Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis

Figures

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  Fig. 1. The flowchart of the present study. PD: peritoneal dialysis

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  Fig. 2. Nomogram for predicting the risk of CVE in patients undergoing PD. The nomogram combines multiple clinical variables to estimate the individualized risk of CVE. BMI: body mass index; LV: left ventricular; 4hD/Pcr: dialysate/plasma creatinine ratio at 4h; CVE: cardiovascular event; PD: peritoneal dialysis

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  Fig. 3. Time-dependent receiver operating characteristic curves for predicting cardiovascular event in the training and validation sets. (A) Nomogram in the training set; (B) XGBoost model in the training set; (C) Random survival forest model in the training set; (D) Nomogram in the validation set; (E) XGBoost model in the validation set; (F) Random survival forest model in the validation set. The area under the curve was calculated at 1, 3, and 5 years to evaluate the models’ discriminative performance

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  Fig. 4. SHAP summary plot for the random survival forest model. (A) The top 15 important features ranked by mean SHAP values, which represent the average contribution of each feature to the model’s prediction. (B) Each patient was represented by a dot, with the x-axis position indicating the SHAP value for the corresponding feature. 4hD/Pcr: dialysate/plasma creatinine ratio at 4h; LV: left ventricular; LA: left atrium; EF: ejection fraction; DM: diabetes mellitus; CVD: cardiovascular disease; IVST: interventricular septum thickness; BMI: body mass index; LVH: left ventricular hypertrophy; LVPWT: left ventricular posterior wall thickness; SHAP: SHapley Additive exPlanations

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  Fig. 5. Cut-off risk score calculated using the maximally selected rank statistics in the validation set. The optimal cut-off point corresponds to the strongest association with incident cardiovascular event. This method was used to divide patients into high-risk and low-risk groups based on their individual predicted risk scores

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  Fig. 6. Kaplan-Meier curve showing the cumulative CVE -free survival probability for high-risk and low-risk groups divided based on the cut-off risk score from the random survival forest model. The log-rank test was used to compare survival between the two groups. CVE: cardiovascular event