Hussain Iqram, Zeepvat Julianna, Reid M Cary, Czaja Sara, Pryor Kane O, Boyer Richard
Department of Anesthesiology, Weill Cornell Medicine, New York, NY 10065, United States.
Division of Geriatrics and Palliative Medicine, Weill Cornell Medicine, New York, NY 10065, United States.
Comput Methods Programs Biomed. 2025 Nov;271:108980. doi: 10.1016/j.cmpb.2025.108980. Epub 2025 Jul 21.
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.
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.
Adults with higher CRF exhibited elevated levels of moderate-to-vigorous physical activity (MVPA), maximal activity energy expenditure (aEE), 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, HRR, and nonlinear dynamics of HRV serve as reliable indicators of enhanced CRF test performance.
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.
预测术前心肺适能（CRF）对于评估手术患者的并发症风险和不良结局至关重要。CRF通过次极量运动测试结合心肺运动测试（CPET）或6分钟步行测试（6MWT）进行正式评估。然而，正式的CRF测试作为术前筛查工具并不实用。具有活动记录和心率监测功能的腕戴式设备越来越能够预测生理指标。我们的目的是使用可穿戴设备获取的生理数据开发一种具有临床可解释性的机器学习（ML）模型，以预测老年人的CRF，并评估该模型是否能够准确估计用于术前风险评估的6MWT距离。
我们检查了计划进行重大非心脏手术的老年人（N = 65）佩戴Fitbit设备收集的心率和活动数据。数据收集在手术前1周内进行，期间参与者进行其典型的日常活动。我们的主要目的是利用这种可穿戴技术预测该组人群的CRF。我们采用机器学习集成回归模型以6MWT结果为指标预测CRF。此外，我们应用夏普利特征归因方法来深入了解可穿戴数据中的特定特征如何在模型中对CRF预测做出贡献，有助于个性化的适能预测。
CRF较高的成年人表现出中等到剧烈身体活动（MVPA）、最大活动能量消耗（aEE）、心率恢复（HRR）和非线性心率变异性（HRV）水平升高。这些指标随着6MWT结果的改善而同时增加。我们采用随机森林和线性回归技术的回归模型显示出强大的预测能力，估计CRF的决定系数值分别为0.91和0.81。夏普利特征归因方法阐明，较高水平的MVPA、aEE、HRR和HRV的非线性动态是CRF测试表现增强的可靠指标。
可穿戴数据驱动的活动和心率指标的整合构成了利用可穿戴设备提供术前心肺适能评估的基础，支持手术风险分层、个性化术前康复和改善患者结局。
