Evidence map›Paper›PMID 40713497›Full record

ArticleBMC cardiovascular disorders2025

Association between glucose-to-albumin ratio and ischemic stroke risk in patients with coronary heart disease: a machine learning-based predictive model analysis.

Ling Hou, Yuanhong Li, Qianfei Liu

Abstract read
In one paragraph

Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ling HouDepartment of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Shiyan, Hubei Province, People's Republic of China.
Yuanhong LiCardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, Hubei Province, People's Republic of China. lyh0101@vip.163.com.
Qianfei LiuDepartment of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, Hubei Province, People's Republic of China. l879612253@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronary heart disease (CHD) and ischemic stroke (IS) share several pathophysiological mechanisms and risk factors, such as hypertension, hyperlipidemia, and diabetes. Investigating novel markers, such as the glucose-to-albumin ratio (GAR), for predicting the risk of IS in CHD patients holds significant clinical value.

methodsWe retrospectively enrolled 1,885 patients diagnosed with CHD who were treated at our hospital from January 1, 2022, to July 31, 2024. Feature selection was conducted using the Boruta algorithm, and a multilayer perceptron (MLP) model was employed to predict the risk of IS in CHD patients. The performance of the model was evaluated using ROC curves and calibration plots. SHAP values and partial dependence plots (PDP) were used to interpret the model's predictions.

resultsThe study showed that patients in the IS group were older and had significantly higher rates of hypertension and diabetes compared to those without AIS. Additionally, the AIS group had a higher prevalence of triple-vessel disease and right coronary artery lesions. GAR was significantly elevated in the IS group compared to the non-IS group. Key features identified by the Boruta algorithm included GAR, hyperlipidemia, and a history of hypertension. SHAP analysis indicated that GAR was significantly associated with IS risk, and PDP analysis further confirmed GAR as an independent predictor of IS.

conclusionGAR is a significant independent predictor of IS risk in CHD patients, with elevated GAR levels being strongly associated with an increased risk of IS.

Indexed as

Blood GlucoseCoronary DiseaseIschemic StrokeMachine LearningSerum Albumin, HumanAgedBiomarkersFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisRetrospective StudiesRisk AssessmentRisk FactorsALB protein, humanBiomarkersBlood GlucoseSerum Albumin, HumanCoronary heart diseaseGlucose-to-albumin ratioIschemic strokeMachine learningPredictive model

Identifiers

PMID40713497
PMCPMC12291368

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.