Assessing Intrauterine Retention according to Microscopic Stillbirth Features: A Cluster Analysis Approach.

Abstract

Background: Previous studies identified microscopic changes associated with intrauterine retention of stillbirths based on clinical time of death. The objective of this study was to utilize unsupervised machine learning (not reliant on subjective measures) to identify features associated with time from death to delivery. Methods: Data were derived from the Stillbirth Collaborative Research Network. Features were chosen a priori for entry into hierarchical cluster analysis, including fetal and placental changes. Results: A four-cluster solution (coefficient = 0.983) correlated with relative time periods of "no retention," "mild retention," "moderate retention," and "severe retention." Loss of nuclear basophilia within fetal organs were found at varying rates among these clusters. Conclusions: Hierarchical cluster analysis is able to classify stillbirths based on histopathological changes, roughly correlating to length of intrauterine retention. Such clusters, which rely solely on objective fetal and placental findings, can help clinicians more accurately assess the interval from death to delivery.