Application of a cloud platform that identifies patient-ventilator asynchrony and enables continuous monitoring of mechanical ventilation in intensive care unit
Xiangyu Chen,
Junping Fan,
Wenxian Zhao,
Ruochun Shi,
Nan Guo,
Zhigang Chang,
Maifen Song,
Xuedong Wang,
Yan Chen,
Tong Li,
Guang-gang Li,
Longxiang Su,
Yun Long
Affiliations
Xiangyu Chen
Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
Junping Fan
Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, China
Wenxian Zhao
Department of Critical Care Medicine, Beijing Puren Hospital, Beijing, 100062, China
Ruochun Shi
Department of Critical Care Medicine, Beijing Sixth Hospital, Beijing, 100007, China
Nan Guo
Intensive Care Unit, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China
Zhigang Chang
Intensive Care Unit, Beijing Hospital, Beijing, 100005, China
Maifen Song
Department of Critical Care Medicine, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China
Xuedong Wang
Intensive Care Unit, Beijing Hepingli Hospital, Beijing, 100013, China
Yan Chen
Intensive Care Unit, Beijing Longfu Hospital, Beijing, 100010, China
Tong Li
Intensive Care Unit, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
Guang-gang Li
Department of Critical Care Medicine, 7th Medical Center of PLA General Hospital, Beijing, China
Longxiang Su
Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China; Corresponding author. Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1st Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
Yun Long
Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China; Corresponding author. Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1st Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
Background: Patient-ventilator asynchrony (PVA) frequently occurs in mechanically ventilated patients within the ICU and has the potential for harm. Depending solely on the health care team cannot accurately and promptly identify PVA. To address this issue, our team has developed a cloud-based platform for monitoring mechanical ventilation (MV), comprising the PVA-RemoteMonitor system and the 24-h MV analysis report. We conducted a survey to evaluate physicians' satisfaction and acceptance of the platform in 14 ICUs. Methods: Data from medical records, clinical information systems, and ventilators were uploaded to the cloud platform and underwent data processing. The data were analyzed to monitor PVA and displayed in the front-end. The 24-h analysis report for MV was generated for clinical reference. Critical care physicians in 14 hospitals' ICUs that involved in the platform participated in a questionnaire survey, among whom 10 physicians were interviewed to investigate physicians’ acceptance and opinions of this system. Results: The PVA-RemoteMonitor system exhibited a high level of specificity in detecting flow insufficiency, premature cycle, delayed cycle, reverse trigger, auto trigger, and overshoot, with sensitivities of 90.31 %, 98.76 %, 99.75 %, 99.97 %, 100 %, and 99.69 %, respectively. The 24-h analysis report supplied essential data about PVA and respiratory mechanics. 86.2 % (75/87) of physicians supported the application of this platform. Conclusions: The PVA-RemoteMonitor system accurately identified PVA, and the MV analysis report provided guidance in controlling PVA. Our platform can effectively assist ICU physicians in the management of ventilated patients.
