Akademik Veri Yönetim Sistemi
THE LANCET RESPIRATORY MEDICINE, cilt.10, sa.4, ss.367-377, 2022 (SCI-Expanded)
Background Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory)
with distinct clinical and biological features and differential treatment responses have been identified using latent class
analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier
models using readily available clinical variables have been described in four randomised controlled trials. We aimed to
assess the performance of these models in observational cohorts of ARDS.
Methods In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier
models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of
Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with
LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables,
and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and
demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve
(AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity,
specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using
data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational
Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational,
observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to
determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory
pressure (PEEP) strategy, with 90-day mortality as the dependent variable.
Findings The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92
(95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when
using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94]
vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory
subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001).
There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower
90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory
subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group;
hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in
the low PEEP group).
Interpretation Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in
observational cohorts. Application of these models can provide valuable prognostic information and could inform
management strategies for personalised treatment, including application of PEEP, once prospectively validated.