Use of the Win Ratio Analysis in Critical Care Trials

• Published in: American journal of respiratory and critical care medicine Vol. 209; no. 7; pp. 798 - 804
• Main Authors: Monzo, Luca, Levy, Bruno, Duarte, Kevin, Baudry, Guillaume, Combes, Alain, Ouattara, Alexandre, Delmas, Clément, Kimmoun, Antoine, Girerd, Nicolas
• Format: Journal Article
• Language: English
• Published: United States American Thoracic Society 01.04.2024
• Subjects: Algorithms; Clinical trials; Critical Care; Humans; Life Sciences; Outcome Assessment, Health Care; Primary care; Reliability; clinical trial; critical care; heart failure; cardiogenic shock; cardiovascular diseases; Conception and design: L.M; A.O; A.C; C.D. and A.K. Analysis and interpretation: K.D; L.M; G.B; B.L. and N.G. Data collection: B.L; Conception and design: L.M. B.L. and N.G. Data collection: B.L. A.C. A.O. C.D. and A.K. Analysis and interpretation: K.D. L.M. G.B. and N.G. Drafting the; and N.G. Drafting the
• ISSN: 1073-449X; 1535-4970; 1535-4970
• DOI: 10.1164/rccm.202309-1644CP

Composite outcomes are commonly used in critical care trials to estimate the treatment effect of an intervention. A significant limitation of classical analytic approaches is that they assign equal statistical importance to each component in a composite, even if these do not have the same clinical importance (i.e., in a composite of death and organ failure, death is clearly more important). The win ratio (WR) method has been proposed as an alternative for trial outcomes evaluation, as it effectively assesses events based on their clinical relevance (i.e., hierarchical order) by comparing each patient in the intervention group with their counterparts in the control group. This statistical approach is increasingly used in cardiovascular outcome trials. However, WR may be useful to unveil treatment effects also in the critical care setting, because these trials are typically moderately sized, thus limiting the statistical power to detect small differences between groups, and often rely on composite outcomes that include several components of different clinical importance. Notably, the advantages of this approach may be offset by several drawbacks (such as ignoring ties and difficulties in selecting and ranking endpoints) and challenges in appropriate clinical interpretation (i.e., establishing clinical meaningfulness of the observed effect size). In this perspective article, we present some key elements to implementing WR statistics in critical care trials, providing an overview of strengths, drawbacks, and potential applications of this method. To illustrate, we conduct a reevaluation of the HYPO-ECMO (Hypothermia during Venoarterial Extracorporeal Membrane Oxygenation) trial using the WR framework as a case example.