Abstract
INTRODUCTION: Previous studies have identified lesions commonly found in placentas associated with stillbirth but have not distinguished across a range of gestational ages (GAs). The objective of this study was to identify lesions associated with stillbirths at different GAs by adapting methods from the chemical machine learning field to assign lesion importance based on correlation with GA. METHODS: Placentas from the Stillbirth Collaborative Research Network were examined according to standard protocols. GAs at stillbirth were categorized as: <28 weeks (extreme preterm stillbirth [PTSB]), 28-33'6 weeks (early PTSB), 34-36'6 weeks (late PTSB), ≥37 weeks (term stillbirth). We identified and ranked the most discriminating placental features, as well as those that were similar across GA ranges, using Kernel Principal Covariates Regression (KPCovR). RESULTS: These analyses included 210 (47.2%) extreme PTSB, 85 (19.1%) early PTSB, 62 (13.9%) late PTSB, and 88 (19.8%) term stillbirths. When we compute the KPCovR, the first principal covariate indicates that there are four lesions (acute funisitis & nucleated fetal red blood cells found in extreme PTSB; multifocal reactive amniocytes & multifocal meconium found in term stillbirth) that distinguish GA ranges among all stillbirths. DISCUSSION: There are distinct placental lesions present across GA ranges in stillbirths; these lesions are identifiable using sophisticated feature selection. Further investigation may identify histologic changes across gestations that relate to fetal mortality.