[Acute respiratory distress syndrome secondary to severe pneumonia: how to perform precise and individualized assessment].
Authors
Affiliations (1)
Affiliations (1)
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China.
Abstract
Severe pneumonia is the most common cause of acute respiratory distress syndrome (ARDS), and precise and individualized assessment is a prerequisite for improving therapeutic outcomes. Of all bedside assessment tools, respiratory physiological indicators are the most readily available and widely adopted, covering key metrics including gas exchange, respiratory mechanics, respiratory drive, and inspiratory effort. Recent prospective cohort investigations have validated that physiological metrics, including driving pressure, mechanical power, respiratory drive, and inspiratory effort, correlate strongly with mortality among patients with ARDS. Furthermore, thoracic imaging technologies (such as chest CT, ultrasound, and electrical impedance tomography) can accurately evaluate morphological alterations in ARDS lung tissue and dynamically visualize regional lung ventilation function. These tools have demonstrated considerable clinical utility for facilitating precise diagnosis, tailoring individualized ventilation settings, and assessing weaning readiness. Additionally, evaluating inflammatory immune status and biological subphenotypes not only helps elucidate the core pathophysiological mechanisms underlying ARDS secondary to severe pneumonia, but also provides a rationale for precision therapies using medications such as glucocorticoids. Bedside assessment indicators are growing in variety and availability. Artificial intelligence can efficiently integrate and analyze these large-scale multimodal datasets to construct predictive models. These models can track disease progression trajectories, optimize ARDS management, and advance the translation of Clinical Decision Support Systems into clinical practice. This approach represents a vital direction for future research.