可解释的机器学习算法预测腹膜透析患者的心血管事件。

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

摘要

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

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.

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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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