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Objective <p>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.</p> Methods <p>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.</p> Results <p>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&#xa0;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.</p> Conclusion <p>The RSF model may be a useful method for evaluating CVE risk in PD patients.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月22日

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  • Qiqi Yan,
  • Guiling Liu,
  • Ruifeng Wang,
  • Dandan Li,
  • Xiaoli Chen,
  • Jingjing Cong,
  • Deguang Wang

摘要

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.