Personalized Respiratory Support in ARDS: A Physiology-to-Bedside Review
- PMID: 37445211
- PMCID: PMC10342961
- DOI: 10.3390/jcm12134176
Personalized Respiratory Support in ARDS: A Physiology-to-Bedside Review
Abstract
Acute respiratory distress syndrome (ARDS) is a leading cause of disability and mortality worldwide, and while no specific etiologic interventions have been shown to improve outcomes, noninvasive and invasive respiratory support strategies are life-saving interventions that allow time for lung recovery. However, the inappropriate management of these strategies, which neglects the unique features of respiratory, lung, and chest wall mechanics may result in disease progression, such as patient self-inflicted lung injury during spontaneous breathing or by ventilator-induced lung injury during invasive mechanical ventilation. ARDS characteristics are highly heterogeneous; therefore, a physiology-based approach is strongly advocated to titrate the delivery and management of respiratory support strategies to match patient characteristics and needs to limit ARDS progression. Several tools have been implemented in clinical practice to aid the clinician in identifying the ARDS sub-phenotypes based on physiological peculiarities (inspiratory effort, respiratory mechanics, and recruitability), thus allowing for the appropriate application of personalized supportive care. In this narrative review, we provide an overview of noninvasive and invasive respiratory support strategies, as well as discuss how identifying ARDS sub-phenotypes in daily practice can help clinicians to deliver personalized respiratory support and potentially improve patient outcomes.
Keywords: AHRF; ARDS; CPAP; HFOT; NIV; mechanical ventilation.
Conflict of interest statement
D.L.G. received payments for travel expenses by Getinge and Air Liquide, as well as for speaking fees by Intersurgical, GE, Fisher and Paykel, and Gilead. M.A. received payments for board participation from Maquet, Air Liquide, and Chiesi. D.L.G. and M.A. disclose a research grant by General Electric Healthcare. D.L.G. was supported by grants by ESICM and SIAARTI.
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