Back to Search Start Over

Development and Validation of an Algorithm to Predict Stillbirth Gestational Age in Medicaid Billing Records.

Authors :
Thai TN
Smolinski NE
Nduaguba S
Bird S
Straub L
Bateman BT
Hernandez-Diaz S
Huybrechts KF
Rasmussen SA
Winterstein AG
Source :
American journal of epidemiology [Am J Epidemiol] 2024 Sep 20. Date of Electronic Publication: 2024 Sep 20.
Publication Year :
2024
Publisher :
Ahead of Print

Abstract

Introduction: With Medicaid covering half of US pregnancies, Medicaid Analytic eXtract (MAX) provides a valuable data source to enrich understanding about stillbirth etiologies.<br />Objective: We developed and validated a claims-based algorithm to predict GA at stillbirth.<br />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.<br />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.<br />Discussion: Our prediction algorithm for stillbirths can facilitate pregnancy research in the Medicaid population.<br /> (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Details

Language :
English
ISSN :
1476-6256
Database :
MEDLINE
Journal :
American journal of epidemiology
Publication Type :
Academic Journal
Accession number :
39307537
Full Text :
https://doi.org/10.1093/aje/kwae369