ArticleLearning health systems2025
Development and validation of a computable phenotype for adolescent idiopathic scoliosis.
Article in Learning health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Development and validation of a computable phenotype for adolescent idiopathic scoliosis.Learning health systems · 2025Article
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7 authors.
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Abstract
Introduction: There remains a lack of understanding of the etiology and treatment effectiveness for Adolescent idiopathic scoliosis (AIS). The objective of this study was to develop and validate a computable phenotype for patients with AIS to facilitate rapid learning through large-scale observational research using real-world data. Study Design: Four computable phenotype (CP) algorithms were developed and tested. The algorithms were executed against the Shriners Children's (SC) Research Data Warehouse using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from January 1, 2016 to December 31, 2022. CPs composed of diagnosis and imaging procedure utilization codes were evaluated iteratively against a prospective registry of scoliosis patients. The highest-performing phenotype was then evaluated through manual chart review for validation. Demographic characteristics of the patients meeting the phenotype definition were assessed. Results: The four alternative CPs ranged from 24 103 to 15 292 unique patients. The CP that balanced sensitivity (92.7%) and specificity (81.8%) when evaluated against a prospective registry of scoliosis patients was chosen as the final AIS CP. Among 50 patients with phenotype-confirmed AIS, 36 (72%) had chart-validated AIS, and 14 (28%) were identified as false positives. Of the 14 false positives, 6 cases had a diagnosis of spinal asymmetry. Among the patients meeting the phenotype definition, the average age of patients with AIS treated at SC is 13.6 years (SD = 1.64) and patients are primarily female (73.7%) and white (56.2%). Conclusion: The CP had good performance in identifying pediatric patients with AIS. Future refinements to the algorithm should include the use of x-ray parameters or the application of natural language processing to unstructured EHR data to better distinguish AIS cases from other spinal diagnoses. This CP is a fundamental step to facilitate a learning health system environment that can rapidly develop evidence to improve pediatric patient outcomes.
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