A wearable six-minute walk-based system to predict postoperative pulmonary complications after cardiac valve surgery: an exploratory study.
10.7507/1001-5515.202305007
- Author:
Yuqiang WANG
1
;
Jiachen WANG
2
;
Jian ZHANG
3
;
Zeruxin LUO
4
;
Yingqiang GUO
1
;
Zhengbo ZHANG
5
;
Pengming YU
4
Author Information
1. Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China.
2. General Hospital of Tibet Military Region, Lhasa 850007, P. R. China.
3. Medical School of Chinese PLA, Beijing 100853, P. R. China.
4. Rehabilitation Medical Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China.
5. Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing 100853, P. R. China.
- Publication Type:Journal Article
- Keywords:
6-Minute Walk Test;
Cardiac valve surgery;
Continuous monitoring;
Postoperative pulmonary complications;
Predictive model
- MeSH:
Humans;
Lung;
Walking/physiology*;
Walk Test;
Heart Valves/surgery*;
Postoperative Period;
Postoperative Complications/etiology*
- From:
Journal of Biomedical Engineering
2023;40(6):1117-1125
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, wearable devices have seen a booming development, and the integration of wearable devices with clinical settings is an important direction in the development of wearable devices. The purpose of this study is to establish a prediction model for postoperative pulmonary complications (PPCs) by continuously monitoring respiratory physiological parameters of cardiac valve surgery patients during the preoperative 6-Minute Walk Test (6MWT) with a wearable device. By enrolling 53 patients with cardiac valve diseases in the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, the grouping was based on the presence or absence of PPCs in the postoperative period. The 6MWT continuous respiratory physiological parameters collected by the SensEcho wearable device were analyzed, and the group differences in respiratory parameters and oxygen saturation parameters were calculated, and a prediction model was constructed. The results showed that continuous monitoring of respiratory physiological parameters in 6MWT using a wearable device had a better predictive trend for PPCs in cardiac valve surgery patients, providing a novel reference model for integrating wearable devices with the clinic.
