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

Abstract

Objective: To compare the performance of predictive models for cardiovascular event (CVE) in patients undergoing peritoneal dialysis (PD) based on machine learning algorithm and Cox proportional hazard regression.

Methods: This study included patients underwent PD catheterization in our center from January 1, 2010, to July 31, 2022. The patients were randomly divided into training and validation sets in a 7:3 ratio. Cox regression, extreme gradient boosting (XGBoost), and random survival forest (RSF) models were developed using the training set and validated using the validation set. The time-dependent area under the curve (AUC) and concordance index (C-index) were used to evaluate the discriminative ability of predictive models.

Results: A total of 318 patients were enrolled in this study. 110 (34.6%) patients developed CVE during the median follow-up of 31(16,56) months. The RSF model had better predictive performance, with a C-index of 0.725 and 1-, 3-, and 5-year time-dependent AUC of 0.812, 0.836, and 0.706 in the validation set, respectively. The top 5 important variables identified were platelet count, age, 4 hD/Pcr, left atrium diameter, and left ventricular diameter. Patients were classified into high-risk and low-risk groups based on the cut-off risk score calculated using the maximally selected rank statistics in the validation set. The log-rank test showed a significant difference in cumulative CVE-free survival probability between the two groups.

Conclusion: The RSF model may be a useful method for evaluating CVE risk in PD patients.

Keywords: Cardiovascular event; Machine learning; Peritoneal dialysis; Predictive model; Random survival forest.

Fig. 1

The flowchart of the present study. PD: peritoneal dialysis

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

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

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

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

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