Methodological development of the remote ventilate view platform for real-time monitoring of patient-ventilator asynchrony and respiratory parameters in severe pneumonia patients

Xiangyu Chen , Siyi Yuan , Elias Baedorf Kassis , Song Zhang , Yi Chi , Shengjun Liu , Fuhong Cai , Yue Ma , Yan Li , Longxiang Su , Yun Long
{"title":"Methodological development of the remote ventilate view platform for real-time monitoring of patient-ventilator asynchrony and respiratory parameters in severe pneumonia patients","authors":"Xiangyu Chen ,&nbsp;Siyi Yuan ,&nbsp;Elias Baedorf Kassis ,&nbsp;Song Zhang ,&nbsp;Yi Chi ,&nbsp;Shengjun Liu ,&nbsp;Fuhong Cai ,&nbsp;Yue Ma ,&nbsp;Yan Li ,&nbsp;Longxiang Su ,&nbsp;Yun Long","doi":"10.1016/j.jointm.2025.07.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Patient-ventilator asynchrony (PVA) is common in critically ill patients undergoing mechanical ventilation and may adversely affect clinical outcomes. Traditional bedside assessment methods are subjective and intermittent. We developed a real-time digital platform to continuously monitor ventilator waveforms and quantify overall asynchrony burden of severe pneumonia patients.</div></div><div><h3>Methods</h3><div>The study retrospectively analyzed mechanically ventilated coronavirus disease 2019 (COVID-19 patients admitted to the Department of Critical Care Medicine of Peking Union Medical College Hospital (PUMCH) from December 2022 to August 2023. Ventilator waveforms were continuously collected and processed using the remote ventilate view platform, which automatically identified eight PVA subtypes and calculated the Overall Asynchrony Index (OAI) across the full ventilation course. Respiratory mechanics were also extracted. Primary outcomes included intensive care unit (ICU) mortality and 28-day ventilator-free days (VFDs), while secondary outcomes included the length of ICU stay and duration of mechanical ventilation. The study used R, Jamovi, and Python for statistical analysis.</div></div><div><h3>Results</h3><div>Twenty-three mechanically ventilated COVID-19 patients admitted to the ICU at Peking Union Medical College Hospital were included in this study. No correlation was found between the index and ventilatory parameters, compliance, and disease severity. Patients with an OAI ≥10 % were more likely to have fewer 28-day VFDs (1.3 days <em>vs</em>. 11.4 days, <em>P</em> = 0.027) and were demonstrated to have a higher ICU mortality (66.7 % <em>vs</em>. 18.2 %, <em>P</em> = 0.036). Among eight types of PVA, flow insufficiency was found to be associated with prognosis (<em>P</em> = 0.012). OAI correlated with the prognosis of COVID-19 patients. Patients with an OAI ≥10 % were more likely to have fewer 28-day VFDs and higher ICU mortality.</div></div><div><h3>Conclusions</h3><div>A higher OAI and increased flow insufficiency were associated with worse outcomes in COVID-19 patients receiving mechanical ventilation. This study demonstrates the feasibility and clinical potential of a real-time, platform-based approach for automated detection and longitudinal monitoring of PVA.</div></div>","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"5 4","pages":"Pages 367-376"},"PeriodicalIF":0.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of intensive medicine","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667100X25000611","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Patient-ventilator asynchrony (PVA) is common in critically ill patients undergoing mechanical ventilation and may adversely affect clinical outcomes. Traditional bedside assessment methods are subjective and intermittent. We developed a real-time digital platform to continuously monitor ventilator waveforms and quantify overall asynchrony burden of severe pneumonia patients.

Methods

The study retrospectively analyzed mechanically ventilated coronavirus disease 2019 (COVID-19 patients admitted to the Department of Critical Care Medicine of Peking Union Medical College Hospital (PUMCH) from December 2022 to August 2023. Ventilator waveforms were continuously collected and processed using the remote ventilate view platform, which automatically identified eight PVA subtypes and calculated the Overall Asynchrony Index (OAI) across the full ventilation course. Respiratory mechanics were also extracted. Primary outcomes included intensive care unit (ICU) mortality and 28-day ventilator-free days (VFDs), while secondary outcomes included the length of ICU stay and duration of mechanical ventilation. The study used R, Jamovi, and Python for statistical analysis.

Results

Twenty-three mechanically ventilated COVID-19 patients admitted to the ICU at Peking Union Medical College Hospital were included in this study. No correlation was found between the index and ventilatory parameters, compliance, and disease severity. Patients with an OAI ≥10 % were more likely to have fewer 28-day VFDs (1.3 days vs. 11.4 days, P = 0.027) and were demonstrated to have a higher ICU mortality (66.7 % vs. 18.2 %, P = 0.036). Among eight types of PVA, flow insufficiency was found to be associated with prognosis (P = 0.012). OAI correlated with the prognosis of COVID-19 patients. Patients with an OAI ≥10 % were more likely to have fewer 28-day VFDs and higher ICU mortality.

Conclusions

A higher OAI and increased flow insufficiency were associated with worse outcomes in COVID-19 patients receiving mechanical ventilation. This study demonstrates the feasibility and clinical potential of a real-time, platform-based approach for automated detection and longitudinal monitoring of PVA.
重型肺炎患者实时监测患者-呼吸机不同步及呼吸参数的远程通气视图平台方方学开发
患者-呼吸机不同步(PVA)在接受机械通气的危重患者中很常见,并可能对临床结果产生不利影响。传统的床边评估方法是主观的和间歇性的。我们开发了一个实时数字平台来连续监测呼吸机波形并量化重症肺炎患者的总体非同步负担。方法回顾性分析2022年12月至2023年8月北京协和医院重症医学科收治的2019冠状病毒病(COVID-19)患者。使用远程通气视图平台连续收集和处理呼吸机波形,自动识别8种PVA亚型并计算整个通气过程中的总体异步指数(OAI)。同时提取呼吸力学。主要结局包括重症监护病房(ICU)死亡率和28天无呼吸机天数(vfd),次要结局包括ICU住院时间和机械通气持续时间。本研究使用R、Jamovi和Python进行统计分析。结果入选北京协和医院重症监护病房机械通气的新冠肺炎患者23例。该指数与通气参数、依从性和疾病严重程度之间没有相关性。OAI≥10%的患者28天vfd更少(1.3天vs. 11.4天,P = 0.027), ICU死亡率更高(66.7% vs. 18.2%, P = 0.036)。在8种PVA类型中,血流不足与预后相关(P = 0.012)。OAI与COVID-19患者预后相关。OAI≥10%的患者更有可能出现较少的28天vfd和较高的ICU死亡率。结论采用机械通气的COVID-19患者OAI升高和血流不全加重与预后较差相关。本研究证明了实时、基于平台的PVA自动检测和纵向监测方法的可行性和临床潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of intensive medicine
Journal of intensive medicine Critical Care and Intensive Care Medicine
CiteScore
1.90
自引率
0.00%
发文量
0
审稿时长
58 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信