Construction and Validation of a Hospital Mortality Risk Model for Advanced Elderly Patients with Heart Failure Based on Machine Learning.

IF 2 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL
International Journal of General Medicine Pub Date : 2025-06-20 eCollection Date: 2025-01-01 DOI:10.2147/IJGM.S514972
Shuai Shang, Meng Wei, Huasheng Lv, Xiaoyan Liang, Yanmei Lu, Baopeng Tang
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引用次数: 0

Abstract

Purpose: This study aimed to develop and validate a model based on machine learning algorithms to predict the risk of in-hospital death among advanced elderly patients with Heart Failure (HF).

Methods: A total of 4580 advanced elderly patients who were admitted to the hospital and diagnosed with HF from May 2012 to September 2023 were included in this study, among whom 552 cases (12.5%) died. The least absolute shrinkage and selection operator (LASSO) regression and Boruta feature selection were used to screen the baseline variables to identify the variables significantly associated with death. Subsequently, seven different machine learning models were constructed and their prediction performances were evaluated. The Shapley Additive Explanations (SHAP) values were used to analyze the impact of key variables on the model prediction results.

Results: A total of seven variables significantly associated with death were selected by LASSO regression and Boruta feature selection, including white blood cell count (WBC), neutrophil percentage (Neut %), C-reactive protein (CRP), D-dimer, glycated serum protein (GSP), N-terminal pro-B-type natriuretic peptide (NT-ProBNP), and body mass index (BMI). Among all the models, the extreme gradient boosting (XGB) model performed the best, with an area under the curve (AUC) value of 0.933, a sensitivity of 0.79, a specificity of 0.89, a recall of 0.79, and an F1 score of 0.59 on the validation set. The SHAP analysis showed that CRP, BMI, NT-ProBNP, D-dimer, and GSP were the main influencing factors for death.

Conclusion: This study successfully constructed a prediction model for the in-hospital death risk of advanced elderly patients with HF, and the XGB model exhibited excellent prediction performance. This model can be used for the early clinical identification of high-risk patients and thus provide support for individualized treatment strategies.

基于机器学习的高龄心力衰竭患者医院死亡率风险模型构建与验证
目的:本研究旨在开发和验证基于机器学习算法的模型,以预测晚期老年心力衰竭(HF)患者的院内死亡风险。方法:选取2012年5月至2023年9月住院诊断为HF的老年晚期患者4580例,其中死亡552例(12.5%)。使用最小绝对收缩和选择算子(LASSO)回归和Boruta特征选择筛选基线变量,以识别与死亡显著相关的变量。随后,构建了7种不同的机器学习模型,并对其预测性能进行了评估。采用Shapley加性解释(SHAP)值分析关键变量对模型预测结果的影响。结果:通过LASSO回归和Boruta特征选择,共筛选出与死亡显著相关的7个变量,包括白细胞计数(WBC)、中性粒细胞百分比(Neut %)、c反应蛋白(CRP)、d -二聚体、糖化血清蛋白(GSP)、n端前b型利钠肽(NT-ProBNP)、体重指数(BMI)。在所有模型中,极端梯度增强(XGB)模型表现最好,曲线下面积(AUC)值为0.933,灵敏度为0.79,特异性为0.89,召回率为0.79,验证集的F1得分为0.59。SHAP分析显示,CRP、BMI、NT-ProBNP、d -二聚体和GSP是影响死亡的主要因素。结论:本研究成功构建了高龄心衰患者院内死亡风险预测模型,XGB模型具有较好的预测效果。该模型可用于临床早期识别高危患者,从而为个性化治疗策略提供支持。
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
International Journal of General Medicine
International Journal of General Medicine Medicine-General Medicine
自引率
0.00%
发文量
1113
审稿时长
16 weeks
期刊介绍: The International Journal of General Medicine is an international, peer-reviewed, open access journal that focuses on general and internal medicine, pathogenesis, epidemiology, diagnosis, monitoring and treatment protocols. The journal is characterized by the rapid reporting of reviews, original research and clinical studies across all disease areas. A key focus of the journal is the elucidation of disease processes and management protocols resulting in improved outcomes for the patient. Patient perspectives such as satisfaction, quality of life, health literacy and communication and their role in developing new healthcare programs and optimizing clinical outcomes are major areas of interest for the journal. As of 1st April 2019, the International Journal of General Medicine will no longer consider meta-analyses for publication.
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