Interpretable framework for predicting preoperative cardiorespiratory fitness using wearable data

Abstract

Objectives

Predicting 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.

Methods

We 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.

Results

Adults with higher CRF exhibited elevated levels of moderate-to-vigorous physical activity (MVPA), maximal activity energy expenditure (aEE_max), heart rate recovery (HRR), and non-linear heart rate variability (HRV). These measures increased concurrently with improvements in 6MWT outcomes. Our regression models, employing random forest and linear regression techniques, demonstrated strong predictive capabilities, with coefficient of determination values of 0.91 and 0.81, respectively, for estimating CRF. The shapley feature attribution approach elucidated those greater levels of MVPA, aEE_max, HRR, and nonlinear dynamics of HRV serve as reliable indicators of enhanced CRF test performance.

Conclusion

The 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.