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. 2024 Oct 30:11:1473629.
doi: 10.3389/fmed.2024.1473629. eCollection 2024.

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

Affiliations

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

Brijesh V Patel et al. Front Med (Lausanne). .

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; 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.

Clinical trial registration: clinicaltrials.gov, NCT04115709.

Keywords: ARDS; clinical decision support; driving pressure; mechanical ventilation; respiratory mechanics.

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Conflict of interest statement

SR and DK have previously been, but are no longer, shareholders of Mermaid Care A/S, who manufactured the decision support system presented here. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Panel (a) illustrates the structure of the system, including measurement and ventilator inputs to physiological models, which result in calculated patient-specific model parameters and physiological simulations on the color-coded decision space of outcomes. Panel (b) illustrates the complexity of the physiological model, including physiological subsystems (surrounded by dashed boxes) and measurement inputs (surrounded by solid boxes). Panel (c) illustrates the system’s output of patients’ physiological state represented as parameter values for organ systems. Panel (d) illustrates current ventilator settings and systems advice along with the patient’s resulting state, which is illustrated on the hexagon representing the decision space. Gray boxes illustrate current and simulated physiological variables. The green heagon represents a patient with a small risk of adverse effects (color green) with decision-theoretic penalty scores plotted as coordinates on the hexagon represented as a gray outline.
Figure 2
Figure 2
CONSORT diagram illustrating the number of patients randomized in the study and allocated to intervention and control arms and whether treated with extracorporeal membrane oxygenation (ECMO). The number of patients used in each analysis is also illustrated.
Figure 3
Figure 3
Box plots illustrating primary outcome measures of driving pressures on the control and intervention arm measured from breath holds (A) or breath by breath (B) as calculated in the ESM.
Figure 4
Figure 4
Box plots illustrating safety measures for the percentage time spent: A - below SpO2 values of 88%; B - above end-tidal CO2 values of 7 kPa in control ventilation; and C - below the respiratory rate of 12 breaths per minute in pressure support ventilation.

References

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