Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy

Figure 1 The flow chart. PD: Pancreatoduodenectomy.

Figure 2 Variable filtering and weight allocation. A: Correlation matrix analysis; B: Weight distribution of the candidate variables. BMI: Body mass index; ASA: American Society of Anesthesiologists; CRP: C-reactive protein; WBC: White blood cell; PCT: Procalcitonin; AGR: Albumin-to-globulin ratio; PNI: Prognostic nutrition index; NLR: Neutrophil-to-lymphocyte ratio; NAR: Neutrophil-to-albumin ratio; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; HALP: Hemoglobin level × albumin level × lymphocyte count/platelet count ratio; RFC: Random forest classifier; SVM: Support vector machine; DT: Decision tree; ANN: Artificial neural network; XGboost: Extreme gradient boosting.

Figure 3 Visualization of predictive model based on machine learning algorithm. A: Random forest classifier model; B: Decision tree (DT) model. The candidate factors associated with postoperative pancreatic fistula were ordered via RFC algorithm (A) and (B) prediction node and weight were allocated via DT algorithm. BMI: Body mass index; ASA: American Society of Anesthesiologists; CRP: C-reactive protein; WBC: White blood cell; PCT: Procalcitonin; AGR: Albumin-to-globulin ratio; PNI: Prognostic nutrition index; NLR: Neutrophil-to-lymphocyte ratio; NAR: Neutrophil-to-albumin ratio; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; HALP: Hemoglobin level × albumin level × lymphocyte count/platelet count ratio; RFC: Random forest classifier; SVM: Support vector machine; DT: Decision tree; ANN: Artificial neural network.

Figure 4 Visualization of predictive model based on artificial neural network algorithm. A: Artificial neural network model; B: Variable importance using connection weight. BMI: Body mass index; PCT: Procalcitonin; PNI: Prognostic nutrition index; NAR: Neutrophil-to-albumin ratio; PLR: Platelet-to-lymphocyte ratio; HALP: Hemoglobin level × albumin level × lymphocyte count/platelet count ratio.

Figure 5 Efficiency evaluation of machine learning-based prediction model. A: Decision curve analysis (DCA) of training set; B: DCA of testing set. SVM: Support vector machine; DT: Decision tree; ANN: Artificial neural network; RFC: Random forest classifier; XGboost: Extreme gradient boosting.