BACKGROUND Pancreatic fistula is the most common complication of pancreatic surgeries that causes more serious conditions,including bleeding due to visceral vessel erosion and peritonitis.AIM To develop a machine lear...BACKGROUND Pancreatic fistula is the most common complication of pancreatic surgeries that causes more serious conditions,including bleeding due to visceral vessel erosion and peritonitis.AIM To develop a machine learning(ML)model for postoperative pancreatic fistula and identify significant risk factors of the complication.METHODS A single-center retrospective clinical study was conducted which included 150 patients,who underwent pancreat-oduodenectomy.Logistic regression,random forest,and CatBoost were employed for modeling the biochemical leak(symptomless fistula)and fistula grade B/C(clinically significant complication).The performance was estimated by receiver operating characteristic(ROC)area under the curve(AUC)after 5-fold cross-validation(20%testing and 80%training data).The risk factors were evaluated with the most accurate algorithm,based on the parameter“Importance”(Im),and Kendall correlation,P<0.05.RESULTS The CatBoost algorithm was the most accurate with an AUC of 74%-86%.The study provided results of ML-based modeling and algorithm selection for pancreatic fistula prediction and risk factor evaluation.From 14 parameters we selected the main pre-and intraoperative prognostic factors of all the fistulas:Tumor vascular invasion(Im=24.8%),age(Im=18.6%),and body mass index(Im=16.4%),AUC=74%.The ML model showed that biochemical leak,blood and drain amylase level(Im=21.6%and 16.4%),and blood leukocytes(Im=11.2%)were crucial predictors for subsequent fistula B/C,AUC=86%.Surgical techniques,morphology,and pancreatic duct diameter less than 3 mm were insignificant(Im<5%and no correlations detected).The results were confirmed by correlation analysis.CONCLUSION This study highlights the key predictors of postoperative pancreatic fistula and establishes a robust ML-based model for individualized risk prediction.These findings contribute to the advancement of personalized periop-erative care and may guide targeted preventive strategies.展开更多
文摘BACKGROUND Pancreatic fistula is the most common complication of pancreatic surgeries that causes more serious conditions,including bleeding due to visceral vessel erosion and peritonitis.AIM To develop a machine learning(ML)model for postoperative pancreatic fistula and identify significant risk factors of the complication.METHODS A single-center retrospective clinical study was conducted which included 150 patients,who underwent pancreat-oduodenectomy.Logistic regression,random forest,and CatBoost were employed for modeling the biochemical leak(symptomless fistula)and fistula grade B/C(clinically significant complication).The performance was estimated by receiver operating characteristic(ROC)area under the curve(AUC)after 5-fold cross-validation(20%testing and 80%training data).The risk factors were evaluated with the most accurate algorithm,based on the parameter“Importance”(Im),and Kendall correlation,P<0.05.RESULTS The CatBoost algorithm was the most accurate with an AUC of 74%-86%.The study provided results of ML-based modeling and algorithm selection for pancreatic fistula prediction and risk factor evaluation.From 14 parameters we selected the main pre-and intraoperative prognostic factors of all the fistulas:Tumor vascular invasion(Im=24.8%),age(Im=18.6%),and body mass index(Im=16.4%),AUC=74%.The ML model showed that biochemical leak,blood and drain amylase level(Im=21.6%and 16.4%),and blood leukocytes(Im=11.2%)were crucial predictors for subsequent fistula B/C,AUC=86%.Surgical techniques,morphology,and pancreatic duct diameter less than 3 mm were insignificant(Im<5%and no correlations detected).The results were confirmed by correlation analysis.CONCLUSION This study highlights the key predictors of postoperative pancreatic fistula and establishes a robust ML-based model for individualized risk prediction.These findings contribute to the advancement of personalized periop-erative care and may guide targeted preventive strategies.
