Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis. Issue 10 (27th April 2017)

• Record Type: Journal Article
• Authors: Bos, L D; Schouten, L R; van Vught, L A; Wiewel, M A; Ong, D S Y; Cremer, O; Artigas, A; Martin-Loeches, I; Hoogendijk, A J; van der Poll, T; Horn, J; Juffermans, N; Calfee, C S; Schultz, M J
• Other Names: author non-byline.; Frencken Jos F author non-byline.; Bonten Marc author non-byline.; Klein Klouwenberg Peter M C author non-byline.; van Hooijdonk Roosmarijn T M author non-byline.; Huson Mischa A author non-byline.; Straat Marleen author non-byline.; Witteveen Esther author non-byline.; Glas Gerie J author non-byline.; Wieske Luuk author non-byline.; Scicluna Brendon P author non-byline.; Belkasim-Bohoudi H author non-byline.
• Is Part Of: Thorax. Volume 72:Issue 10(2017)
• Page Start: 876
• Page End: 883
• Publication Date: 2017-04-27
• Subjects: ARDS -- Cytokine Biology; Chest -- Diseases -- Periodicals; Thorax; Chest -- Diseases; Periodicals; Periodicals; 617.54
• DOI: 10.1136/thoraxjnl-2016-209719
• Languages: English
• ISSNs: 0040-6376

Abstract

Abstract : Rationale: We hypothesised that patients with acute respiratory distress syndrome (ARDS) can be clustered based on concentrations of plasma biomarkers and that the thereby identified biological phenotypes are associated with mortality. Methods: Consecutive patients with ARDS were included in this prospective observational cohort study. Cluster analysis of 20 biomarkers of inflammation, coagulation and endothelial activation provided the phenotypes in a training cohort, not taking any outcome data into account. Logistic regression with backward selection was used to select the most predictive biomarkers, and these predicted phenotypes were validated in a separate cohort. Multivariable logistic regression was used to quantify the independent association with mortality. Results: Two phenotypes were identified in 454 patients, which we named 'uninflamed' (N=218) and 'reactive' (N=236). A selection of four biomarkers (interleukin-6, interferon gamma, angiopoietin 1/2 and plasminogen activator inhibitor-1) could be used to accurately predict the phenotype in the training cohort (area under the receiver operating characteristics curve: 0.98, 95% CI 0.97 to 0.99). Mortality rates were 15.6% and 36.4% (p<0.001) in the training cohort and 13.6% and 37.5% (p<0.001) in the validation cohort (N=207). The 'reactive phenotype' was independent from confounders associated with intensive care unit mortality (training cohort: OR 1.13, 95% CI 1.04 to 1.23; validation cohort: OR 1.18, 95% CI 1.06 to 1.31). Conclusions: Patients with ARDS can be clustered into two biological phenotypes, with different mortality rates. Four biomarkers can be used to predict the phenotype with high accuracy. The phenotypes were very similar to those found in cohorts derived from randomised controlled trials, and these results may improve patient selection for future clinical trials targeting host response in patients with ARDS.