基于 Klotho 的机器学习模型用于预测慢性肾脏病患者的肾脏和心血管预后

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yating Wang, Yu Shi, Tangli Xiao, Xianjin Bi, Qingyu Huo, Shaobo Wang, Jiachuan Xiong, Jinghong Zhao
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引用次数: 0

摘要

背景:本研究旨在开发和验证一种基于血清Klotho的机器学习(ML)模型,用于预测慢性肾脏病(CKD)患者的终末期肾脏病(ESKD)和心血管疾病(CVD):使用 400 名非透析 CKD 患者队列训练了五个不同的 ML 模型,以预测三个不同时间点(3 年、5 年和 8 年)的 ESKD 和 CVD 风险。数据集分为训练集(70%)和内部验证集(30%)。这些模型参考了包括血清 Klotho 在内的 47 个临床特征数据。筛选出表现最佳的模型,用于确定每种结果的风险因素。使用各种指标对模型性能进行评估:研究结果表明,Lasso 回归模型预测 ESKD 的准确率最高(C 指数=0.71)。该模型的主要特征包括估计肾小球滤过率(eGFR)、24 小时尿微量白蛋白、血清白蛋白、磷酸盐、甲状旁腺激素和血清 Klotho,其曲线下面积(AUC)最高,为 0.930(95% CI:0.897-0.962)。此外,在心血管疾病风险预测方面,随机生存森林(RSF)模型的准确度最高(C-index=0.66),AUC 最高,达到 0.782(95% CI:0.633-0.930)。该模型的主要特征包括年龄、原发性高血压病史、血钙、肿瘤坏死因子-α和血清 Klotho:我们成功开发并验证了基于 Klotho 的 CKD 患者心血管疾病和 ESKD 的 ML 风险预测模型,该模型性能良好,表明其具有很高的临床实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Klotho-based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease
Background: This study aimed to develop and validate a machine learning (ML) model based on serum Klotho for predicting end-stage kidney disease (ESKD) and cardiovascular disease (CVD) in patients with chronic kidney disease (CKD). Methods: Five different ML models were trained to predict the risk of ESKD and CVD at three different time points (3, 5, and 8-year) using a cohort of 400 non-dialysis CKD patients. The dataset was divided into a training set (70%) and an internal validation set (30%). These models were informed by data comprising 47 clinical features, including serum Klotho. The best-performing model was selected and used to identify risk factors for each outcome. Model performance was assessed using various metrics. Results: The findings showed that the Lasso regression model had the highest accuracy (C-index=0.71) in predicting ESKD. The features mainly included in this model were estimated glomerular filtration rate (eGFR), 24-hour urinary microalbumin, serum albumin, phosphate, parathyroid hormone, and serum Klotho, which achieved the highest area under the curve (AUC) of 0.930 (95% CI: 0.897-0.962). In addition, for the CVD risk prediction, the Random Survival Forest (RSF) model with the highest accuracy (C-index=0.66) was selected and achieved the highest AUC of 0.782 (95% CI: 0.633-0.930). The features mainly included in this model were age, history of primary hypertension, calcium, tumor necrosis factor-alpha, and serum Klotho. Conclusion: We successfully developed and validated Klotho-based ML risk prediction models for CVD and ESKD in CKD patients with good performance, indicating their high clinical utility.
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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