Mechanical ventilation is a fundamental intervention in intensive care medicine, providing vital support for patients with severe respiratory failure. However, this life-sustaining therapy also carries the risk of harm. Ventilator-induced lung injury (VILI) is now predominantly understood in terms of lung overdistension, characterized by excessive stress and strain on pulmonary tissue. In recent years, a variety of novel monitoring strategies have emerged, from refined measurements of respiratory mechanics to advanced imaging and physiologic modeling, to help in bedside detection of excessive lung stress and strain. Electrical impedance tomography is a non-invasive tool providing real-time imaging of regional ventilation and assisting in the diagnosis of overdistension and its minimization through positive end-expiratory pressure titration, also during partial support ventilation. Pleural and lung ultrasound might also suggest the occurrence of overdistension, although clinical data are still preliminary. Bedside maneuvers, such as changing patient positioning or applying abdominal weights, can help identify overdistension by observing change in respiratory mechanics. Ventilator-based methods like the recruitment-to-inflation ratio and the overdistension index help assess the risk of overdistension, despite requiring careful interpretation and validation. Biomarkers such as Clara cell secretory protein-16 and stretch-induced gene signatures represent a promising avenue for real-time monitoring of lung injury, though further validation is needed. These tools aim to help clinicians individualize ventilator settings, balancing adequate gas exchange with lung protection. Despite this progress, most techniques remain in the realm of research. Few have undergone the rigorous physiological and clinical validation necessary for routine bedside use. As the critical care community moves toward more personalized ventilation strategies, establishing reliable, real-time methods to assess lung stress and strain at the bedside will be key to translating innovation into improved patient outcomes.
Pettenuzzo et al. (Mon,) studied this question.