Physiology-guided mechanical ventilation: Monitoring, proportional assist, and bounded automation

Mechanical ventilation has evolved into a complex intervention that influences lung injuries, respiratory muscle function, and hemodynamic stability. Although lung-protective strategies improve outcomes in acute respiratory distress syndrome, bedside management remains limited by incomplete monitoring of key physiologic variables, including lung stress, inspiratory effort and regional ventilation. This constrains decision such as positive end-expiratory pressure titration and ventilatory assist targeting. Emerging technologies aim to address these gaps by improving physiological assessment and enabling more individualized care. Tools such as esophageal manometry, airway occlusion pressure (P0.1), diaphragm electrical activity, and electrical impedance tomography provide insight into lung mechanics, respiratory drive, and regional ventilation. Proportional modes of ventilation improve patient-synchrony, though their impact on patient-centered outcomes remains variables. Automation and artificial intelligence are increasingly applied to ventilator management, supporting wave analysis, detection of asynchrony, and prediction of weaning readiness. These tools may also assist clinical decision-making within predefined safety limit. We propose a pragmatic, clinical-directed framework integrating physiologic monitoring, proportional assist, and bounded decision support to optimize lung protection, diaphragm function, and hemodynamica stability.