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Estimating Heart Failure Health Status From Multimodal Wearables: Implementation of a Feature-Engineering Framework for KCCQ-12 Prediction

Year: 2026

Type: article

Abstract: Routine monitoring of symptom burden and quality of life in heart failure (HF) patients is limited by intermittent sampling of validated instruments, such as the Kansas City Cardiomyopathy Questionnaire (KCCQ-12). As more wearable devices track physiological measures in real-world settings than ever before, we propose an exploratory implementation of a feature-engineering framework to bridge this sampling gap, approximating KCCQ-12 scores and patient health status from wearable features. In this narrative, we processed data from the 8-week multicenter observational study of HF patients (NYHA class I-III) wearing Everion® multisensor smartwatches: "Evaluating Mobile Health Tool Use for Capturing Patient-centered Outcomes Measures in HF Patients" (NCT04191356). Then, LASSO logistic regression was applied for feature selection, followed by binary classification and linear mixed-effects modeling to predict KCCQ-12 scores. Among patients generating sufficient data (n = 59), there were 173 unique KCCQ-12 observations. From 7,235 candidate features, LASSO retained 88 predictors (Supplementary Table 1), including the top predictors: p6MWD (positive), daily steps/walking time (positive), nocturnal respiratory variability (negative), and patient-reported dyspnea (negative). Binary classification models achieved test AUC=0.77 (sensitivity 91%, specificity 63%) and linear mixed-effects models produced a RMSE of 14.7 (R 2 = 0.58) for predicting patient health status and continuous KCCQ-12 scores, respectively. A patient-level split analysis revealed decreased model performance (RMSE = 18.9; R 2 =0.33), suggesting limited generalizability to new individuals. This exploratory implementation with small sample size outlines wearable features that warrant future investigation for KCCQ-12 score monitoring and offers early-stage lessons for assessing such features as proxies of KCCQ-12 scores in real-world settings.

Source: Mayo Clinic Proceedings Digital Health

DOI: 10.1016/j.mcpdig.2026.100403