Brijesh V Patel, Sharon Mumby, Nicholas Johnson, Rhodri Handslip, Sunil Patel, Teresa Lee, Martin S Andersen, Emanuela Falaschetti, Ian M Adcock, Danny F McAuley, Masao Takata, Thomas Staudinger, Dan S Karbing, Matthieu Jabaudon, Peter Schellongowski, Stephen E Rees
{"title":"一项随机对照试验，评估基于生理模型/数字孪生决策支持系统对急性呼吸窘迫综合征患者机械通气的建议。","authors":"Brijesh V Patel, Sharon Mumby, Nicholas Johnson, Rhodri Handslip, Sunil Patel, Teresa Lee, Martin S Andersen, Emanuela Falaschetti, Ian M Adcock, Danny F McAuley, Masao Takata, Thomas Staudinger, Dan S Karbing, Matthieu Jabaudon, Peter Schellongowski, Stephen E Rees","doi":"10.3389/fmed.2024.1473629","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Acute respiratory distress syndrome (ARDS) is highly heterogeneous, both in its clinical presentation and in the patient's physiological responses to changes in mechanical ventilator settings, such as PEEP. This study investigates the clinical efficacy of a physiological model-based ventilatory decision support system (DSS) to personalize ventilator therapy in ARDS patients.</p><p><strong>Methods: </strong>This international, multicenter, randomized, open-label study enrolled patients with ARDS during the COVID-19 pandemic. Patients were randomized to either receive active advice from the DSS (intervention) or standard care without DSS advice (control). The primary outcome was to detect a reduction in average driving pressure between groups. Secondary outcomes included several clinically relevant measures of respiratory physiology, ventilator-free days, time from control mode to support mode, number of changes in ventilator settings per day, percentage of time in control and support mode ventilation, ventilation- and device-related adverse events, and the number of times the advice was followed.</p><p><strong>Results: </strong>A total of 95 patients were randomized in this study. The DSS showed no significant effect on average driving pressure between groups. However, patients in the intervention arm had a statistically improved oxygenation index when in support mode ventilation (-1.41, 95% CI: -2.76, -0.08; <i>p</i> = 0.0370). Additionally, the ventilatory ratio significantly improved in the intervention arm for patients in control mode ventilation (-0.63, 95% CI: -1.08, -0.17, <i>p</i> = 0.0068). The application of the DSS led to a significantly increased number of ventilator changes for pressure settings and respiratory frequency.</p><p><strong>Conclusion: </strong>The use of a physiological model-based decision support system for providing advice on mechanical ventilation in patients with COVID-19 and non-COVID-19 ARDS showed no significant difference in driving pressure as a primary outcome measure. However, the application of approximately 60% of the DSS advice led to improvements in the patient's physiological state.</p><p><strong>Clinical trial registration: </strong>clinicaltrials.gov, NCT04115709.</p>","PeriodicalId":12488,"journal":{"name":"Frontiers in Medicine","volume":"11 ","pages":"1473629"},"PeriodicalIF":3.1000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11559429/pdf/","citationCount":"0","resultStr":"{\"title\":\"A randomized control trial evaluating the advice of a physiological-model/digital twin-based decision support system on mechanical ventilation in patients with acute respiratory distress syndrome.\",\"authors\":\"Brijesh V Patel, Sharon Mumby, Nicholas Johnson, Rhodri Handslip, Sunil Patel, Teresa Lee, Martin S Andersen, Emanuela Falaschetti, Ian M Adcock, Danny F McAuley, Masao Takata, Thomas Staudinger, Dan S Karbing, Matthieu Jabaudon, Peter Schellongowski, Stephen E Rees\",\"doi\":\"10.3389/fmed.2024.1473629\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Acute respiratory distress syndrome (ARDS) is highly heterogeneous, both in its clinical presentation and in the patient's physiological responses to changes in mechanical ventilator settings, such as PEEP. This study investigates the clinical efficacy of a physiological model-based ventilatory decision support system (DSS) to personalize ventilator therapy in ARDS patients.</p><p><strong>Methods: </strong>This international, multicenter, randomized, open-label study enrolled patients with ARDS during the COVID-19 pandemic. Patients were randomized to either receive active advice from the DSS (intervention) or standard care without DSS advice (control). The primary outcome was to detect a reduction in average driving pressure between groups. Secondary outcomes included several clinically relevant measures of respiratory physiology, ventilator-free days, time from control mode to support mode, number of changes in ventilator settings per day, percentage of time in control and support mode ventilation, ventilation- and device-related adverse events, and the number of times the advice was followed.