Optimizing invasive strategies for necrotizing pancreatitis: A Bayesian network analysis of randomized controlled trials

优化坏死性胰腺炎的侵入性治疗策略:基于随机对照试验的贝叶斯网络分析

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Abstract

BACKGROUND: necrotizing pancreatitis is a severe complication of acute pancreatitis, often requiring invasive interventions to manage its high mortality and morbidity. The optimal timing and type of invasive procedures remain uncertain, necessitating a systematic evaluation to guide clinical decision-making. METHODS: A systematic review and Bayesian network meta-analysis were conducted following the PRISMA guidelines. Relevant randomized controlled trials (RCTs) published up to November 2024 were retrieved from PubMed, EMBASE, and the Cochrane Library. The study assessed 10 invasive interventions, including early and delayed drainage, step-up approaches, and open surgeries, focusing on mortality and major complications. Statistical analysis employed random-effects models and Bayesian frameworks to synthesize direct and indirect evidence. RESULTS: Fifteen RCTs involving 857 patients were included. Delayed step-up surgery (DSU) and early drainage (ED) with lavage (EDL) demonstrated significant survival benefits, with lower mortality rates and reduced complications. Conversely, delayed video-assisted surgery (DVS) was associated with the highest mortality. No statistically significant differences were observed between ED and EDL or ED and delayed drainage in direct comparisons. Subgroup analyses revealed no significant mortality difference between early and delayed interventions (OR = 1.15, 95% CI = 0.54-2.46), while EDL and DSU emerged as optimal strategies in early and delayed interventions, respectively. CONCLUSION: This review and network meta-analysis suggests that DSU and EDL may be promising options for treating necrotizing pancreatitis, though current evidence is inconclusive. Given varying risks, especially with DVS, treatment should be tailored to individual cases. More high-quality RCTs are needed to strengthen the evidence and guide practice.

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