INTRODUCTION
With Medicaid covering half of US pregnancies, Medicaid Analytic eXtract (MAX) provides a valuable data source to enrich understanding about stillbirth etiologies.
OBJECTIVE
We developed and validated a claims-based algorithm to predict GA at stillbirth.
METHOD
We linked the stillbirths identified in MAX 1999-2013 to Florida Fetal Death Records (FDRs) to obtain clinical estimates of GA (N=825). We tested several algorithms including using a fixed median GA, median GA at the time of specific prenatal screening tests, and expanded versions considering additional predictors of stillbirth within including linear regression and random forest models. We estimated the proportion of pregnancies with differences of ± 1, 2, 3 and 4 weeks between the predicted and FDR GA and the model mean square error (MSE). We validated the selected algorithms in two external samples.
RESULTS
The best performing algorithm was a random forest model (MSE of 12.67 weeks2) with 84% of GAs within ± 4 weeks. Assigning a fixed GA of 28 weeks resulted in an MSE of 60.21 weeks2 and proportions of GA within ± 4 weeks of 32%. We observed consistent results in the external samples.
DISCUSSION
Our prediction algorithm for stillbirths can facilitate pregnancy research in the Medicaid population.