Key result
ICD-10 coding for MVP shows poor diagnostic accuracy, yielding only ~34% positive predictive value vs. echocardiography.
Why the study?
Accurate validation of ICD-10 diagnosis coding for mitral valve prolapse is needed to ensure reliable conclusions from electronic health record and administrative claims data.
Does administrative ICD-10 coding accurately identify patients with mitral valve prolapse compared to echocardiographic evidence?
Cross-Sectional (n=9,304)
No
Does administrative ICD-10 coding accurately identify patients with mitral valve prolapse compared to echocardiographic evidence?
Administrative ICD-10 coding for mitral valve prolapse suffers from low sensitivity and positive predictive value while introducing non-random phenotypic bias, indicating that researchers using EHR data must incorporate echocardiographic data for valid cohort generation.
Avoid sole reliance on ICD-10 codes for mitral valve prolapse; leaves open validity of prior EHR-based MVP studies without echo confirmation.
Objectives: Electronic health records and administrative claims are increasingly utilized for conducting observational studies, pragmatic clinical trials, and training machine learning models. For accurate and reliable conclusions, input data requires validation. We sought to evaluate International Classification of Diseases, Tenth Revision (ICD-10) diagnosis coding for Mitral valve prolapse (MVP). Methods: We compared ICD-10 diagnosis coding for MVP with echocardiographic evidence of MVP in a large academic center over the years 2018 - 2024. Results: Among 5,520 patients with an MVP ICD-10 code (I34.1), 1,883 had echocardiographic confirmation (positive predictive value 34.1%, 95% CI 32.9% - 35.4%). Among 5,667 patients with echocardiographic confirmed MVP, 3,784 lacked the corresponding ICD-10 code (sensitivity was 33.2%, 95% CI 32.0 % - 34.5%). In sub-analysis with granular covariates (N = 1,183), false-positive coding was associated with higher prevalence of atrial fibrillation, heart failure, and coronary artery disease than patients with confirmed MVP in univariate analysis. In multivariable analysis, heart failure was associated with increased odds of false-positive ICD coding (aOR 1.77, 95% CI 1.08 - 2.89; p = 0.02). Conclusions: These findings suggest that administrative MVP codes do not accurately identify patients with the disease and appear to introduce non-random phenotype bias with implications for EHR research and machine learning.
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Shah et al. (2026) conducted a cross-sectional in Mitral valve prolapse (n=9,304). ICD-10 diagnosis code for mitral valve prolapse (I34.1) vs. Echocardiographic evidence of mitral valve prolapse was evaluated on Positive predictive value of ICD-10 code for MVP (95% CI 32.9% - 35.4%). Administrative ICD-10 coding for mitral valve prolapse had low positive predictive value (34.1%) and sensitivity (33.2%) compared to echocardiography, introducing non-random phenotype bias.
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