Using machine learning to predict the risk of developing hypertensive disorders of pregnancy using a contemporary nulliparous cohort

Jonathan S Schor; Adesh Kadambi; Isabel Fulcher; Kartik K Venkatesh; Mark A Clapp; Senan Ebrahim; Ali Ebrahim; Timothy Wen

doi:10.1016/j.xagr.2024.100386

Using machine learning to predict the risk of developing hypertensive disorders of pregnancy using a contemporary nulliparous cohort

AJOG Glob Rep. 2024 Aug 22;4(4):100386. doi: 10.1016/j.xagr.2024.100386. eCollection 2024 Nov.

Authors

Jonathan S Schor^{1

2}, Adesh Kadambi^{1

3}, Isabel Fulcher^{1

4}, Kartik K Venkatesh^{1

5}, Mark A Clapp¹, Senan Ebrahim¹, Ali Ebrahim¹, Timothy Wen^{1

6}

Affiliations

¹ Delfina Care Inc, San Francisco, CA, USA (Schor, Kadambi, Fulcher, Venkatesh, Clapp, Ebrahim, Ebrahim and Wen).
² University of California, San Francisco (UCSF) Medical Scientist Training Program, San Francisco, CA, USA (Schor).
³ Department of Biomedical Engineering, University of Toronto, Toronto, Canada (Kadambi).
⁴ Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA (Fulcher).
⁵ Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Ohio State University Medical Center, Columbus, OH, USA (Venkatesh).
⁶ Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco (UCSF), San Francisco, CA USA (Wen).

Abstract

Background: Hypertensive disorders of pregnancy (HDP) are significant drivers of maternal and neonatal morbidity and mortality. Current management strategies include early identification and initiation of risk mitigating interventions facilitated by a rules-based checklist. Advanced analytic techniques, such as machine learning, can potentially offer improved and refined predictive capabilities.

Objective: To develop and internally validate a machine learning prediction model for hypertensive disorders of pregnancy (HDP) when initiating prenatal care.

Study design: We developed a prediction model using data from the prospective multisite cohort Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) among low-risk individuals without a prior history of aspirin utilization for preeclampsia prevention. The primary outcome was the development of HDP. Random forest modeling was utilized to develop predictive models. Recursive feature elimination (RFE) was employed to create a reduced model for each outcome. Area under the curve (AUC), 95% confidence intervals (CI), and calibration curves were utilized to assess discrimination and accuracy. Sensitivity analyses were conducted to compare the sensitivity and specificity of the reduced model compared to existing risk factor-based algorithms.

Results: Of 9,124 assessed low risk nulliparous individuals, 21% (n=1,927) developed HDP. The prediction model for HDP had satisfactory discrimination with an AUC of 0.73 (95% CI: 0.70, 0.75). After RFE, a parsimonious reduced model with 30 features was created with an AUC of 0.71 (95% CI: 0.68, 0.74). Variables included in the model after RFE included body mass index at the first study visit, pre-pregnancy weight, first trimester complete blood count results, and maximum systolic blood pressure at the first visit. Calibration curves for all models revealed relatively stable agreement between predicted and observed probabilities. Sensitivity analysis noted superior sensitivity (AUC 0.80 vs 0.65) and specificity (0.65 vs 0.53) of the model compared to traditional risk factor-based algorithms.

Conclusion: In cohort of low-risk nulliparous pregnant individuals, a prediction model may accurately predict HDP diagnosis at the time of initiating prenatal care and aid employment of close interval monitoring and prophylactic measures earlier in pregnancy.

Keywords: Hypertensive disorders of pregnancy; Machine learning; Risk prediction.