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

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Qiqi Yan, Guiling Liu, Ruifeng Wang, Dandan Li, Xiaoli Chen, Jingjing Cong, Deguang Wang
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引用次数: 0

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.

可解释的机器学习算法预测腹膜透析患者的心血管事件。
目的:比较基于机器学习算法和Cox比例风险回归的腹膜透析(PD)患者心血管事件(CVE)预测模型的性能。方法:本研究纳入2010年1月1日至2022年7月31日在我中心行PD导尿术的患者。患者按7:3的比例随机分为训练组和验证组。使用训练集建立Cox回归、极端梯度增强(XGBoost)和随机生存森林(RSF)模型,并使用验证集进行验证。采用随时间变化的曲线下面积(AUC)和一致性指数(C-index)来评价预测模型的判别能力。结果:共纳入318例患者。110例(34.6%)患者在中位随访31(1656)个月期间发生CVE。RSF模型具有较好的预测性能,其c指数为0.725,验证集中1年、3年和5年时间相关的AUC分别为0.812、0.836和0.706。确定的前5个重要变量是血小板计数、年龄、4hd /Pcr、左心房直径和左心室直径。根据验证集中选取的最大秩统计量计算的截止风险评分,将患者分为高危组和低危组。log-rank检验显示,两组患者累计无cve生存率差异有统计学意义。结论:RSF模型可作为评估PD患者CVE风险的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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