The milestone for preventing post-ERCP pancreatitis using novel simplified predictive scoring system: a propensity score analysis

SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES(2020)

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摘要
Background Post-ERCP pancreatitis (PEP) with trans-papillary approach remains a major issue, and the multi-factorial etiology can lead to the development of unpredictable PEP. Therefore, the early identification of PEP is highly desirable to assist with the health cost containment, the reduction in unnecessary admissions, earlier appropriate primary care, and intensive care for preventing progression of severe pancreatitis. This study aimed to establish a simplified predictive scoring system for PEP. Methods Between January 1, 2012, and December 31, 2019, 3362 consecutive trans-papillary ERCP procedures were retrospectively analyzed. Significant risk factors were extracted by univariate, multivariate, and propensity score analyses, and the probability of PEP in the combinations of each factor were quantified using propensity score analysis. The results were internally validated using bootstrapping resampling. Results In the scoring system with four stratifications using combinations of only five extracted risk factors, the very high-risk group showed 28.79% (95% confidence interval [CI], 18.30%–41.25%; P < 0.001) in the predicted incidence rate of PEP, and 9.09% (95% CI, 3.41%–18.74%; P < 0.001) in that of severe PEP; although the adjusted prevalence revealed 3.74% in PEP and 0.90% in severe PEP, respectively. The prediction model had an area under the curve of 0.86 (95% CI, 0.82–0.89) and the optimism-corrected model as an internal validation had an area under the curve of 0.81 (95% CI, 0.77–0.86). Conclusions We established and validated a simplified predictive scoring system for PEP using five risk factors immediately after ERCP to assist with the early identification of PEP.
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关键词
Endoscopic retrograde cholangiopancreatography (ERCP),Post-ERCP pancreatitis (PEP),Predictive scoring system,Propensity score analysis,Internal validation
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