Pratik Sinha1, Kevin L Delucchi2, B Taylor Thompson3, Daniel F McAuley4,5, Michael A Matthay6,7,8, Carolyn S Calfee6,7,8. 1. Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, 505 Parnassus Ave, Box 0111, San Francisco, CA, 94143-0111, USA. pratik.sinha@ucsf.edu. 2. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA. 3. Department of Medicine, Division of Pulmonary and Critical Care, Massachusetts General Hospital, Boston, MA, USA. 4. Centre for Experimental Medicine, School of Medicine, Dentistry and Biomedical Sciences, Queen's University of Belfast, Belfast, UK. 5. Regional Intensive Care Unit, Royal Victoria Hospital, Belfast, UK. 6. Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, 505 Parnassus Ave, Box 0111, San Francisco, CA, 94143-0111, USA. 7. Department of Anesthesia, University of California, San Francisco, San Francisco, CA, USA. 8. Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, USA.
Abstract
PURPOSE: Using latent class analysis (LCA), we have consistently identified two distinct subphenotypes in four randomized controlled trial cohorts of ARDS. One subphenotype has hyper-inflammatory characteristics and is associated with worse clinical outcomes. Further, within three negative clinical trials, we observed differential treatment response by subphenotype to randomly assigned interventions. The main purpose of this study was to identify ARDS subphenotypes in a contemporary NHLBI Network trial of infection-associated ARDS (SAILS) using LCA and to test for differential treatment response to rosuvastatin therapy in the subphenotypes. METHODS: LCA models were constructed using a combination of biomarker and clinical data at baseline in the SAILS study (n = 745). LCA modeling was then repeated using an expanded set of clinical class-defining variables. Subphenotypes were tested for differential treatment response to rosuvastatin. RESULTS: The two-class LCA model best fit the population. Forty percent of the patients were classified as the "hyper-inflammatory" subphenotype. Including additional clinical variables in the LCA models did not identify new classes. Mortality at day 60 and day 90 was higher in the hyper-inflammatory subphenotype. No differences in outcome were observed between hyper-inflammatory patients randomized to rosuvastatin therapy versus placebo. CONCLUSIONS: LCA using a two-subphenotype model best described the SAILS population. The subphenotypes have features consistent with those previously reported in four other cohorts. Addition of new class-defining variables in the LCA model did not yield additional subphenotypes. No treatment effect was observed with rosuvastatin. These findings further validate the presence of two subphenotypes and demonstrate their utility for patient stratification in ARDS.
PURPOSE: Using latent class analysis (LCA), we have consistently identified two distinct subphenotypes in four randomized controlled trial cohorts of ARDS. One subphenotype has hyper-inflammatory characteristics and is associated with worse clinical outcomes. Further, within three negative clinical trials, we observed differential treatment response by subphenotype to randomly assigned interventions. The main purpose of this study was to identify ARDS subphenotypes in a contemporary NHLBI Network trial of infection-associated ARDS (SAILS) using LCA and to test for differential treatment response to rosuvastatin therapy in the subphenotypes. METHODS: LCA models were constructed using a combination of biomarker and clinical data at baseline in the SAILS study (n = 745). LCA modeling was then repeated using an expanded set of clinical class-defining variables. Subphenotypes were tested for differential treatment response to rosuvastatin. RESULTS: The two-class LCA model best fit the population. Forty percent of the patients were classified as the "hyper-inflammatory" subphenotype. Including additional clinical variables in the LCA models did not identify new classes. Mortality at day 60 and day 90 was higher in the hyper-inflammatory subphenotype. No differences in outcome were observed between hyper-inflammatory patients randomized to rosuvastatin therapy versus placebo. CONCLUSIONS: LCA using a two-subphenotype model best described the SAILS population. The subphenotypes have features consistent with those previously reported in four other cohorts. Addition of new class-defining variables in the LCA model did not yield additional subphenotypes. No treatment effect was observed with rosuvastatin. These findings further validate the presence of two subphenotypes and demonstrate their utility for patient stratification in ARDS.
Entities:
Keywords:
ARDS; Latent class analysis; Statins; Subphenotypes
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