Online calculators for predicting the risk of anastomotic stricture after hepaticojejunostomy for bile duct injury after cholecystectomy: a multicenter retrospective study

在线计算器预测胆囊切除术后胆管损伤行肝空肠吻合术后吻合口狭窄风险:一项多中心回顾性研究

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Abstract

BACKGROUND: Anastomotic stricture is a common underlying cause of long-term morbidity after hepaticojejunostomy (HJ) for bile duct injury (BDI) following cholecystectomy. However, there are no methods for predicting stricture risk. This study was aimed at establishing two online calculators for predicting anastomotic stricture occurrence (ASO) and stricture-free survival (SFS) in this patient population. METHODS: The clinicopathological characteristics and follow-up information of patients who underwent HJ for BDI after cholecystectomy from a multi-institutional database were reviewed. Univariate and multivariate analyses of the risk factors of ASO and SFS were performed in the training cohort. Two nomogram-based online calculators were developed and validated by internal bootstrapping resamples ( n =1000) and an external cohort. RESULTS: Among 220 screened patients, 41 (18.64%) experienced anastomotic strictures after a median follow-up of 110.7 months. Using multivariate analysis, four variables, including previous repair, sepsis, HJ phase, and bile duct fistula, were identified as independent risk factors associated with both ASO and SFS. Two nomogram models and their corresponding online calculators were subsequently developed. In the training cohort, the novel calculators achieved concordance indices ( C -indices) of 0.841 and 0.763 in predicting ASO and SFS, respectively, much higher than those of the above variables. The predictive accuracy of the resulting models was also good in the internal ( C -indices: 0.867 and 0.821) and external ( C -indices: 0.852 and 0.823) validation cohorts. CONCLUSIONS: The two easy-to-use online calculators demonstrated optimal predictive performance for identifying patients at high risk for ASO and with dismal SFS. The estimation of individual risks will help guide decision-making and long-term personalized surveillance.

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