Key result
EHR phenotyping algorithm achieves ~94% precision for classifying single ventricle physiology, outperforming prior methods.
Why the study?
Congenital heart disease patients with single ventricle physiology have heterogeneous characteristics that challenge accurate cohort classification using electronic health records.
Does a novel EHR-based phenotyping algorithm improve the identification of single ventricle physiology in congenital heart disease patients compared to a published algorithm?
Observational (n=26,020)
Does a novel EHR-based phenotyping algorithm improve the identification of single ventricle physiology in congenital heart disease patients compared to a published algorithm?
An automated EHR phenotyping algorithm incorporating clinical notes and imaging reports significantly outperforms existing methods for identifying patients with single ventricle physiology.
Answers are grounded in this paper and cite their sources.
May aid EHR phenotyping of single-ventricle patients; leaves open external validation before clinical use.
Objectives: Congenital heart disease (CHD) patients with single ventricle physiology (SVP) have heterogeneous characteristics that challenge cohort classification. We aim to develop a phenotyping algorithm that accurately identifies SVP patients using electronic health record (EHR) data. Materials and Methods: We used ICD-9 and ICD-10 codes for initial classification, then enhanced the algorithm with domain expertise, imaging reports, and progress notes. The algorithm was developed using a cohort of 1020 patients who underwent magnetic resonance imaging scans and tested in a separate cohort of 2500 CHD patients with adjudication. Validation was performed in a holdout group of 22 500 CHD patients. We evaluated performance using accuracy, sensitivity, precision, and F1 score, and compared it to a published algorithm for SVP using the same dataset. Results: In the 2500-testing cohort, our algorithm based on specialty-defined features and International Classification of Diseases (ICD) codes achieved 99.24% accuracy, 94.12% precision, 85.11% sensitivity, and 89.39% F1 score. In contrast, the published method achieved 95.20% accuracy, 43.23% precision, 88.30% sensitivity, and 58.04% F1 score. In the 22 500-validation cohort, our algorithm achieved 93.82% precision, while the published method achieved 43.00%. Discussion and Conclusions: Our automated phenotype algorithm, combined with physician adjudication, outperforms a published method for SVP classification. It effectively identifies false positives by cross-referencing clinical notes and detects missed SVP cases that were due to absent or erroneous ICD codes. Our integrated phenotyping algorithm showed excellent performance and has the potential to improve research and clinical care of SVP patients through the automated development of an electronic cohort for prognostication, monitoring, and management.
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Xu et al. (2025) conducted an observational in Congenital heart disease with single ventricle physiology (n=26,020). EHR-based phenotyping algorithm vs. Published algorithm was evaluated on Precision for single ventricle physiology classification in the validation cohort. An EHR-based phenotyping algorithm achieved 93.82% precision for classifying single ventricle physiology in a 22,500-patient validation cohort, outperforming a published method (43.00%).
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