Liu Liang, Zhang Liu, Zhang Daohai, Guan Tao, He Ting, Liang Bo, Zhao Jinghong
Department of Nephrology, Chongqing Key Laboratory of Prevention and Treatment of Kidney Disease, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China.
Department of Nephrology, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China.
BMC Nephrol. 2025 Apr 5;26(1):177. doi: 10.1186/s12882-025-04091-6.
Cardiovascular events (CVEs), which refer to a spectrum of conditions including heart attacks, stroke and peripheral vascular disease, are the primary cause of death among peritoneal dialysis (PD) patients, accounting for nearly 40% of deaths. Early identification of high-risk individuals is essential to lessen this burden. Machine learning is particularly suited for this task due to its ability to discern complex, non-linear relationships between various clinical variables, which is essential for accurately predicting CVEs in the context of PD. Our study aimed to develop a predictive machine learning model to identify PD patients at risk of CVEs, offering healthcare providers a tool for proactive intervention.
A total of 251 PD patients were enrolled in the study, with an additional 42 patients included for external validation. Initially, 37 variables were collected but reduced to 25 via Lasso regression. Six supervised machine learning algorithms were evaluated, and XGBoost was chosen as the optimal model based on AUC. Both internal and external validation confirmed the model's efficacy, and a web application was developed using the final XGBoost model, which utilized 12 selected variables.
Among the 251 patients, 40 (15.94%) developed CVEs. The XGBoost model demonstrated an AUC of 0.94 in 5-fold cross-validation. A simplified XGBoost model using 12 variables demonstrated robust prediction capabilities with an AUC of 0.88 in 5-fold cross-validation and 0.78 in external validation. The top five predictors of CVEs were age at catheterization, height, HDL, gender and hemoglobin. According to the SHAP summary plot, older age at catheterization, shorter height, male gender, higher serum HDL and lower hemoglobin levels correlated with increased CVEs risk in PD patients.
The machine learning model, based on 12 key variables, offers an effective tool for predicting CVEs in PD patients, enabling early identification of high-risk cases. This model has been integrated into a web application.
心血管事件（CVE）是指一系列病症，包括心脏病发作、中风和外周血管疾病，是腹膜透析（PD）患者的主要死因，占死亡人数的近40%。早期识别高危个体对于减轻这一负担至关重要。机器学习特别适合这项任务，因为它能够识别各种临床变量之间复杂的非线性关系，这对于在PD背景下准确预测CVE至关重要。我们的研究旨在开发一种预测性机器学习模型，以识别有CVE风险的PD患者，为医疗保健提供者提供一种主动干预的工具。
共有251名PD患者纳入研究，另有42名患者纳入外部验证。最初收集了37个变量，但通过套索回归减少到25个。评估了六种监督机器学习算法，并根据AUC选择XGBoost作为最佳模型。内部和外部验证均证实了该模型的有效性，并使用最终的XGBoost模型开发了一个网络应用程序，该模型使用了12个选定的变量。
在251名患者中，40名（15.94%）发生了CVE。XGBoost模型在五折交叉验证中的AUC为0.94。使用12个变量的简化XGBoost模型显示出强大的预测能力，在五折交叉验证中的AUC为0.88，但在外部验证中的AUC为0.78。CVE的前五个预测因素是置管时年龄、身高、高密度脂蛋白（HDL）、性别和血红蛋白。根据SHAP总结图，置管时年龄较大、身高较矮、男性、血清HDL较高和血红蛋白水平较低与PD患者CVE风险增加相关。
基于12个关键变量的机器学习模型为预测PD患者的CVE提供了一种有效工具，能够早期识别高危病例。该模型已集成到一个网络应用程序中。
