Mixed‐integer quadratic programming approach for noninvasive estimation of respiratory effort profile during pressure support ventilation

Information about respiratory mechanics such as resistance, elastance, and muscular pressure is important to mitigate ventilator‐induced lung injury. Particularly during pressure support ventilation, the available options to quantify breathing effort and calculate respiratory system mechanics are of...

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Veröffentlicht in:International journal for numerical methods in biomedical engineering Jg. 39; H. 1; S. e3668 - n/a
Hauptverfasser: Victor, Marcus H., Maximo, Marcos R. O. A., Matsumoto, Monica M. S., Pereira, Sérgio M., Tucci, Mauro R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 01.01.2023
Wiley Subscription Services, Inc
Schlagworte:
Algorithms
Breathing
Computer simulation
Estimation
Humans
Integers
Mathematical models
Mechanical properties
Mechanical ventilation
Mechanics (physics)
mixed‐integer quadratic programming
parameter estimation
Positive-Pressure Respiration - methods
Pressure
pressure support ventilation
Quadratic programming
Respiration
Respiration, Artificial
respiratory effort
Respiratory Mechanics - physiology
Respiratory system
Robustness (mathematics)
Solvers
system identification
Ventilation
Ventilators
Waveforms
pressure support ventilation
parameter estimation
mechanical ventilation
system identification
mixed-integer quadratic programming
respiratory effort
ISSN:2040-7939, 2040-7947, 2040-7947
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Zusammenfassung:Information about respiratory mechanics such as resistance, elastance, and muscular pressure is important to mitigate ventilator‐induced lung injury. Particularly during pressure support ventilation, the available options to quantify breathing effort and calculate respiratory system mechanics are often invasive or complex. We herein propose a robust and flexible estimation of respiratory effort better than current methods. We developed a method for non‐invasively estimating breathing effort using only flow and pressure signals. Mixed‐integer quadratic programming (MIQP) was employed, and the binary variables were the switching moments of the respiratory effort waveform. Mathematical constraints, based on ventilation physiology, were set for some variables to restrict feasible solutions. Simulated and patient data were used to verify our method, and the results were compared to an established estimation methodology. Our algorithm successfully estimated the respiratory effort, resistance, and elastance of the respiratory system, resulting in more robust performance and faster solver times than a previously proposed algorithm that used quadratic programming (QP) techniques. In a numerical simulation benchmark, the worst‐case errors for resistance and elastance were 25% and 23% for QP versus <0.1% and <0.1% for MIQP, whose solver times were 4.7 s and 0.5 s, respectively. This approach can estimate several breathing effort profiles and identify the respiratory system's mechanical properties in invasively ventilated critically ill patients.
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ISSN:2040-7939
2040-7947
2040-7947
DOI:10.1002/cnm.3668