For a study, the researchers sought to create and validate a classification of sleeve gastrectomy leaks based on protocolized computed tomography (CT) findings and widely functional characteristics that can be used to predict outcomes. Investigators looked at patients with staple line leakage from a prospective database (2009–2018). A Delphi process was used on candidate variables (80–20). Based on outcomes (salvage resection, length of stay, and complications) and predictor variables, correlations were used to stratify 4 groups. Block randomization was used to create the training and validation cohorts. Based on the look of the CT scan and the length of time after surgery, a 4-tiered classification was created. There was a high level of interobserver agreement (κ=0.85, P<0.001). There were 59 patients in total (training: 30, validation: 29). Weight somewhere between 127.4 and 31.3 compared to somewhere between 141.0 and 47.9 kg (P=0.203), age somewhere between 42.5 and 10.8 versus somewhere between 38.9 and 10.0 years (P=0.187), female 65.5% versus 80.0% (P=0.211). There was a trend toward longer hospital stays in the training group as grading progressed (I=10.5 days; II=24 days; III=66.5 days; IV=72 days; P=0.005). Salvage resection risk rose (risk ratio grade 4 = 9; P=0.043), as did the severity of consequence (P=0.027). The risk of salvage resection (P=0.007), hospital stay (P=0.001), and complications (P=0.016) was also replicated in the validation group. Study group created and validated a categorization system based on protocolized CT imaging that predicts a step-by-step rise in the risk of salvage resection, complication severity, and length of stay in the hospital. The system should make patient care easier and allow for comparisons of outcomes and intervention efficacy.
