Identification of distinct clinical phenotypes of acute respiratory distress syndrome with differential responses to treatment

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.

Using machine learning for the early prediction of sepsis-associated ARDS in the ICU and identification of clinical phenotypes with differential responses to treatment

“…In our sepsisassociated ARDS patient classification, Cluster 0 patients were Frontiers in Physiology frontiersin.org 13 the least ill and had poor lung recruitability, which is equivalent to the hypoinflammatory phenotype classification of ARDS by Calfee et al (2014) in 2014; mortality is lower with low PEEP. In type I ARDS according to the classification of Liu et al (2021), low PEEP had a lower 60-day mortality rate than high PEEP, which may be because the use of lower PEEP in these patients is sufficient to maintain alveolar inflation and increase functional residual capacity. On the other hand, the patients of Cluster 1 and Cluster 2 have more severe illness, which is equivalent to hyperinflammatory ARDS patients, with a higher mortality rate than Cluster 0.…”

“…However, the determination of biological phenotype is inseparable from the definition of plasma biomarkers, and thus this is difficult to determine at bedside. In 2021, Liu et al (2021) used fast and easily available clinical indicators to classify ARDS patients into three clinical phenotypes. These subtypes were not defined by plasma biomarkers and were thus more convenient for rapid clinical application.…”

“…Therefore, the clinical applicability of this classification system may be limited. In 2021, Liu et al (2021) In this study, we used rapid and easy-to-obtain early clinical indicators in a large-scale population of sepsis and sepsisassociated ARDS patients to build an early diagnostic model for these patients. A rapid early clinical subgroup classification was adopted for this population, assuming that these phenotypes have different clinical outcomes and different treatment responses.…”

“…Therefore, the clinical applicability of this classification system may be limited. In 2021, Liu et al (2021) proposed a new division of ARDS patients into three clinical phenotypes using rapidly accessible clinical indicators. They concluded that the clinical phenotypes of ARDS were associated with different treatment responses to randomized interventions, but this study was based on the entire ARDS population.…”

Longitudinal phenotypes in patients with acute respiratory distress syndrome: a multi-database study

“…Recently, two studies declared that inflammatory phenotypes could also be accurately predicted without biomarker data using supervised learning approaches [24,25]. Another study identified three phenotypes of ARDS based on routine medical data in the eICU database [26]. From another perspective, we concentrated primarily on pulmonary mechanics and organ function, including mechanical power and ventilatory ratio, which were both significantly associated with mortality in ARDS [27,28], and had never been employed to identify phenotypes in traditional ARDS.…”