Evidence map›Paper›PMID 39076425›Full record

ArticleReviews in cardiovascular medicine2023

Correlation Analysis of Gensini Score in Diabetic Patients with Coronary Heart Disease.

Jinyue Qi, Yunzhe Wang, Zhiyu Liu, Fengyi Yu, Junnan Tang, Jinying Zhang

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jinyue QiDepartment of Cardiology, The First Affiliated Hospital of Zhengzhou University, 450052 Zhengzhou, Henan, China.
Yunzhe WangDepartment of Cardiology, The First Affiliated Hospital of Zhengzhou University, 450052 Zhengzhou, Henan, China.
Zhiyu LiuDepartment of Cardiology, The First Affiliated Hospital of Zhengzhou University, 450052 Zhengzhou, Henan, China.
Fengyi YuDepartment of Cardiology, The First Affiliated Hospital of Zhengzhou University, 450052 Zhengzhou, Henan, China.
Junnan TangDepartment of Cardiology, The First Affiliated Hospital of Zhengzhou University, 450052 Zhengzhou, Henan, China.
Jinying ZhangDepartment of Cardiology, The First Affiliated Hospital of Zhengzhou University, 450052 Zhengzhou, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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).

Indexed as

cardiovascular heart diseasediabetes mellitusGensini scoreMACCEs

Identifiers

PMID39076425
PMCPMC11272856

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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.