Development and Validation of Gestational Age Estimation Algorithms for Nonlive Births in Administrative Healthcare Databases

BACKGROUND: Algorithms to estimate gestational age (GA) for nonlive births have been developed in other administrative healthcare databases, but their applicability in Korea remains unknown. We adapted algorithms developed in the United States and evaluated their validity in Korean healthcare claims data. METHODS: Using the National Health Information Database of South Korea, we linked GA information on influenza vaccination from the national vaccination registry to establish a reference standard. Nonlive births were stratified into spontaneous/induced abortions and stillbirths. Four algorithms were tested: (1) assigning outcome-specific GA, (2) adjusting GA based on ultrasound scan records, (3) a regression model using gestational markers as predictors, and (4) a random forest model. Algorithms were evaluated by the proportion of estimates within 1-4 weeks of the reference standard and the mean squared error (MSE). External validation was conducted using an independent dataset. RESULTS: Random forests performed best for both spontaneous/induced abortions (MSE 1.68 weeks 2 ) and stillbirths (MSE 0.97 weeks 2 ), with 92.6% (95% confidence interval 91.6, 93.4) and 97.4% (96.2, 98.3) of predictions falling within 2 weeks of the reference standard, respectively. However, in the external validation set, the ultrasound record-based adjustment approach performed similarly to the random forest approach for both spontaneous/induced abortions (MSE 8.37 vs. 8.15 weeks 2 ) and stillbirths (MSE 12.42 vs. 12.52 weeks 2 ). CONCLUSIONS: Deterministic approaches may be preferable for estimating GA of nonlive births in the National Health Information Database, as they are simpler to implement and perform comparably to model-based algorithms. These algorithms can support pregnancy research in the Korean population.