• Yazmin Johari, William Catchlove, Madeleine Tse, Kalai Shaw, Eldho Paul, Richard Chen, Damien Loh, Andrew Packiyanathan, Paul Burton, Peter Nottle, Samantha Ellis, and Wendy Brown.
• Monash University Department of Surgery, Central Clinical School, Monash University, Melbourne, Australia.
• Ann. Surg. 2022 Feb 1; 275 (2): e401-e409.

ObjectivesTo develop and validate a classification of sleeve gastrectomy leaks able to reliably predict outcomes, from protocolized computed tomography (CT) findings and readily available variables.Summary Of Background DataLeaks post sleeve gastrectomy remain morbid and resource-consuming. Incidence, treatments, and outcomes are variable, representing heterogeneity of the problem. A predictive tool available at presentation would aid management and predict outcomes.MethodsFrom a prospective database (2009-2018) we reviewed patients with staple line leaks. A Delphi process was undertaken on candidate variables (80-20). Correlations were performed to stratify 4 groupings based on outcomes (salvage resection, length of stay, and complications) and predictor variables. Training and validation cohorts were established by block randomization.ResultsA 4-tiered classification was developed based on CT appearance and duration postsurgery. Interobserver agreement was high (κ = 0.85, P < 0.001). There were 59 patients, (training: 30, validation: 29). Age 42.5 ± 10.8 versus 38.9 ± 10.0 years (P = 0.187); female 65.5% versus 80.0% (P = 0.211), weight 127.4 ± 31.3 versus 141.0 ± 47.9 kg, (P = 0.203). In the training group, there was a trend toward longer hospital stays as grading increased (I = 10.5 d; II = 24 d; III = 66.5 d; IV = 72 d; P = 0.005). Risk of salvage resection increased (risk ratio grade 4 = 9; P = 0.043) as did complication severity (P = 0.027).Findings were reproduced in the validation group: risk of salvage resection (P = 0.007), hospital stay (P = 0.001), complications (P = 0.016).ConclusionWe have developed and validated a classification system, based on protocolized CT imaging that predicts a step-wise increased risk of salvage resection, complication severity, and increased hospital stay. The system should aid patient management and facilitate comparisons of outcomes and efficacy of interventions.Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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