ArticleReviews in cardiovascular medicine2023
Correlation Analysis of Gensini Score in Diabetic Patients with Coronary Heart Disease.
Article in Reviews in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Serum long non-coding RNA SNHG9 as a diagnostic biomarker for acute coronary syndrome and a predictor of prognosis following percutaneous coronary intervention: a clinical evaluation.Journal of cardiothoracic surgery · 2026Article
- Development and validation of a machine learning model for predicting coronary artery disease risk: a retrospective study.Frontiers in cardiovascular medicine · 2026Article
- Artificial intelligence-enhanced three-dimensional echocardiography reveals left atrial-ventricular coupling index as a novel prognostic marker in coronary artery disease.BMC cardiovascular disorders · 2025Article
- The Relationship Between the C-reactive Protein-Triglyceride Glucose Index and the Risk of Coronary Heart Disease Alongside the Severity of Coronary Artery Stenosis in Older Adults With Different Levels of Glucose Metabolism: A Real-World Retrospective Clinical Study.Reviews in cardiovascular medicine · 2025Article
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Authors and funding
6 authors.
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Abstract
Background: Assessment of risk factors is essential for clinical diagnosis and prevention in patients with both diabetes mellitus (DM) and coronary heart disease (CHD). In the present study we investigated correlation of the Gensini score with the incidence of major adverse cardiac and cerebrovascular events (MACCEs) in patients with DM and CHD. Methods: A total of 802 DM patients with CHD admitted to the First Affiliated Hospital of Zhengzhou University and who met the inclusion criteria were enrolled in the study. The median follow-up time for these patients was 3000 days (range 382.5-3000). Receiver operating characteristic (ROC) curves for the Gensini score were generated and the area under the curve (AUC) was calculated. Patients were divided into two groups based on the Gensini score cut-off value. Univariate and multivariate Cox proportional hazard regression analysis was used to identify the risk factors associated with MACCEs. The incidence of MACCEs in the two groups was compared using Kaplan-Meier analysis. Results: The AUC of the ROC curve was 0.675. The maximum Youden's index was 0.248 at a Gensini score cut-off value of 74.8605. This gave a sensitivity and specificity for the prediction of MACCE of 68.8% and 56%, respectively. A high Gensini score was a risk factor for MACCEs, and the incidence of MACCEs was significantly greater in the high Gensini score group compared to the low Gensini score group. Conclusions: A high Gensini score is a risk factor for patients with DM and CHD and is associated with a high incidence of MACCEs. Clinical Trial Registration: The details of study design are registered on http://www.chictr.org.cn (identifier: ChiCTR-2200055450).
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