The “respiratory envelope”, an aviation-inspired framework for patient tailored mechanical ventilation
Christiaan L. Meuwese (Erasmus MC)
Eline Oppersma (University of Twente)
Peter Somhorst (TU Delft - Mechanical Engineering, Erasmus MC)
Leo Heunks (Radboud University Medical Center)
Joris A. Melkert (TU Delft - Aerospace Engineering)
Annemijn H. Jonkman (Erasmus MC, TU Delft - Mechanical Engineering)
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Abstract
Contemporary mechanical ventilation strategies in ARDS rely on lung-protective ventilation approaches that typically utilize separate static variables/settings (e.g., tidal volumes, plateau pressure) and cut-off values. This approach fails to capture complex patient dynamics. Increasing efforts have focused on the interactions between different parameters (e.g. mechanical power, driving pressure); however, these only accommodate a limited number of interactions and do not integrate the complexity of patient-specific, severity-dependent and time-varying thresholds for “safe” mechanical ventilation. In aviation, the “flight envelope” concept revolutionized flight safety by defining flexible, context-dependent boundaries as a function of multiple dimensions (e.g. flight speed, altitude, (dynamic) loads on the aircraft) within which an aircraft can operate. When a new aircraft is developed, its operational safety envelope is determined analytically. Calculations are quickly followed by flight simulations and flight testing, beginning from a known safe point within the envelope and gradually carefully exploring its boundaries to characterize its limits. Inspired by this aviation approach, we propose the “respiratory envelope” framework for mechanical ventilation: a conceptual framework that allows for the incorporation of multiple (time-dependent) dimensions and integrates interactions between ventilator variables/settings and patient characteristics. Just as flight envelopes informed the development of automated envelope protection algorithms and autopilot systems, a similar framework could be applied in clinical settings to simulate real-time “safety zones” based on continuously monitored, yet underutilized, physiological data, such as in digital twins.