BibTex format
@article{Patel:2024:10.3389/fmed.2024.1473629,
author = {Patel, B and Mumby, S and Johnson, N and Handslip, R and Patel, S and Lee, T and Anderson, MS and Falaschetti, E and Adcock, I and McAuley, D and Takata, M and Staudinger, T and Karbing, DS and Jabaudon, M and Schellongowski, P and Rees, SE and Patel, B},
doi = {10.3389/fmed.2024.1473629},
journal = {Frontiers in Medicine},
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},
url = {http://dx.doi.org/10.3389/fmed.2024.1473629},
volume = {11},
year = {2024}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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 pre
AU - Patel,B
AU - Mumby,S
AU - Johnson,N
AU - Handslip,R
AU - Patel,S
AU - Lee,T
AU - Anderson,MS
AU - Falaschetti,E
AU - Adcock,I
AU - McAuley,D
AU - Takata,M
AU - Staudinger,T
AU - Karbing,DS
AU - Jabaudon,M
AU - Schellongowski,P
AU - Rees,SE
AU - Patel,B
DO - 10.3389/fmed.2024.1473629
PY - 2024///
SN - 2296-858X
TI - 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
T2 - Frontiers in Medicine
UR - http://dx.doi.org/10.3389/fmed.2024.1473629
UR - https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1473629/abstract
VL - 11
ER -