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.

Keywords:

• perinatal pathology
• time of death
• stillbirth/intrauterine fetal demise
• unsupervised machine learning

Acknowledgements

This manuscript is based on material previously presented at the Society for Pediatric Pathology Fall 2022 Meeting, held on October 7–9, 2022, in Rochester, NY.

Author contributions

TEKC contributed to data analysis, data interpretation, literature search, generation of figures, writing of manuscript. GRS, RMS, UMR, and DJD contributed to study design, data interpretation. HP contributed to study design, data interpretation, writing of manuscript.

Disclosure statement

The authors have no financial or other conflicts of interest to disclose.

Additional information

Funding

This work was supported by grant funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development: U10-HD045953 Brown University, Rhode Island; U10-HD045925 Emory University, Georgia; U10-HD045952 University of Texas Medical Branch at Galveston, Texas; U10-HDO45955 University of Texas Health Sciences Center at San Antonio, Texas; U10-HD045944 University of Utah Health Sciences Center, Utah; and U01-HD045954 RTI International, RTP. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.