Evidence map›Paper›PMID 37081535›Full record

ArticleCardiovascular diabetology2023

Predictive value of high sensitivity C-reactive protein in three-vessel disease patients with and without type 2 diabetes.

Lei Guo, Haichen Lv, Junjie Wang, Bo Zhang, Yifan Zhu, Xiaoyan Zhang, Hao Zhu, Xuchen Zhou, Yunlong Xia

Open access · goldAbstract read
In one paragraph

Article in Cardiovascular diabetology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.6field-weighted citation impact, top 6% of its field
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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 1 country.

Lei GuoDepartment of Cardiology, the First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China.
Haichen LvDepartment of Cardiology, the First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China.
Junjie WangDepartment of Cardiology, the First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China.
Bo ZhangDepartment of Cardiology, the First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China.
Yifan ZhuDepartment of Cardiology, Shengjing Hospital of China Medical University, Shenyang, People's Republic of China.
Xiaoyan ZhangDepartment of Radiology, Fuyang Hospital of Anhui Medical University, Fuyang, People's Republic of China.
Hao ZhuDepartment of Cardiology, the First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China.
Xuchen ZhouDepartment of Cardiology, the First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China.
Yunlong XiaDepartment of Cardiology, the First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China. yunlong_xia@126.com.
First Affiliated Hospital of Dalian Medical University · CNDalian Medical University · CNChina Medical University · CNFuyang Second People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes mellitus (DM) and atherosclerosis are multifactorial conditions and share a common inflammatory basis. Three-vessel disease (TVD) represents a major challenge for coronary intervention. Nonetheless, the predictive value of high-sensitivity C-reactive protein (hs-CRP) for TVD patients with or without type 2 DM remains unknown. Herein, we aimed to ascertain the long-term predictive value of hs-CRP in TVD patients according to type 2 DM status from a large cohort.

methodsA total of 2734 TVD patients with (n = 1040, 38%) and without (n = 1694, 62%) type 2 diabetes were stratified based on the hs-CRP (< 2 mg/L vs. ≥ 2 mg/L). Three multivariable analysis models were performed to evaluate the effect of potential confounders on the relationship between hs-CRP level and clinical outcomes. The Concordance index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to assess the added effect of hs-CRP and the baseline model with established risk factors on the discrimination of clinical outcomes. The primary endpoint was major adverse cardiac and cerebrovascular events (MACCE).

resultsThe median follow-up duration was 2.4 years. Multivariate Cox regression analyses showed that the incidence of MACCE (adjusted hazard ratio [HR] 1.17, 95% confidence interval [CI] 1.01-1.35, p = 0.031) and all-cause death (HR 1.82, 95% CI 1.07-3.11, p = 0.026) were significantly higher in the diabetic group compared to the non-diabetic group. In the diabetic group, the incidence of MACCE (adjusted HR 1.51, 95% CI 1.09-2.10, p = 0.013) was significantly higher in the high hs-CRP group than in the low hs-CRP group; no significant difference was found for all-cause death (HR 1.63; 95% CI 0.58-4.58, p = 0.349). In the non-diabetic group, the prevalence of MACCE (adjusted HR 0.93, 95% CI 0.71-1.22, p = 0.613) was comparable between the two groups. Finally, the NRI (0.2074, p = 0.001) and IDI (0.0086, p = 0.003) for MACCE were also significantly increased after hs-CRP was added to the baseline model in the diabetic group.

conclusionsElevated hs-CRP is an independent prognostic factor for long-term outcomes of MACCE in TVD patients with type 2 diabetes but not in those without type 2 diabetes. Compared to traditional risk factors, hs-CRP improved the risk prediction of adverse cardiovascular events in TVD patients with type 2 diabetes.

Indexed as

Coronary Artery DiseaseDiabetes Mellitus, Type 2Myocardial InfarctionStrokeBiomarkersC-Reactive ProteinHumansPrognosisReceptors, ImmunologicRisk FactorsBiomarkersC-Reactive ProteinCRP protein, humanReceptors, ImmunologicCoronary artery diseaseDiabetesHigh-sensitivity C-reactive proteinOutcomesThree-vessel disease

Identifiers

PMID37081535
PMCPMC10120230
OpenAlexW4366549118

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Registered trials

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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.