Background: The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a validated patient-reported outcome measure for heart failure. However, its clinical utility is limited by incomplete and inconsistent data collection. We aimed to develop and validate machine learning models to estimate KCCQ overall summary scores from electronic health record (EHR) data. Methods: We assembled a retrospective cohort of 10,889 heart failure patients with recorded KCCQ scores from the Truveta database. Predictor features were derived from structured EHR variables across 13 historical time windows (15-360 days). Multiple regression algorithms were evaluated, followed by SHapley Additive exPlanations (SHAP)-based feature reduction and nested cross-validation for hyperparameter optimization. Model performance was assessed using the coefficient of determination (R2), mean absolute error (MAE), and ordinal discrimination and calibration for categorical severity classification. Results: Histogram-based gradient boosting (HGB) with HGB-SHAP feature selection achieved the strongest performance, reducing feature dimensionality by more than 94\% while maintaining estimation accuracy. The 240-day window performed best (R2=0.522, MAE=12.485). For categorical severity classification, the model demonstrated strong ordinal discrimination (mean ordinal AUROC=0.850). Quantile-based calibration improved classification balance, increasing the F1-score for the most severe category (KCCQ<25) from 0.180 to 0.428 and the quadratic weighted kappa from 0.601 to 0.640. Longer EHR observation windows were associated with improved prediction performance. Conclusion: Machine learning models can estimate KCCQ scores from routine EHR data with clinically meaningful accuracy and strong discriminatory performance. This approach may help extend assessment of patient-reported health status to populations in which survey-based data are incompletely captured, supporting population-level cardiovascular outcomes assessment and risk stratification in heart failure care.

The authors have declared no competing interest.

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This work was funded by Truveta Inc.

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This study used fully de-identified data and did not involve interaction with human subjects. Therefore, it was determined to be exempt from Institutional Review Board (IRB) review in accordance with applicable regulations.

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The data used in this study are available to Truveta subscribers through Truveta Studio (studio.truveta.com). To support reproducibility while protecting data privacy, the code supporting the findings of this study will be made publicly available at https://github.com/TruvetaResearchPublic/kccq_score_estimation upon publication, along with synthetic (pseudo) inputs and documentation to reproduce the analyses. Because the shared inputs are synthetic, the reproduced numerical results may differ from those reported here, but the analytical workflow and overall approach will be the same.

https://github.com/TruvetaResearchPublic/kccq_score_estimation