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.
Abhijit Duggal, MD, MPH, MSc, FACP
Assistant Professor
Cleveland Clinic Foundation, United States
Disclosure information not submitted.
Ary Serpa Neto, MD, MSc, PhD
Assistant Professor
Hospital Israelita Albert Einstein and Faculdade De Medicina Do ABC, United States
Disclosure information not submitted.
Diego Rey, PhD
Chief Scientific Officer
Endpoint Health Inc, United States
Disclosure information not submitted.
Rodrigo Deliberato, MD, MSc, PhD
Head of Clinical Data Science
Endpoint Health Inc., United States
Disclosure information not submitted.
Title: Heterogeneity of Effect of Nutrition Strategies by Subphenotypes Derived from Clinical Data in ARDS
Introduction/Hypothesis: Acute respiratory distress syndrome (ARDS) is a heterogeneous condition. Recently, two subphenotypes using widely available clinical data were identified. Subphenotype B exhibits clinical and laboratory signals compatible with higher inflammation and has higher mortality compared to subphenotype A. The presence of heterogeneity of treatment effect (HTE) between these subphenotypes has not been explored. The objective of this study was to assess the HTE of different nutrition strategies on 60-day mortality according to these subphenotypes.
Methods: Retrospective evaluation of the EDEN trial, that compared full vs. trophic enteral feeding. Patients were classified in two subphenotypes using a K-means clustering algorithm 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 the two large trials (EDEN and FACTT), and validated using data from four trials (ALVEOLI, ARMA, SAILS, and ART). Biological characteristics of the subphenotypes were validated by evaluating the levels of pro-inflammatory plasma biomarkers. The primary outcome was 60-day mortality. HTE followed a pre-planned Bayesian hierarchical logistic model and is reported as odds ratio (OR), 95% credible interval (CrI) and probability of benefit (OR < 1.00). Priors were weakly informative, and analyses considering different priors were performed.
Results: Data from 777 ARDS patients were analyzed. There was a different probability of benefit of full enteral feeding in subphenotype A (OR, 0.78 [95% CrI, 0.49 to 1.22], probability of benefit of 86.3%) compared to subphenotype B (OR, 1.05 [95% CrI, 0.66 to 1.67], probability of benefit of 42.1%). The probability that assignment to full enteral feeding group results in lower OR for 60-day mortality in patients in subphenotype B, compared to subphenotype A, was 18.3%. The use of different priors did not change these findings.
Conclusions: Our study demonstrates small HTE of full enteral feeding in ARDS patients across two subphenotypes derived from common clinical variables. The probability of better outcomes with full enteral feeding in more inflamed patients was 18.3%. This work may allow predictive enrichment in future clinical trials.