Performance of a consensus-based algorithm for diagnosing anastomotic leak after minimally invasive esophagectomy for esophageal cancer

Anastomotic leak (AL) is a common and severe complication after esophagectomy. This study aimed to assess the performance of a consensus-based algorithm for diagnosing AL after minimally invasive esophagectomy. This study used data of the ICAN trial, a multicenter randomized clinical trial comparing cervical and intrathoracic anastomosis, in which a predefined diagnostic algorithm was used to guide diagnosing AL. The algorithm identified patients suspected of AL based on clinical signs, blood C-reactive protein (cut-off value 200 mg/L), and/or drain amylase (cut-off value 200 IU/L). Suspicion of AL prompted evaluation with contrast swallow computed tomography and/or endoscopy to confirm AL. Primary outcome measure was algorithm performance in terms of sensitivity, specificity, and positive and negative predictive values (PPV, NPV), respectively. AL was defined according to the definition of the Esophagectomy Complications Consensus Group. 245 patients were included, and 125 (51%) patients were suspected of AL. The algorithm had a sensitivity of 62% (95% confidence interval [CI]: 46-75), a specificity of 97% (95% CI: 89-100), and a PPV and NPV of 94% (95% CI: 79-99) and 77% (95% CI: 66-86), respectively, on initial assessment. Repeated assessment in 19 patients with persisting suspicion of AL despite negative or inconclusive initial assessment had a sensitivity of 100% (95% CI: 77-100). The algorithm showed poor performance because the low sensitivity indicates the inability of the algorithm to confirm AL on initial assessment. Repeated assessment using the algorithm was needed to confirm remaining leaks.

Automated summary

This study aimed to assess the performance of a consensus-based algorithm for diagnosing anastomotic leak after minimally invasive esophagectomy. The algorithm identified patients suspected of anastomotic leak based on clinical signs and laboratory tests, and further diagnostic tests were used to confirm the leak. The algorithm showed poor performance with low sensitivity on initial assessment, indicating the need for repeated assessment to confirm leaks.