</p><p><strong>Results: </strong>A total of 95 patients were randomized in this study. The DSS showed no significant effect on average driving pressure between groups. However, patients in the intervention arm had a statistically improved oxygenation index when in support mode ventilation (-1.41, 95% CI: -2.76, -0.08; <i>p</i> = 0.0370). Additionally, the ventilatory ratio significantly improved in the intervention arm for patients in control mode ventilation (-0.63, 95% CI: -1.08, -0.17, <i>p</i> = 0.0068). The application of the DSS led to a significantly increased number of ventilator changes for pressure settings and respiratory frequency.</p><p><strong>Conclusion: </strong>The use of a physiological model-based decision support system for providing advice on mechanical ventilation in patients with COVID-19 and non-COVID-19 ARDS showed no significant difference in driving pressure as a primary outcome measure. However, the application of approximately 60% of the DSS advice led to improvements in the patient's physiological state.</p><p><strong>Clinical trial registration: </strong>clinicaltrials.gov, NCT04115709.</p>\",\"PeriodicalId\":12488,\"journal\":{\"name\":\"Frontiers in Medicine\",\"volume\":\"11 \",\"pages\":\"1473629\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11559429/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fmed.2024.1473629\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fmed.2024.1473629","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
A randomized control trial evaluating the advice of a physiological-model/digital twin-based decision support system on mechanical ventilation in patients with acute respiratory distress syndrome.
Background: Acute respiratory distress syndrome (ARDS) is highly heterogeneous, both in its clinical presentation and in the patient's physiological responses to changes in mechanical ventilator settings, such as PEEP. This study investigates the clinical efficacy of a physiological model-based ventilatory decision support system (DSS) to personalize ventilator therapy in ARDS patients.
Methods: This international, multicenter, randomized, open-label study enrolled patients with ARDS during the COVID-19 pandemic. Patients were randomized to either receive active advice from the DSS (intervention) or standard care without DSS advice (control). The primary outcome was to detect a reduction in average driving pressure between groups. Secondary outcomes included several clinically relevant measures of respiratory physiology, ventilator-free days, time from control mode to support mode, number of changes in ventilator settings per day, percentage of time in control and support mode ventilation, ventilation- and device-related adverse events, and the number of times the advice was followed.
Results: A total of 95 patients were randomized in this study. The DSS showed no significant effect on average driving pressure between groups. However, patients in the intervention arm had a statistically improved oxygenation index when in support mode ventilation (-1.41, 95% CI: -2.76, -0.08; p = 0.0370). Additionally, the ventilatory ratio significantly improved in the intervention arm for patients in control mode ventilation (-0.63, 95% CI: -1.08, -0.17, p = 0.0068). The application of the DSS led to a significantly increased number of ventilator changes for pressure settings and respiratory frequency.
Conclusion: The use of a physiological model-based decision support system for providing advice on mechanical ventilation in patients with COVID-19 and non-COVID-19 ARDS showed no significant difference in driving pressure as a primary outcome measure. However, the application of approximately 60% of the DSS advice led to improvements in the patient's physiological state.
期刊介绍：
Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate
- the use of patient-reported outcomes under real world conditions
- the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines
- the scientific bases for guidelines and decisions from regulatory authorities
- access to medicinal products and medical devices worldwide
- addressing the grand health challenges around the world
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