Objective: We sought to create a machine learning (ML) model to identify variables that would aid in the prediction of surgical morbidity in cases of placenta accreta spectrum (PAS).
Study Design: A multicenter analysis including all cases of PAS identified by pathology specimen confirmation, across five tertiary care perinatal centers in New York City from 2013 to 2022. We developed models to predict operative morbidity using 213 variables including demographics, obstetrical information, and limited prenatal imaging findings detailing placental location.
Background: Biologically active cervical glands provide a mucous barrier while influencing the composition and biomechanical strength of the cervical extracellular matrix. Cervical remodeling during ripening may be reflected as loss of the sonographic cervical gland area. As sonographic cervical length remains suboptimal for universal screening, adjunctive evaluation of other facets of the mid-trimester cervix may impart additional screening benefit.
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