Computer methods and programs in biomedicine · 2026
Patient-ventilator interaction-Development of a mathematical model of the respiratory center.
СтатьяНазвание и аннотация: Перевод в очереди
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Hennigs Carlotta, Bilda Franziska, Selpien Helene, Lerg Tjorben, Männel Georg, Becher Tobias, Schädler Dirk, Rostalski Philipp
Аннотация
BACKGROUND AND OBJECTIVE: Patient-ventilator interaction critically influences the safety and effectiveness of mechanical ventilation, particularly under support modes such as pressure support ventilation (PSV).
METHODS: This study introduces a mathematical model of the respiratory center that integrates chemical feedback, mechanical feedback, and reflex mechanisms to simulate spontaneous breathing and its interaction with a mechanical ventilator. The model incorporates O2 and CO2 chemoreflexes, mechanical feedback, and neural control circuits.
RESULTS: Simulation studies evaluated the model's performance under hypercapnia, hypoxia, varying PSV levels, and different types of patient-ventilator asynchrony, comparing outcomes to published literature data. The model accurately reproduced respiratory rate and tidal volume responses with mean absolute percentage errors below 12%, and generated asynchrony frequencies within a 5% acceptance margin. Characteristic ventilatory patterns and emergent asynchronies appeared naturally, without explicit programming of events, demonstrating the physiological plausibility of the model.
CONCLUSIONS: This respiratory center model provides a robust tool for investigating respiratory control, testing ventilator strategies, and supporting the development of intelligent ventilation approaches, including in silico clinical trials. Its ability to reproduce both normal and pathological ventilatory patterns underscores its potential for translational and clinical research applications.
METHODS: This study introduces a mathematical model of the respiratory center that integrates chemical feedback, mechanical feedback, and reflex mechanisms to simulate spontaneous breathing and its interaction with a mechanical ventilator. The model incorporates O2 and CO2 chemoreflexes, mechanical feedback, and neural control circuits.
RESULTS: Simulation studies evaluated the model's performance under hypercapnia, hypoxia, varying PSV levels, and different types of patient-ventilator asynchrony, comparing outcomes to published literature data. The model accurately reproduced respiratory rate and tidal volume responses with mean absolute percentage errors below 12%, and generated asynchrony frequencies within a 5% acceptance margin. Characteristic ventilatory patterns and emergent asynchronies appeared naturally, without explicit programming of events, demonstrating the physiological plausibility of the model.
CONCLUSIONS: This respiratory center model provides a robust tool for investigating respiratory control, testing ventilator strategies, and supporting the development of intelligent ventilation approaches, including in silico clinical trials. Its ability to reproduce both normal and pathological ventilatory patterns underscores its potential for translational and clinical research applications.