Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals - Wang, Jiachen; Zang, Yaning; Wu, Qian; She, Yingjia; Xu, Haoran; Zhang, Jian; Cai, Shan; Li, Yuzhu; Zhang, Zhengbo (Frontiers in Physiology, 2022)
Background and objective: The six-minute walk test (6MWT) is a common functional assessment test, but adverse events during the test can be potentially dangerous and can lead to serious consequences, such as death and life-threatening events. This study aimed to predict the occurrence of adverse events during 6MWT, using continuous physiological parameters combined with demographic variables. Methods: 578 patients who had performed standardized 6MWT with wearable devices from 3 hospitals were included in this retrospective study. Adverse events occurred in 73 patients (12.6%). ECG, respiratory signal, tri-axial acceleration signals, oxygen saturation, demographic variables and scales assessment were obtained. Feature extraction and selection of physiological signals were performed during 2-minute resting and 1-minute movement phases. 5-fold cross-validation was used to assess the machine learning models. The predictive ability of different models and scales was compared. Results: Of the 16 features selected by the recursive feature elimination method, heart rate related features were the most important. Light Gradient Boosting Machine (LightGBM) had the highest AUC of 0.874±0.063 and the AUC of Logistic Regression was AUC of 0.869±0.067. The National Early Warning Score (NEWS), a scale related to physiological parameters, was more effective than other scales (Borg and Modified Medical Research Council (mMRC) dyspnea score) with an AUC of 0.739. Conclusion: It’s feasible to predict the occurrence of adverse event during 6MWT using continuous physiological parameters combined with demographic variables. Wearable sensors/systems can be used for continuous physiological monitoring and provide additional tools for patient safety, no matter during 6MWT or in daily life.