The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models

Figures

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  Fig. 1. Flowchart of patient selection from MIMIC-IV and eICU-CRD database. MIMIC Medical Information Mort for Intensive Care, eICU-CRD Telehealth Intensive Care Unit Collaborative Research Database

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  Fig. 2. Feature selection analyzed by Boruta algorithm. The horizontal axis is the name of each variable, and the vertical axis is the Z-value of each variable. The box plot shows the Z-value of each variable in the model calculation. The green boxes represent the 76 important variables, the yellow represents tentative attributes, and the red represents unimportant variables. los_icu length of stay in intensive care unit, scr serum creatinine, eGFR estimated glomerular filtration rate, CKD chronic kidney disease, ACS acute coronary syndrome, HT hypertension, PCI percutaneous coronary intervention, CABG coronary artery bypass grafting, NOAC Non-vitamin K Antagonist Oral Anticoagulant, CRRT continuous renal replacement therapy, max maximum, min minimum, WBC white blood cell, RBC red blood cell, ALT alanine aminotransferase, AST aspartate aminotransferase, ALP alkaline phosphatase, BUN blood urea nitrogen, INR International Normalized Ratio, PT prothrombin time, PTT partial thromboplastin time, SOFA sequential organ failure assessment, sbp systolic blood pressure, dbp diastolic blood pressure, mbp mean blood pressure, HR heart rate, spo2 oxyhemoglobin saturation

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  Fig. 3. Discrimination performance of eight machine learning models. A ROC of eight machine learning models. B P-R curves of eight machine learning models. The GBDT algorism exhibited the best performance both in ROC and P-R curves. ROC Receiver Operating Characteristic, P-R curve precision/recall curve, SVM support vector machine, GBDT Gradient Boosting Decision Tree Machine, KNN k-nearest neighbors, NN neural network, XGBoost Extreme Gradient Boosting, AUC area under the curve

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  Fig. 4. SHAP analysis result. A Bar charts that rank the importance of the top 20 significant variables most correlated to in-hospital death in GBDT model. B Impact of each feature on the in-hospital mortality in GBDT model by SHAP values. GBDT Gradient Boosting Decision Tree Machine, SHAP Shapley Additive Explanations, spo2 oxyhemoglobin saturation, HR heart rate, WBC white blood cell, CABG coronary artery bypass grafting, SOFA sequential organ failure assessment, sbp systolic blood pressure, BUN blood urea nitrogen, PTT partial thromboplastin time, ALT alanine aminotransferase, AST aspartate aminotransferase, PT prothrombin time

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  Fig. 5. External validation for the GBDT model in the eICU-CRD dataset. A DCA curve of the GBDT model in external validation. B calibration curve of the GBDT model in external validation. C ROC of the GBDT model in external validation. D P-R curves of the GBDT models in external validation. DCA showed the GBDT model had some net benefit compared with the “treat-none” or “treat-all” strategies with a certain degree of clinical utility. The AUC (0.865) and AP (0.672) results demonstrated the GBDT model had good predictive values in external validation. DCA decision curve analysis, ROC Receiver Operating Characteristic, P-R curve precision/recall curve, GBDT Gradient Boosting Decision Tree Machine, eICU-CRD Telehealth Intensive Care Unit Collaborative Research Database