Browsing by Author "Pe'er, Itsik"

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    A Comprehensive and Bias-Free Machine Learning Approach for Risk Prediction of Preeclampsia with Severe Features in a Nulliparous Study Cohort
    (Research Square, 2023-04-10) Lin, Yun; Mallia, Daniel; Clark-Sevilla, Andrea; Catto, Adam; Leshchenko, Alisa; Yan, Qi; Haas, David; Wapner, Ronald; Pe'er, Itsik; Raja, Anita; Salleb-Aouissi, Ansaf; Obstetrics and Gynecology, School of Medicine
    Objective: Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed machine learning models that predict the onset of preeclampsia with severe features or eclampsia at discrete time points in a nulliparous pregnant study cohort. Materials and methods: The prospective study cohort to which we applied machine learning is the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b) study, which contains information from eight clinical sites across the US. Maternal serum samples were collected for 1,857 individuals between the first and second trimesters. These patients with serum samples collected are selected as the final cohort. Results: Our prediction models achieved an AUROC of 0.72 (95% CI, 0.69-0.76), 0.75 (95% CI, 0.71-0.79), and 0.77 (95% CI, 0.74-0.80), respectively, for the three visits. Our initial models were biased toward non-Hispanic black participants with a high predictive equality ratio of 1.31. We corrected this bias and reduced this ratio to 1.14. The top features stress the importance of using several tests, particularly for biomarkers and ultrasound measurements. Placental analytes were strong predictors for screening for the early onset of preeclampsia with severe features in the first two trimesters. Conclusion: Experiments suggest that it is possible to create racial bias-free early screening models to predict the patients at risk of developing preeclampsia with severe features or eclampsia nulliparous pregnant study cohort.
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    Genetic Risk and First-Trimester Cardiovascular Health Predict Hypertensive Disorders of Pregnancy in Nulliparous Women
    (Elsevier, 2025) Mathew, Vineetha; Khan, Raiyan R.; Jowell, Amanda R.; Yan, Qi; Pe'er, Itsik; Truong, Buu; Natarajan, Pradeep; Yee, Lynn M.; Khan, Sadiya S.; Sharma, Garima; Patel, Aniruddh P.; Cho, So Mi Jemma; Pabon, Maria A.; McNeil, Rebecca B.; Spencer, Jillyn; Silver, Robert M.; Levine, Lisa D.; Grobman, William A.; Catov, Janet M.; Haas, David M.; Honigberg, Michael C.; Obstetrics and Gynecology, School of Medicine
    Background: Hypertensive disorders of pregnancy (HDPs) (preeclampsia/eclampsia and gestational hypertension) are a leading cause of maternal and perinatal morbidity and mortality and are associated with long-term maternal cardiovascular disease. High genetic risk and poor cardiovascular health (CVH) are each associated with HDPs, but whether genetic risk for HDP is modified by CVH status in early pregnancy is unknown. Objectives: In this study, the authors sought to test the independent and joint associations of genetic risk and first-trimester CVH with development of HDP. Methods: We examined genotyped participants from the nuMoM2b (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be), a prospective observational cohort that enrolled nulliparous individuals with singleton pregnancies from 2010 to 2013 at 8 U.S. clinical sites. Genetic risk was calculated according to a validated genetic risk score for HDP. A first-trimester CVH score was closely adapted from the American Heart Association Life's Essential 8 model. Genetic risk and CVH were each categorized as low (bottom quintile), intermediate (quintile 2-4), or high (top quintile). The primary outcome was development of HDP. Multivariable-adjusted logistic regression was used to test the independent and joint associations of genetic risk and CVH with development of HDPs. Results: Among 7,499 participants (mean age 27.0 years), the median first-trimester CVH score was 77.1 (Q1-Q3: 67.1-85.7). Overall, 1,032 participants (13.8%) developed an HDP (487 [6.5%] preeclampsia, 545 [7.3%] gestational hypertension). Genetic risk and CVH were each independently and additively associated with HDP (high vs low genetic risk: adjusted OR [aOR]: 2.21 [95% CI: 1.78-2.77; P < 0.001]; low vs high CVH: aOR: 2.92 [95% CI: 2.28-3.74; P < 0.001]). There was no significant interaction between genetic risk and CVH regarding risk of HDPs (Pinteraction > 0.05). HDP incidence ranged from 4.5% (low genetic risk, high CVH) to 25.7% (high genetic risk, low CVH). Compared with low CVH, high CVH was associated with 53%-74% lower risk of HDP across genetic risk strata. Findings were consistent when examining preeclampsia/eclampsia and gestational hypertension separately. Conclusions: Lower genetic risk and higher first-trimester CVH were independently and additively associated with lower risk of developing HDPs in nulliparous individuals. Favorable CVH in early pregnancy may mitigate high genetic risk for HDP.