一项评估基于生理模型/数字孪生的决策支持系统对急性呼吸窘迫综合征患者机械通气建议的随机对照试验。

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.

作者信息

Patel Brijesh V, Mumby Sharon, Johnson Nicholas, Handslip Rhodri, Patel Sunil, Lee Teresa, Andersen Martin S, Falaschetti Emanuela, Adcock Ian M, McAuley Danny F, Takata Masao, Staudinger Thomas, Karbing Dan S, Jabaudon Matthieu, Schellongowski Peter, Rees Stephen E

机构信息

Division of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom.

Department of Critical Care, Royal Brompton Hospital, London, United Kingdom.

出版信息

Front Med (Lausanne). 2024 Oct 30;11:1473629. doi: 10.3389/fmed.2024.1473629. eCollection 2024.

DOI:10.3389/fmed.2024.1473629

PMID:39540041

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11559429/

Abstract

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; = 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, = 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.

CLINICAL TRIAL REGISTRATION

clinicaltrials.gov, NCT04115709.

摘要

背景

急性呼吸窘迫综合征（ARDS）在临床表现以及患者对机械通气设置变化（如呼气末正压通气[PEEP]）的生理反应方面具有高度异质性。本研究调查了基于生理模型的通气决策支持系统（DSS）在ARDS患者中个性化通气治疗的临床疗效。

方法

这项国际多中心随机开放标签研究纳入了新冠疫情期间的ARDS患者。患者被随机分为两组，一组接受DSS的积极建议（干预组），另一组接受无DSS建议的标准治疗（对照组）。主要结局是检测两组之间平均驱动压的降低情况。次要结局包括呼吸生理学的几项临床相关指标、无呼吸机天数、从控制模式转换到支持模式的时间、每天呼吸机设置的变化次数、控制和支持模式通气的时间百分比、通气及设备相关不良事件以及遵循建议的次数。

结果

本研究共随机分配了95名患者。DSS对两组之间的平均驱动压无显著影响。然而，干预组患者在支持模式通气时氧合指数有统计学意义的改善（-1.41，95%置信区间：-2.76，-0.08；P = 0.0370）。此外，干预组患者在控制模式通气时通气比显著改善（-0.63，95%置信区间：-1.08，-0.17；P = 0.0068）。DSS的应用导致压力设置和呼吸频率的呼吸机变化次数显著增加。