Interpretable framework for predicting preoperative cardiorespiratory fitness using wearable data.

objectivesPredicting preoperative cardiorespiratory fitness (CRF) is crucial for assessing the risk of complications and adverse outcomes in patients undergoing surgery. CRF is formally evaluated through submaximal exercise testing with cardiopulmonary exercise testing (CPET) or the 6-minute walk test (6MWT). However, formal CRF testing is impractical as a preoperative screening tool. Wrist-worn devices with actigraphy and heart rate monitoring have become increasingly capable of predicting physiological measurements. Our aim was to develop a clinically interpretable machine learning (ML) model using wearable-derived physiological data to predict CRF for older adults, and to access whether this model can accurately estimate the 6MWT distances for preoperative risk evaluation.

methodsWe examined heart rate and activity data collected from Fitbit devices worn by older adults (N = 65) who were scheduled to undergo major noncardiac surgery. Data collection took place over a 1-week period prior to surgery while participants engaged in their typical daily activities. Our primary aim was to leverage this wearable technology to forecast CRF among this group. We employed a machine-learning ensemble regression model to predict CRF, using 6MWT outcomes as an index. Further, we applied the shapley feature attribution approach to gain insights into how specific features derived from wearable data contribute to CRF prediction within the model, aiding in personalized fitness prediction.

resultsAdults with higher CRF exhibited elevated levels of moderate-to-vigorous physical activity (MVPA), maximal activity energy expenditure (aEE

conclusionThe integration of wearable data-driven activity and heart rate metrics forms the basis for utilizing wearables to provide preoperative cardiorespiratory fitness assessments, supporting surgical risk stratification, personalized prehabilitation, and improved patient outcomes.