Developing algorithms for identifying major structural birth defects using automated electronic health data

Purpose Given the 2015 transition to International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) diagnostic coding, updates to our previously published algorithms for major structural birth defects (BDs) were necessary. Aims of this study were to update, validate, and refine algorithms for identifying selected BDs, and then to use these algorithms to describe BD prevalence in the vaccine safety datalink (VSD) population. Methods We converted our ICD‐9‐CM list of selected BDs to ICD‐10‐CM using available crosswalks with manual review of codes. We identified, chart reviewed, and adjudicated a sample of infants in the VSD with ≥2 ICD‐10‐CM diagnoses for one of seven common BDs. Positive predictive values (PPVs) were calculated; for BDs with suboptimal PPV, algorithms were refined. Final automated algorithms were applied to a cohort of live births delivered 10/1/2015–9/30/2017 at eight VSD sites to estimate BD prevalence. This research was approved by the HealthPartners Institutional Review Board, by all participating VSD sites, and by the CDC, with a waiver of informed consent. Results Of 573 infants with ≥2 diagnoses for a targeted BD, on adjudication, we classified 399 (69.6%) as probable cases, 31 (5.4%) as possible cases and 143 (25.0%) as not having the targeted BD. PPVs for the final BD algorithms ranged from 0.76 (hypospadias) to 1.0 (gastroschisis). Among 212 857 births over 2 years following transition to ICD‐10‐CM coding, prevalence for the full list of selected defects in the VSD was 1.8%. Conclusions Algorithms can identify infants with selected BDs using automated healthcare data with reasonable accuracy. Our updated algorithms can be used in observational studies of maternal vaccine safety and may be adapted for use in other surveillance systems.