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

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 patients with HF (New York Heart Association, class I-III) wearing Everion multisensor smartwatches: "Evaluating Mobile Health Tool Use for Capturing Patient-centered Outcomes Measures in Patients with HF" (NCT04191356). Then, Least Absolute Shrinkage and Selection Operator 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 7235 candidate features, Least Absolute Shrinkage and Selection Operator retained 88 predictors (Supplemental Table 1, available online at https://www.mcpdigitalhealth.org/), including the top predictors: passive functional capacity metrics (positive), daily steps/walking time (positive), nocturnal respiratory variability (negative), and patient-reported dyspnea (negative). Binary classification models achieved a test of area under the curve=0.77 (sensitivity, 91%; specificity, 63%), and linear mixed-effects models produced a root mean squared error of 14.7 (R