老年急性肾损伤住院患者的主要不良肾脏事件：基于机器学习的模型开发与验证研究

Major Adverse Kidney Events in Hospitalized Older Patients With Acute Kidney Injury: Machine Learning-Based Model Development and Validation Study.

作者信息

Luo Xiao-Qin, Zhang Ning-Ya, Deng Ying-Hao, Wang Hong-Shen, Kang Yi-Xin, Duan Shao-Bin

机构信息

Department of Geriatrics, The Second Xiangya Hospital of Central South University, Changsha, China.

Information Center, The Second Xiangya Hospital of Central South University, Changsha, China.

出版信息

J Med Internet Res. 2025 Jan 3;27:e52786. doi: 10.2196/52786.

DOI:10.2196/52786

PMID:39752664

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11748444/

Abstract

BACKGROUND

Acute kidney injury (AKI) is a common complication in hospitalized older patients, associated with increased morbidity, mortality, and health care costs. Major adverse kidney events within 30 days (MAKE30), a composite of death, new renal replacement therapy, or persistent renal dysfunction, has been recommended as a patient-centered endpoint for clinical trials involving AKI.

OBJECTIVE

This study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.

METHODS

A total of 4266 older patients (aged ≥ 65 years) with AKI admitted to the Second Xiangya Hospital of Central South University from January 1, 2015, to December 31, 2020, were included and randomly divided into a training set and an internal test set in a ratio of 7:3. An additional cohort of 11,864 eligible patients from the Medical Information Mart for Intensive Care Ⅳ database served as an external test set. The Boruta algorithm was used to select the most important predictor variables from 53 candidate variables. The eXtreme Gradient Boosting algorithm was applied to establish a prediction model for MAKE30. Model discrimination was evaluated by the area under the receiver operating characteristic curve (AUROC). The SHapley Additive exPlanations method was used to interpret model predictions.

RESULTS

The overall incidence of MAKE30 in the 2 study cohorts was 28.3% (95% CI 26.9%-29.7%) and 26.7% (95% CI 25.9%-27.5%), respectively. The prediction model for MAKE30 exhibited adequate predictive performance, with an AUROC of 0.868 (95% CI 0.852-0.881) in the training set and 0.823 (95% CI 0.798-0.846) in the internal test set. Its simplified version achieved an AUROC of 0.744 (95% CI 0.735-0.754) in the external test set. The SHapley Additive exPlanations method showed that the use of vasopressors, mechanical ventilation, blood urea nitrogen level, red blood cell distribution width-coefficient of variation, and serum albumin level were closely associated with MAKE30.

CONCLUSIONS

An interpretable eXtreme Gradient Boosting model was developed and validated to predict MAKE30, which provides opportunities for risk stratification, clinical decision-making, and the conduct of clinical trials involving AKI.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR2200061610; https://tinyurl.com/3smf9nuw.

摘要

背景

急性肾损伤（AKI）是住院老年患者常见的并发症，与发病率、死亡率及医疗费用增加相关。30天内主要不良肾脏事件（MAKE30），即死亡、开始新的肾脏替代治疗或持续性肾功能障碍的综合情况，已被推荐作为涉及AKI的临床试验以患者为中心的终点指标。

目的

本研究旨在开发并验证一种基于机器学习的模型，以预测住院老年AKI患者的MAKE30。

方法

纳入2015年1月1日至2020年12月31日在中南大学湘雅二医院住院的4266例年龄≥65岁的老年AKI患者，并按7:3的比例随机分为训练集和内部测试集。另外，来自重症监护医学信息集市Ⅳ数据库的11864例符合条件的患者组成外部测试集。使用Boruta算法从53个候选变量中选择最重要的预测变量。应用极端梯度提升算法建立MAKE30的预测模型。通过受试者操作特征曲线下面积（AUROC）评估模型的辨别力。使用SHapley值加法解释法解释模型预测结果。

结果

2个研究队列中MAKE30的总体发生率分别为28.3%（95%CI 26.9%-29.7%）和26.7%（95%CI 25.9%-27.5%）。MAKE30的预测模型表现出足够的预测性能，训练集中的AUROC为0.868（95%CI 0.852-0.881），内部测试集中为0.823（95%CI 0.798-0.846）。其简化版本在外部测试集中的AUROC为0.744（95%CI 0.735-0.754）。SHapley值加法解释法表明，使用血管升压药、机械通气、血尿素氮水平、红细胞分布宽度变异系数和血清白蛋白水平与MAKE30密切相关。