Tetsuya Kawakita, Philip Greenland, Victoria L Pemberton, William A Grobman, Robert M Silver, C Noel Bairey Merz, Rebecca B McNeil, David M Haas, Uma M Reddy, Hyagriv Simhan, George R Saade
BACKGROUND: The prevalence of metabolic syndrome is rapidly increasing in the United States. We hypothesized that prediction models using data obtained during pregnancy can accurately predict the future development of metabolic syndrome. OBJECTIVE: To develop machine-learning models to predict the development of metabolic syndrome using factors ascertained in nulliparous pregnant individuals. STUDY DESIGN: This was a secondary analysis of a prospective cohort study (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be Heart Health Study [nuMoM2b HHS])...
March 23, 2024: American Journal of Obstetrics and Gynecology