Using an artificial neural network to predict biliary fistula after pancreaticoduodenal resection

Aim. To determine the risk factors of biliary fistula after pancreaticoduodenal resection.

Materials and methods. 128 pancreaticoduodenal resections were performed in the period of 2018–2023. Biliary fistula was predicted using a neural network and logistic regression. Prediction accuracy was evaluated by ROC analysis (Receiver Operator Characteristics). The DeLong test was used to compare ROC curves.

Results. Biliary fistula developed in 16 patients (12.5%). Univariate analysis showed that risk factors of biliary fistula included the patient's age >70 years, Charlson comorbidity index >7 points, diabetes mellitus, postsurgical anemia, common bile duct diameter <5 mm, and pancreatic fistula. In multivariate analysis, diabetes mellitus, common bile duct diameter <5 mm, and anemia after pancreaticoduodenal resection increased the risk of biliary fistula. A prognostic multivariate model of biliary fistula development, constructed using an artificial neural network demonstrated higher sensitivity (87.5%) and specificity (95.5%) compared to the logistic regression model (68.8% and 90.2%; p = 0.03).

Conclusion. The use of neural networks in predictive analysis of pancreaticoduodenal resection results can increase the efficiency of biliary fistula prediction.

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