Risk prediction of biliary infection after endoscopic drainage for malignant perihilar biliary obstruction: A 10-year...

As a prominent drainage method, endoscopic retrograde cholangiopancreatography (ERCP) with stenting has been universally employed to treat malignant perihilar biliary obstruction (MPHBO). Nonetheless, postoperative biliary infection (PBI) constitutes a remarkable complication associated with this procedure, which can result in fatal outcomes under some circumstances.

To investigate the risk factors and predict the occurrence of PBI following ERCP drainage in patients suffering from MPHBO.

This retrospective study analyzed data from patients who underwent ERCP drainage at three different centers. Independent risk factors for PBI were identified by adopting multivariate analyses. Logistic regression model and artificial neural network (ANN) models were developed and validated to predict PBI.

A total of 288 patients who underwent 403 ERCP procedures were included in the study. The incidence of PBI was 39% (158/403). As evidently demonstrated by multivariate analysis, the Bismuth-Corlett classification (odds ratio [OR] = 1.412; 95% confidence interval [CI]: 1.144-1.743; P = 0.001), hypokalemia (OR = 4.080; 95%CI: 1.958-8.505; P < 0.001), and aspartate transaminase (AST) (OR = 1.003; 95%CI: 1.000-1.006; P = 0.021) were independent risk factors for PBI. Simultaneously, both a logistic regression model (area under the curve [AUC] = 0.734) and an ANN model (AUC = 0.867) were developed by adopting these factors. As suggested by a validation through 45 additional cases, the ANN model demonstrated an AUC of 0.940, surpassing the logistic regression model's AUC of 0.791.

The Bismuth-Corlett classification, hypokalemia, and AST levels were identified as independent risk factors for PBI following ERCP drainage. The ANN model was proven to be an effective approach for the anticipation of the PBI occurrence.