Emily Van Ark, PhD
Data Scientist
Endpoint Health Inc., United States
Disclosure information not submitted.
Jeff Osborn
Chief Technology Officer
Endpoint Health Inc., United States
Disclosure information not submitted.
Diego Rey, PhD
Chief Scientific Officer
Endpoint Health Inc, United States
Disclosure information not submitted.
Abhijit Duggal, MD, MPH, MSc, FACP
Assistant Professor
Cleveland Clinic Foundation, United States
Disclosure information not submitted.
Bruno Tomazini, MD
Researcher
Hospital Sirio Libanes, United States
Disclosure information not submitted.
Flavia Bueno, PhD
Researcher
Hospital Sirio Libanes, United States
Disclosure information not submitted.
Camila Sampaio
Researcher
Hospital Sirio Libanes, United States
Disclosure information not submitted.
Guilherme Olivato, MD
Researcher
Hospital Albert Einstein, United States
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Adriano Pereira, MD, PhD
Researcher
Hospital Albert Einstein, United States
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Felipe Dal-Pizzol, MD, PhD
Researcher
Hospital São José, United States
Disclosure information not submitted.
Luciano Azevedo, MD, PhD
Assistant Professor
Hospital Sirio Libanes, United States
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Ary Serpa Neto, MD, MSc, PhD
Assistant Professor
Hospital Israelita Albert Einstein and Faculdade De Medicina Do ABC, United States
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Rodrigo Deliberato, MD, MSc, PhD
Head of Clinical Data Science
Endpoint Health Inc., United States
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Title: Subphenotypes in COVID-19 ARDS: Secondary Analysis of a Randomized Clinical Trial
Introduction/Hypothesis: Acute respiratory distress syndrome (ARDS) is a heterogeneous condition. Recently, two subphenotypes were identified using widely available clinical data, one of them exhibiting clinical and laboratory signals typical of a higher inflammation and mortality profile. However, the validation of these subphenotypes within the COVID-19 ARDS population has not yet been reported. The objective of this study was to evaluate if these subphenotypes show similar characteristics in patients with COVID-19 induced ARDS.
Methods: This is a retrospective analysis of CoDEX, a pivotal multicenter randomized clinical trial evaluating dexamethasone against the usual care of critically ill patients with COVID-19 associated ARDS. Patients were classified in two subphenotypes by applying K-means clustering on nine clinical elements collected at randomization (pH, PaO2, bicarbonate, bilirubin, creatinine, mean arterial pressure, heart rate, respiratory rate and FiO2). This model was constructed using data from two large trials (EDEN and FACTT), and validated using data from four trials (ALVEOLI, ARMA, SAILS, and ART). Biological characteristics of the subphenotypes were evaluated by the levels of pro-inflammatory plasma biomarkers. The primary outcome was the identification of two previously validated subphenotypes in the present population. Secondary outcomes was 28-day mortality. Outcomes were compared using a mixed-effect cumulative logistic model.
Results: From the 165 patients analyzed, 103 (62.4%) were identified as subphenotype A and 62 (37.6%) as subphenotype B. Patients in subphenotype B had higher SAPS III and SOFA; higher heart rate, FiO2, ferritin and leukocytes; lower mean arterial pressure; and had more need of vasopressors and renal replacement therapy compared to subphenotype A at randomization. At day 28, patients in subphenotype B higher mortality (54.4% vs 71.0%; p = 0.034) and had less 28 ventilator free days (p = 0.01).
Conclusions: When applying subphenotypes previously found in ARDS cohorts to COVID-19 patients with ARDS, the same differential clinical, laboratorial and outcome characteristics were observed. This is the first study showing that the application of a subphenotype strategy employing only widely available clinical variables is feasible in a cohort of COVID-19 ARDS patients.