Abstract

Introduction: The objective determination of a person’s cardiorespiratory fitness over time, especially in those with heart failure, can play a critical role in optimizing individual therapies, identifying early signs of decompensation, and accelerate the development of novel therapeutic interventions. However, existing methods, such as formal cardiopulmonary exercise testing (CPET), or surrogates like a 6-minute walk test (6MWT) are difficult for both patients and health systems and provide only sparse samples. Hypothesis: Using a wearable ECG-patch sensor and machine learning methods, cardiorespiratory fitness can be estimated daily using routine vital sign dynamics during free-living activities. Methods: CPETs and multiple days of ECG-patch sensor data were collected within 2 weeks of each other for 228 participants (146 normal, and 82 with Heart Failure). A model for VO 2 max estimation was developed using a two-stage procedure. (Figure) In the first stage, the model was pretrained on an unsupervised regression task on over 2000 unique patients’ data, representing over 200 patient-years of activity of daily living. The second stage fine-tuned the pretrained model to predict VO 2 max, using a K-Fold cross validation procedure with 50% train, 25% validation, and 25% testing for four folds. Results: CPET-determined VO 2 max values ranged from 7.1 to 69.1 ml/kg/min (median=28.9, IQR=24.5). The Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE) of the estimated VO 2 max was 0.85 and 6.2 ml/kg/min, respectively. For the heart failure subgroup, the PCC and MAE are 0.73 and 3.7 ml/kg/min, respectively. (Figure) Conclusion: Our results show that estimated VO 2 max can be accurately and conveniently determined at high frequency in individuals with a wide range of cardiorespiratory fitness using data from a patch sensor during routine daily activities. Ongoing work in a large HF trial will evaluate its performance over time relative to 6MWT and surveys.

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