Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals
Background and Objective: The 6-min 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 and low quality of life. This study aimed to predict the occurrence of adverse events during 6MWT, using c...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.887954/full |
| _version_ | 1811244451600793600 |
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| author | Jiachen Wang Yaning Zang Qian Wu Yingjia She Haoran Xu Jian Zhang Shan Cai Yuzhu Li Zhengbo Zhang |
| author_facet | Jiachen Wang Yaning Zang Qian Wu Yingjia She Haoran Xu Jian Zhang Shan Cai Yuzhu Li Zhengbo Zhang |
| author_sort | Jiachen Wang |
| collection | DOAJ |
| description | Background and Objective: The 6-min 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 and low quality of life. This study aimed to predict the occurrence of adverse events during 6MWT, using continuous physiological parameters combined with demographic variables.Methods: 578 patients with respiratory disease who had performed standardized 6MWT with wearable devices from three hospitals were included in this 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-min resting and 1-min 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, those related to blood oxygen were the most important and those related to heart rate were the most numerous. 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 mMRC (Modified Medical Research Council) scale and Borg scale had the lowest performance, with an AUC of 0.733 and 0.656 respectively.Conclusion: It is 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 during 6MWT. |
| first_indexed | 2024-04-12T14:25:15Z |
| format | Article |
| id | doaj.art-6c57ad0f30684f8fb962cebc2ad5f9cd |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-12T14:25:15Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-6c57ad0f30684f8fb962cebc2ad5f9cd2022-12-22T03:29:28ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-06-011310.3389/fphys.2022.887954887954Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological SignalsJiachen Wang0Yaning Zang1Qian Wu2Yingjia She3Haoran Xu4Jian Zhang5Shan Cai6Yuzhu Li7Zhengbo Zhang8Medical School of Chinese PLA, Beijing, ChinaDepartment of Kinesiology, Shanghai University of Sport, Shanghai, ChinaDepartment of Pulmonary and Critical Care Medicine, the Second Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Pulmonary and Critical Care Medicine, Hainan Hospital of PLA General Hospital, Sanya, ChinaMedical School of Chinese PLA, Beijing, ChinaMedical School of Chinese PLA, Beijing, ChinaDepartment of Pulmonary and Critical Care Medicine, the Second Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Pulmonary and Critical Care Medicine, Hainan Hospital of PLA General Hospital, Sanya, ChinaCenter for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing, ChinaBackground and Objective: The 6-min 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 and low quality of life. This study aimed to predict the occurrence of adverse events during 6MWT, using continuous physiological parameters combined with demographic variables.Methods: 578 patients with respiratory disease who had performed standardized 6MWT with wearable devices from three hospitals were included in this 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-min resting and 1-min 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, those related to blood oxygen were the most important and those related to heart rate were the most numerous. 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 mMRC (Modified Medical Research Council) scale and Borg scale had the lowest performance, with an AUC of 0.733 and 0.656 respectively.Conclusion: It is 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 during 6MWT.https://www.frontiersin.org/articles/10.3389/fphys.2022.887954/full6-min walk testadverse eventsmachine learningwearable devicesphysiological signals |
| spellingShingle | Jiachen Wang Yaning Zang Qian Wu Yingjia She Haoran Xu Jian Zhang Shan Cai Yuzhu Li Zhengbo Zhang Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals Frontiers in Physiology 6-min walk test adverse events machine learning wearable devices physiological signals |
| title | Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals |
| title_full | Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals |
| title_fullStr | Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals |
| title_full_unstemmed | Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals |
| title_short | Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals |
| title_sort | predicting adverse events during six minute walk test using continuous physiological signals |
| topic | 6-min walk test adverse events machine learning wearable devices physiological signals |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.887954/full |
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