Development and validation of an interpretable machine learning model for predicting acute kidney injury after pancreatic surgery

Figure 1 Flow diagram of patient selection. ¹Pancreatic surgery encompassed pancreaticoduodenectomy, distal pancreatectomy, and total pancreatectomy. AKI: Acute kidney injury; CKD: Chronic kidney disease; BMI: Body mass index; KDIGO: Kidney disease improving global outcomes; SCr: Serum creatinine.

Figure 2 Feature selection by Boruta algorithm and least absolute shrinkage and selection operator. A: Through Boruta the 18 filtered variables were as follows: Surgery duration; Intensive care unit admission after surgery; Surgical approach; Intraoperative red blood cell (RBC) transfusion; Tumor length; Postoperative serum creatinine; Postoperative albumin; Intraoperative blood loss; Sex; Postoperative prothrombin time; Age; Postoperative bilirubin; Postoperative activated partial thromboplastin time; Preoperative prothrombin time; Malignancy history; History of stroke; Total input on postoperative day 0; And angiotensin-converting enzyme inhibitor/angiotensin receptor blocker medication; B: Through least absolute shrinkage and selection operator, the 11 filtered variables were as follows: Intensive care unit admission after surgery; Nonsteroidal anti-inflammatory drugs; Malignancy history; History of stroke; Surgery duration; Postoperative white blood cell; Surgical approach; Postoperative serum creatinine; Postoperative bilirubin; Preoperative bilirubin; And intraoperative RBC transfusion. A footnote of 0 (such as variable 0) indicates the preoperative value while a footprint of 1 (such as variable 1) indicates the postoperative value. CVM: Cross-validation mean; Alb: Albumin; APTT: Activated partial thromboplastin time; TBil: Total bilirubin; PT: Prothrombin time; RBC: Red blood cell; ICU: Intensive care unit; SCr: Serum creatinine.

Figure 3 Receiver operating characteristic curves of different models. A: Developing cohort; B: Validation cohort. AUC: Area under the curve; CatBoost: Categorical boosting; KNN: K-nearest neighbor; LightGBM: Light gradient boosting machine; SVM: Support vector machine; XGBoost: Extreme gradient boosting with classification trees.

Figure 4 Calibration curves of different models. A: Developing cohort; B: Validation cohort. CatBoost: Categorical boosting; KNN: K-nearest neighbor; LightGBM: Light gradient boosting machine; SVM: Support vector machine; XGBoost: Extreme gradient boosting with classification trees.

Figure 5 Decision curves of different models. A: Developing cohort; B: Validation cohort. CatBoost: Categorical boosting; KNN: K-nearest neighbor; LightGBM: Light gradient boosting machine; SVM: Support vector machine; XGBoost: Extreme gradient boosting with classification trees.

Figure 6 SHapley Additive exPlanations summary plot for the categorical boosting model. A: Average absolute impact of variables on the final model output magnitude ordered by decreasing feature importance; B: Beeswarm plot of the final model. A footnote of 0 (such as variable 0) means the preoperative value while a footprint of 1 (such as variable 1) means the postoperative value. SCr: Serum creatinine; ICU: Intensive care unit; WBC: White blood cell; RBC: Red blood cell; TBil: Total bilirubin; PT: Prothrombin time; APTT: Activated partial thromboplastin time; Alb: Albumin; NSAIDs: Nonsteroidal anti-inflammatory drugs; ACEI: Angiotensin-converting enzyme inhibitors; ARB: Angiotensin receptor blockers; POD: Postoperative day; SHAP: SHapley Additive exPlanations.

Figure 7 SHapley Additive exPlanations dependence plot of the categorical boosting model. Each panel shows that each feature affects the output of the final model. A: Operative time; B: Postoperative serum creatinine; C: Postoperative white blood cell count; D: Intraoperative red blood cell transfusion; E: Postoperative bilirubin; F: Postoperative prothrombin time; G: Postoperative activated partial thromboplastin time; H: Total input on the day of surgery; I: Postoperative albumin. The X-axis represents the raw values of each feature, and the Y-axis indicates the SHapley Additive exPlanations (SHAP) values of the features. When the SHAP value of a specific feature exceeds zero, it indicates an increased risk of acute kidney injury. A footnote of 0 (such as variable 0) means the preoperative value while a footprint of 1 (such as variable 1) means the postoperative value. SHAP: SHapley Additive exPlanations; SCr: Serum creatinine; WBC: White blood cell; RBC: Red blood cell; TBil: Total bilirubin; PT: Prothrombin time; APTT: Activated partial thromboplastin time; Alb: Albumin; POD: Postoperative day.

Figure 8 Online acute kidney injury risk calculator. AKI: Acute kidney injury; Alb: Albumin; ICU: Intensive care unit; TBil: Total bilirubin; PT: Prothrombin time; SCr: Serum creatinine; CHD: Coronary heart disease.