Chen Xiangyu, Fan Junping, Zhao Wenxian, Shi Ruochun, Guo Nan, Chang Zhigang, Song Maifen, Wang Xuedong, Chen Yan, Li Tong, Li Guang-Gang, Su Longxiang, Long Yun
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.
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.
Heliyon. 2024 Jun 27;10(13):e33692. doi: 10.1016/j.heliyon.2024.e33692. eCollection 2024 Jul 15.
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.
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.
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.
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.
患者 - 呼吸机不同步（PVA）在重症监护病房（ICU）接受机械通气的患者中频繁发生，且存在潜在危害。仅依靠医护团队无法准确及时地识别PVA。为解决这一问题，我们的团队开发了一个基于云的机械通气监测平台，包括PVA远程监测系统和24小时机械通气分析报告。我们在14个ICU进行了一项调查，以评估医生对该平台的满意度和接受度。
来自病历、临床信息系统和呼吸机的数据被上传到云平台并进行数据处理。对数据进行分析以监测PVA，并在前端显示。生成24小时机械通气分析报告以供临床参考。参与该平台的14家医院ICU的重症医学医生参与了问卷调查，其中10名医生接受了访谈，以调查医生对该系统的接受度和意见。
PVA远程监测系统在检测流量不足、提前周期、延迟周期、反向触发、自动触发和过冲方面表现出高度特异性，灵敏度分别为90.31%、98.76%、99.75%、99.97%、100%和99.69%。24小时分析报告提供了有关PVA和呼吸力学的重要数据。86.2%（75/87）的医生支持该平台的应用。
PVA远程监测系统准确识别PVA，机械通气分析报告为控制PVA提供了指导。我们的平台可以有效地协助ICU医生管理机械通气患者。
