Co-enrolment in critical care trials: a secondary analysis of the RECOVERY-RS trial
By
Christopher Eleftheriou
Chen Ji
Ranjit Lall
Daniel F. McAuley
Gavin D. Perkins
Keith Couper
November 20, 2025
Clinical Scorecard: Concurrent Participation in Critical Care Research: A Secondary Analysis of the RECOVERY-RS Study
At a Glance
Category Detail
Condition COVID-19 acute hypoxaemic respiratory failure
Key Mechanisms Non-invasive respiratory support strategies including CPAP, HFNO, and conventional oxygen therapy
Target Population Hospitalised adult patients with COVID-19 respiratory failure
Care Setting Critical care units across UK hospitals
Key Highlights
62% of patients in RECOVERY-RS were co-enrolled in another clinical study during the COVID-19 pandemic. Co-enrolment rates varied widely across hospitals, ranging from 25% to 97%. Co-enrolment did not materially influence trial outcomes of tracheal intubation or mortality.
Guideline-Based Recommendations
Diagnosis
Identify patients with COVID-19 acute hypoxaemic respiratory failure eligible for respiratory support trials.
Management
Randomise patients to CPAP, HFNO, or conventional oxygen therapy as per RECOVERY-RS protocol. Consider co-enrolment in multiple studies to optimise trial recruitment in critical care settings.
Monitoring & Follow-up
Monitor outcomes including tracheal intubation and mortality. Track co-enrolment status to assess potential impact on study findings.
Risks
Be aware of regulatory and participant burden challenges associated with co-enrolment. Consider potential influence of co-enrolment on study data interpretation, though evidence suggests minimal impact.
Patient & Prescribing Data
Critically ill COVID-19 patients enrolled in respiratory support trials
Co-enrolment is common and feasible without materially affecting primary trial outcomes.
Clinical Best Practices
Implement co-enrolment strategies to maximise recruitment in critical care research. Ensure ethical and regulatory approvals address co-enrolment considerations. Collect and analyse co-enrolment data to understand its influence on trial results. Engage multidisciplinary teams and patients to support high co-enrolment rates. Use logistic regression or similar methods to evaluate co-enrolment impact on outcomes.
References