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Abstract #127954 Published in IGR 25-3

Development and Evaluation of a Computable Phenotype for Normal Tension Glaucoma

PURPOSE: To develop a computable phenotype for normal tension glaucoma (NTG) to enhance disease identification from electronic health records (EHRs). DESIGN: Retrospective cohort study. SUBJECTS: Deidentified EHR data from an academic medical center identified 1851 patients aged ≥40 years, with glaucoma and available clinical notes. METHODS: Of these 1851 patients, 200 were randomly selected for a chart review to receive gold standard diagnoses. Four rule-based NTG computable phenotypes were developed and tested. Phenotype 1 relied on NTG International Classification of Diseases (ICD)-9 and ICD-10 codes. Phenotype 2 incorporated structured intraocular pressure (IOP) data and medication lists. Phenotype 3 used only structured IOP data. Phenotype 4 combined structured IOP and medication data natural language processing (NLP) to extract IOP values and NTG mentions from chart notes. Internal and external validation were performed. MAIN OUTCOME MEASURES: F1 score, sensitivities, specificities, positive predictive value (PPV), negative predictive value (NPV), and accuracy. RESULTS: Chart review identified NTG in 30% of patients, and only 7% had NTG ICD codes. Phenotype 1 had an F1 of 36.8%, sensitivity 24.1%, specificity 97%, PPV 77.8%, NPV 74.9%, and accuracy 75.1%. Compared with ICD codes, phenotypes 2 and 3 had F1 of 66.7% and 69.8%, sensitivity 77.6% and 89.7%, specificity 76.3% and 71.1%, PPV 58.4% and 57.1%, NPV 88.8% and 94.1%, and accuracy of 76.7% and 76.7%, respectively. Incorporating NLP, phenotype 4 had the best performance with an F1 of 77.4%, sensitivity 82.8%, specificity 86.7%, PPV 72.7%, NPV 92.1%, and accuracy 85.5%. Phenotypes 2 to 4 increase NTG case detection fourfold compared with phenotype 1. CONCLUSIONS: Normal tension glaucoma phenotypes using NLP achieved the best overall performance, and those incorporating structured data perform better than ICD codes alone. The NTG ICD code-based phenotype is highly specific but lacks sensitivity. Insights from this study may inform the development of computable phenotypes for other disease subtypes within broader disease categories. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon.

Classification:

15 Miscellaneous

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