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CME Credit Offered
CME Credit Offered
Abstract Award
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Poster Session 3

(821) A comparative study of stillbirth classification schemes

Friday, January 31, 2025
10:30 AM – 12:30 PM  
Objective: Stillbirth, defined as fetal death at ≥20 weeks’ gestation, complicates ~1 in 160 pregnancies and causes vary widely. The most helpful tests in determining cause of stillbirth are fetal autopsy, placental pathology, and genetic testing, yet uptake is often low or incomplete. Several methodologies exist to classify stillbirth into phenotypic groups based on pathologic and clinical findings, each varying in complexity and ease of use. Our objective was to determine the agreement between three published classification systems (PASS, INCODE, and ReCoDe) using a cohort of patients who had complete diagnostic evaluation after stillbirth.

Study Design: Retrospective cohort study was conducted examining stillbirths occurring at two tertiary care centers from 2016 to 2022 with completed genetic and pathologic evaluation (n=85). Three independent reviewers examined the cases according to selected classification schemes.  Descriptive statistics were calculated and reliability analysis using Fleiss’ kappa was performed to determine agreement.

Results:

Leading cause of stillbirth among the classification schemes was placental in origin followed by fetal (µ=48%, µ=33%) (Table 1). Each scheme had low rate of unclassified cases (µ=3%). Agreement between the three classification schemes was fair (κ=0.38, CI 0.30-0.46; p< 0.001). 26% had positive genetic findings on diagnostic testing. Sub-analysis of cases with a genetic diagnosis did not demonstrate increased agreement (κ=0.33, CI 0.14-0.53; p< 0.001).

Conclusion:

Findings demonstrate tested classification schemes perform similarly on most stillbirth cases regarding minimization of unclassified diagnoses, but incompletely agree on all classifications. This highlights the diagnostic imprecision of stillbirth. These classification systems are limited by the user’s interpretation of findings as well as failure to account for pathophysiologic interactions in cases affected by multiple clinical, genetic, or pathologic factors. Future work should be aimed at using tools such as machine-learning to develop classification algorithms to address these limitations.