Development and validation of machine learning models to predict in-hospital mortality in ICU patients with sepsis and chronic kidney disease

Figures

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  Fig. 1. Flowchart of the study design. ICU, intensive care unit; MIMIC, Medical Information Mort for Intensive Care; eICU-CRD, Telehealth Intensive Care Unit Collaborative Research Database; SHAP, Shapley Additive Explanations

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  Fig. 2. Boruta result for feature selection. Variables were listed on the horizontal axis, and Z-score was shown on the vertical axis. The important features were exhibited in green, the unimportant in red and tentative in yellow. CPD, chronic pulmonary disease; CAD, coronary artery disease; CVD, cerebrovascular disease; RRT, renal replacement therapy; los_hospital, length of hospital stay; los_icu, length of stay in the intensive care unit; max, maximal value; min, minimal value; mean, mean value; wbc, white blood cell; alt, alanine transaminase; ast, aspartate aminotransferase; bun, blood urea nitrogen; sbp, systolic blood pressure; sbp, diastolic blood pressure; mbp, mean arterial pressure; hr, heart rate; spo2, pulse oximeter readings of oxygen saturation

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  Fig. 3. ROC and PR curve of the predictive models. A: ROC of the predictive models. B: P-R curves of the predictive models. 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; Sequential Organ Failure Assessment (SOFA); National Early Warning Score (NEWS); Modified Early Warning Score (MEWS); AUC: area under the Receiver Operating Characteristic

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  Fig. 4. ROC, calibration plot and DCA curve of XGBoost in external validation cohort (eICU-CRD). ROC, receiver operating characteristic; AUC: area under the Receiver Operating Characteristic; DCA, decision curve analysis, XGBoost, Extreme Gradient Boosting; eICU-CRD, Telehealth Intensive Care Unit Collaborative Research Database

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  Fig. 5. SHARP summary plot for the top 20 features of the XGBoost model. A: Bar charts that rank the top 20 significant variables most correlated to in-hospital mortality; B: Impact of each feature on the in-hospital mortality. XGBoost, Extreme Gradient Boosting; SHAP, Shapley Additive Explanations; min, minimum value; max, maximum value; mean, average value; spo2, oxyhemoglobin saturation; sbp, systolic blood pressure; wbc, white blood cell; los_hospital, length of hospital stay; hr, heart rate; los_icu, length of stay in intensive care unit

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  Fig. 6. SHAP force and dependence plot for the XGBoost model. A and B: SHAP force plot of a sample in the validation cohort. The color represents the contributions of each feature, with red being positive and blue being negative. C: SHAP dependence plot for the top 9 features in the XGBoost model. XGBoost, Extreme Gradient Boosting; SHAP, Shapley Additive Explanations; min, minimum value; max, maximum value; mean, average value; spo2, oxyhemoglobin saturation; sbp, systolic blood pressure; wbc, white blood cell; los_hospital, length of hospital stay; hr, heart rate; los_icu, length of stay in intensive care unit