Machine learning approach in diagnosis and risk factors detection of pancreatic fistula

Abstract

Abstract Introduction: The aim of the study was to develop a predictive ML model for postoperative pancreatic fistula and to determine the main risk factors of the complication. Materials and Methods: We performed a single-centre retrospective clinical study. 150 patients, who underwent pancreatoduodenal resection in FSBI NMRRC, were included. We developed ML models of biochemic leak and fistula B/C development. Logistic regression, Random forest and CatBoost algorithms were employed. The risk factors were evaluated basing on the most accurate model, roc auc, and Kendall correlation, p<0.05. Results: We detected a significant positive correlation between blood and drain amylase level increase in association with biochemical leak and fistula B/C. The CatBoost algorithm was the most accurate, roc auc 74%-86%. The main pre- and intraoperative prognostic factors of all the fistulas were tumor vascular invasion, age and BMI, roc auc 70%. Specific fistula B/C factors were the same. Basing on the 3-5 days data, biochemical leak and fistula B/C risk factors were blood and drain amylase levels, blood leukocytes, roc auc 86% and 75 %. Conclusion: We developed sufficient quality ML models of postoperative pancreatic fistulas. Blood and drain amylase level increase, tumor vascular invasion, age and BMI were the major risk factors of further fistula B/C development.