Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data.

Journal: BMC medical informatics and decision making
PMID: 39891164

Abstract

BACKGROUND: Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs.

Authors

  • Lixuan Li
    Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China.
  • Yuekong Hu
    Department of Rehabilitation Medicine, West China Tianfu Hospital, Sichuan University, Chengdu, China.
  • Zhicheng Yang
    PAII Inc, Palo Alto, CA, 94306, USA.
  • Zeruxin Luo
    Department of Rehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, China.
  • Jiachen Wang
    Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37235, USA.
  • Wenqing Wang
    National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China.
  • Xiaoli Liu
    Neurology Department, Zhejiang Hospital, Zhejiang 310013, China.
  • Yuqiang Wang
    Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, China.
  • Yong Fan
    CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
  • Pengming Yu
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Zhengbo Zhang
    Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China. Electronic address: zhengbozhang@126.com.

Keywords

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