinical challenge.

Purpose of the Study:

  • To develop quantitative models for predicting preeclampsia onset gestational age.
  • Utilize electronic health records (EHRs) for predictive modeling.

Main Methods:

  • Retrospective collection of 1178 (discovery) and 881 (validation) preeclamptic pregnancy records.
  • Construction of two Cox-proportional hazards models: a baseline model and a full model including lab findings, vitals, and medications.
  • Model development using 80% of discovery data, testing on 20%, and validation with external data.

Main Results:

  • The full model demonstrated improved predictive performance (Concordance index of 0.69) compared to the baseline model (0.64) in discovery data.
  • Area Under the Curve (AUC) values for predicting preeclampsia at 34 and 37 weeks were 0.70 for the full model.
  • Key predictors included number of fetuses, hypertension, parity, and early pregnancy diastolic blood pressure.

Conclusions:

  • EHR data are valuable for predicting preeclampsia gestational age.
  • Cox-proportional hazards models with 5 predictors offer practical tools for clinicians to assess preeclampsia onset risk.