解放军医学杂志2026，Vol.51Issue(7)：1022-1030,9.DOI:10.11855/j.issn.0577-7402.2436.2026.0625
ASCVD患者eGDR与长期死亡风险的关系:一项回顾性队列研究与机器学习预测模型构建
Association between eGDR and long-term mortality risk in elderly patients with ASCVD:a retrospective cohort study and construction of machine-learning predictive models
摘要
Abstract
Objective To investigate the association between estimated glucose disposal rate(eGDR)and all-cause and cardiovascular mortality in elderly patients with atherosclerotic cardiovascular disease(ASCVD),and to develop and validate a predictive model for all-cause mortality risk.Methods A total of 2479 patients aged≥60 years with established ASCVD treated at the Second Medical Center of Chinese PLA General Hospital from January 2008 to December 2022 were included in this retrospective analysis.Patients were divided into four groups according to the eGDR quartiles:Q1 group(n=617),Q2 group(n=620),Q3 group(n=622),and Q4 group(n=620),to evaluate the dose-response relationship between eGDR and mortality risk.Cox proportional hazards models and restricted cubic spline(RCS)analyses were employed to assess the association between eGDR and mortality risk.Seven machine learning algorithms(K-nearest neighbours,support vector machines,random forests,least absolute shrinkage and selection operator(LASSO)regression,naive Bayes,extreme gradient boosting,and decision trees)were compared in terms of their predictive performances for all-cause mortality.The optimal model was further interpreted using SHapley Additive exPlanations(SHAP)to rank variable importance.Results During a median follow-up of 8.1 years,1101 all-cause deaths(44.4%)and 429 cardiovascular deaths(17.3%)occurred.Compared with patients in the lowest eGDR quartile,those in the highest quartile had a 28%reduction in all-cause mortality risk(HR=0.72,95%CI 0.57-0.90)and a 42%reduction in cardiovascular mortality risk(HR=0.58,95%CI 0.44-0.78).RCS analysis demonstrated a significant negative correlation between eGDR and both all-cause and cardiovascular mortality(P<0.05).Among the seven algorithms,random forest achieved the highest area under the curve(AUC=0.748,95%CI 0.740-0.756).SHAP analysis identified age,low-density lipoprotein cholesterol(LDL-C),estimated glomerular filtration rate(eGFR),and eGDR as the four most important predictors of all-cause mortality.Conclusions Lower eGDR levels are independently associated with increased risks of both all-cause and cardiovascular mortality in ASCVD patients aged≥60 years.The random forest model incorporating eGDR achieves favorable predictive efficacy for all-cause mortality,supporting eGDR as a promising biomarker for mortality risk stratification in this population.关键词
胰岛素抵抗/估算葡萄糖处置率/动脉粥样硬化性心血管疾病/死亡/机器学习/风险预测Key words
insulin resistance/estimated glucose disposal rate/atherosclerotic cardiovascular disease/mortality/machine learning/risk prediction分类
医药卫生引用本文复制引用
安莉,张帷,付治卿,吕月,李珊..ASCVD患者eGDR与长期死亡风险的关系:一项回顾性队列研究与机器学习预测模型构建[J].解放军医学杂志,2026,51(7):1022-1030,9.基金项目
This work was supported by the Open Project of National Clinical Research Centre for Geriatric Diseases(NCRCG-PLAGH-2024016) 国家老年疾病临床医学研究中心开放课题(NCRCG-PLAGH-2024016) （NCRCG-PLAGH-2024016）
