A systematic review and meta-analysis of enrollment into ARDS and sepsis trials published between 2009 and 2019 in major journals

Abstract

Abstract Background Enrollment problems are common among randomized controlled trials conducted in the ICU. However, little is known about actual trial enrollment rates and influential factors. We set out to determine the overall enrollment rate in recent randomized controlled trials (RCTs) of patients with acute respiratory distress syndrome (ARDS), acute lung injury (ALI), or sepsis, and which factors influenced enrollment rate. Methods We conducted a systematic review by searching Pubmed using predefined terms for ARDS/ALI and sepsis to identify individually RCTs published among the seven highest impact general medicine and seven highest impact critical care journals between 2009 and 2019. Cluster randomized trials were excluded. Data were extracted by two independent reviewers using an electronic database management system. We conducted a random-effects meta-analysis of the eligible trials for the primary outcome of enrollment rate by time and site. Results Out of 457 articles identified, 94 trials met inclusion criteria. Trials most commonly evaluated pharmaceutical interventions (53%), were non-industry funded (78%), and required prospective informed consent (81%). The overall mean enrollment rate was 0.83 (95% confidence interval: 0.57–1.21) participants per month per site. Enrollment in ARDS/ALI and sepsis trials were 0.48 (95% CI 0.32–0.70) and 0.98 (95% CI 0.62–1.56) respectively. The enrollment rate was significantly higher for single-center trials (4.86; 95% CI 2.49–9.51) than multicenter trials (0.52; 95% CI 0.41–0.66). Of the 36 trials that enrolled < 95% of the target sample size, 8 (22%) reported slow enrollment as the reason. Conclusions In this systematic review and meta-analysis, recent ARDS/ALI and sepsis clinical trials had an overall enrollment rate of less than 1 participant per site per month. Novel approaches to improve critical care trial enrollment efficiency are needed to facilitate the translation of best evidence into practice.