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Randomized Controlled Trial
. 2024 Jan 3;111(1):znad370.
doi: 10.1093/bjs/znad370.

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

ESCP EAGLE Safe Anastomosis Collaborative and NIHR Global Health Research Unit in Surgery
Collaborators
Randomized Controlled Trial

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

ESCP EAGLE Safe Anastomosis Collaborative and NIHR Global Health Research Unit in Surgery. Br J Surg. .

Abstract

Background: Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks.

Methods: The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned.

Results: A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31).

Conclusion: Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov).

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Figures

Fig. 1
Fig. 1
Modified PICO graph for EAGLE study *Harmonizing of intraoperative techniques is a suite of learning materials, discussions, and operative videos that explore the challenges that may face surgeons during operation and supports surgeons’ decision-making by presenting the best evidence available on how to tackle these challenges. PICO, patients, intervention, comparator, outcome.
Fig. 2
Fig. 2
Trial design schematic *Randomization stratified by number of beds, referral or non-referral hospital, country income. †Minimum of 18 hospitals per batch to be ready for randomization.
Fig. 3
Fig. 3
Hospital-level CONSORT diagram
Fig. 4
Fig. 4
Patient-level CONSORT diagram
Fig. 5
Fig. 5
Forest plot showing effect of intervention on anastomotic leak rate Meta-analysis was conducted across all 16 batches of the study. Each batch was analysed using a mixed-effects logistic regression model adjusting for hospital number of beds, country income, type of hospital (referral versus non-referral), sex, urgency, and data collection interval. Hospital was included as a random effect. Overall effect pooled in a random-effects meta-analysis using the inverse-variance approach of DerSimonian and Laird. ORs are shown with 95% confidence intervals.
Fig. 6
Fig. 6
Subgroup analyses of primary outcome, analysed across all batches ORs and P values were estimated from a three-level mixed-effects logistic regression model adjusting for hospital number of beds, country income, type of hospital (referral versus non-referral), sex, urgency, and data collection interval. Hospital and batch were included as random effects, with hospital nested within batch. *Proportion of surgeons in each hospital-team completing online modules during the implementation of the intervention. Values in parentheses are percentages unless indicated otherwise; ORs are shown with 95% confidence intervals. h, Hospital-level factors; p, patient-level factors. Results split by batch are available in Tables S10–S22.
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