Validation of an ICD-9-Based Algorithm to Identify Stillbirth Episodes from Medicaid Claims Data

Sabina O. Nduaguba, Nicole E. Smolinski, Thuy N. Thai, Steven T. Bird, Sonja A. Rasmussen and Almut G. Winterstein ()
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Sabina O. Nduaguba: West Virginia University
Nicole E. Smolinski: University of Florida
Thuy N. Thai: University of Florida
Steven T. Bird: Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration
Sonja A. Rasmussen: University of Florida
Almut G. Winterstein: University of Florida

Drug Safety, 2023, vol. 46, issue 5, No 4, 457-465

Abstract: Abstract Introduction In administrative data, accurate timing of exposure relative to gestation is critical for determining the effect of potential teratogen exposure on pregnancy outcomes. Objective To develop an algorithm for identifying stillbirth episodes in the ICD-9-CM era using national Medicaid claims data (1999–2014). Methods Unique stillbirth episodes were identified from clusters of medical claims using a hierarchy that identified the encounter with the highest potential of including the actual stillbirth delivery and that delineated subsequent pregnancy episodes. Each episode was validated using clinical detail on retrieved medical records as the gold standard. Results Among 220 retrieved records, 197 were usable for validation of 1417 stillbirth episodes identified by the algorithm. The positive predictive value (PPV) was 64.0% (57.3–70.7%) overall, 80.4% (73.8–87.1%) for inpatient episodes, 28.2% (14.1–42.3%) for outpatient-only episodes, and 20.0% (2.5–37.5%) for outpatient episodes with overlapping hospitalizations. The absolute difference between the dates of the algorithm-specified stillbirth delivery and the medical record-based event was 4.2 ± 24.3 days overall, 1.7 ± 7.7 days for inpatient episodes, 14.3 ± 51.4 days for outpatient-only episodes, and 1.0 ± 2.0 days for outpatient episodes that overlapped with a hospitalization. Excluding all outpatient episodes, as well as pregnancies involving multiple births, the PPV increased to 82.7% (76.8–89.8%). Conclusions Our algorithm to identify stillbirths from administrative claims data had a moderately high PPV. Positive predictive value was substantially increased by restricting the setting to inpatient episodes and using only input diagnostic codes for singleton stillbirths.

Date: 2023
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DOI: 10.1007/s40264-023-01287-3

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