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Identification of distinct clinical phenotypes of acute respiratory distress syndrome with differential responses to treatment.
Pubmed ID: 34461969
Pubmed Central ID: PMC8404019
Journal: Critical care (London, England)
Publication Date: Aug. 30, 2021
Affiliation: Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, 150 Haping Road, Nangang District, Harbin, 150081, Heilongjiang, China. changsongwangicu@163.com.
MeSH Terms: Humans, Male, Female, Aged, Aged, 80 and over, Middle Aged, Reproducibility of Results, Fluid Therapy, Intensive Care Units, Positive-Pressure Respiration, Phenotype, Respiratory Distress Syndrome, Telemedicine
Authors: Ma X, Wang C, Liu H, Li X, Zhang J, Luo X, Liu X, Liu Y, Wang H, Jiang Y, Jia X, Guo N, Yu K, Gao Y, Han C, Peng Y, Ju Y, Bu Y, Zhao M, Luo L
Cite As: Liu X, Jiang Y, Jia X, Ma X, Han C, Guo N, Peng Y, Liu H, Ju Y, Luo X, Li X, Bu Y, Zhang J, Liu Y, Gao Y, Zhao M, Wang H, Luo L, Yu K, Wang C. Identification of distinct clinical phenotypes of acute respiratory distress syndrome with differential responses to treatment. Crit Care 2021 Aug 30;25(1):320.
Studies:
- Acute Respiratory Distress Network (ARDSNet) Studies 10 and 12 Statins for Acutely Injured Lungs from Sepsis (SAILS)
- Acute Respiratory Distress Network (ARDSNet) Study 04 Assessment of Low tidal Volume and elevated End-expiratory volume to Obviate Lung Injury (ALVEOLI)
- Acute Respiratory Distress Network (ARDSNet) Study 05 Fluid and Catheter Treatment Trial (FACTT)
Abstract
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome, and the identification of homogeneous subgroups and phenotypes is the first step toward precision critical care. We aimed to explore whether ARDS phenotypes can be identified using clinical data, are reproducible and are associated with clinical outcomes and treatment response. METHODS: This study is based on a retrospective analysis of data from the telehealth intensive care unit (eICU) collaborative research database and three ARDS randomized controlled trials (RCTs) (ALVEOLI, FACTT and SAILS trials). We derived phenotypes in the eICU by cluster analysis based on clinical data and compared the clinical characteristics and outcomes of each phenotype. The reproducibility of the derived phenotypes was tested using the data from three RCTs, and treatment effects were evaluated. RESULTS: Three clinical phenotypes were identified in the training cohort of 3875 ARDS patients. Of the three phenotypes identified, phenotype I (n = 1565; 40%) was associated with fewer laboratory abnormalities, less organ dysfunction and the lowest in-hospital mortality rate (8%). Phenotype II (n = 1232; 32%) was correlated with more inflammation and shock and had a higher mortality rate (18%). Phenotype III (n = 1078; 28%) was strongly correlated with renal dysfunction and acidosis and had the highest mortality rate (22%). These results were validated using the data from the validation cohort (n = 3670) and three RCTs (n = 2289) and had reproducibility. Patients with these ARDS phenotypes had different treatment responses to randomized interventions. Specifically, in the ALVEOLI cohort, the effects of ventilation strategy (high PEEP vs low PEEP) on ventilator-free days differed by phenotype (p = 0.001); in the FACTT cohort, there was a significant interaction between phenotype and fluid-management strategy for 60-day mortality (p = 0.01). The fluid-conservative strategy was associated with improved mortality in phenotype II but had the opposite effect in phenotype III. CONCLUSION: Three clinical phenotypes of ARDS were identified and had different clinical characteristics and outcomes. The analysis shows evidence of a phenotype-specific treatment benefit in the ALVEOLI and FACTT trials. These findings may improve the identification of distinct subsets of ARDS patients for exploration in future RCTs.