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Quadrant, necrosis, and infection criteria for the risk stratification of walled-off necrosis: external validation
Hideyuki Shiomi1, Tomotaka Saito2, Tsuyoshi Hamada3
1Division of Hepatobiliary and Pancreatic Diseases, Department of Gastroenterology, Hyogo Medical University, Hyogo, Japan.
Background And Aims:
EUS-guided peripancreatic fluid drainage (EUS-PFD) with on-demand endoscopic necrosectomy, increasingly used to manage walled-off necrosis (WON), is associated with substantial morbidity and mortality. This multicenter study aimed to externally validate recently developed quadrant (an abdominal quadrant distribution), necrosis, and infection (QNI) criteria for risk stratification in this setting.
Methods:
Of 423 patients with pancreatic fluid collections treated in a large multi-institutional cohort between 2010 and 2020, a total of 212 with available preprocedural CT images were included. Clinical outcomes between high- and low-risk groups defined according to QNI criteria were compared.
Results:
The clinical success rate did not differ significantly between the QNI-high and QNI-low groups (74.4% vs 83.5%, respectively; P = .12). Time to clinical success was longer in the QNI-high group compared with the QNI-low group (median, 68 vs 46 days; P = .0016). Compared with the QNI-low group, the QNI-high group was associated with higher rates of percutaneous interventions, direct endoscopic necrosectomy, mortality, and adverse events. The QNI-high group was independently associated with time to clinical success (hazard ratio, .64; 95% confidence interval, .46-.87; P = .005) in multivariable Cox proportional hazards regression analysis and with the risk of grade III or higher adverse events (odds ratio, 2.93; 95% confidence interval, 1.04-8.20; P = .04) in multivariable logistic regression analysis.
Conclusions:
The QNI criteria effectively stratified time to clinical success and the risk of adverse outcomes for patients receiving EUS-PFD of WON. Further investigations could elucidate appropriate management strategies based on QNI-based risk stratification.
