Evidence map›Paper›PMID 38410422›Full record

ArticleJournal of inflammation research2024

A Prediction Model Based on Systemic Immune-Inflammatory Index Combined with Other Predictors for Major Adverse Cardiovascular Events in Acute Myocardial Infarction Patients.

Xiaobo Li, Chen Yu, Xuewei Liu, Yejia Chen, Yutian Wang, Hongbin Liang, ShiFeng Qiu, Li Lei, Jiancheng Xiu

Open access · goldAbstract read
In one paragraph

Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
9.2field-weighted citation impact, top 2% 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 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 3 institutions in 1 country.

Xiaobo LiDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.ORCID 0000-0003-2554-4234
Chen YuDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Xuewei LiuThe Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Southern Medical University, Dongguan, Guangdong, People's Republic of China.
Yejia ChenDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Yutian WangDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Hongbin LiangDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
ShiFeng QiuDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Li LeiDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Jiancheng XiuDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Nanfang Hospital · CNDongguan People’s Hospital · CNHunan Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the prognostic value of the systemic immune-inflammatory index (SII) for predicting in-hospital major adverse cardiovascular events (MACEs) in patients with acute myocardial infarction (AMI) and establish a relevant nomogram. Methods: This study included 954 AMI patients. We examined three inflammatory factors (SII, platelet to lymphocyte ratio (PLR) and neutrophil to lymphocyte ratio (NLR)) to see which one predicts in-hospital MACEs better. The predictors were subsequently screened using bidirectional stepwise regression method, and a MACE nomogram was constructed via logistic regression analysis. The predictive value of the model was evaluated using the area under the curve (AUC), sensitivity and specificity. In addition, the clinical utility of the nomogram was evaluated using decision curve analysis. We also compared the nomogram with the Global Registry of Acute Coronary Events (GRACE) scoring system. Results: 334 (35.0%) patients had MACEs. The SII (AUC =0.684) had a greater predictive value for in-hospital MACEs in AMI patients than the PLR (AUC =0.597, P<0.001) or NLR (AUC=0.654, P=0.01). The area under the curve (AUC) of the SII-based multivariable model for predicting MACEs, which was based on the SII, Killip classification, left ventricular ejection fraction, age, urea nitrogen (BUN) concentration and electrocardiogram-based diagnosis, was 0.862 (95% CI: 0.833-0.891). Decision curve and calibration curve analysis revealed that SII-based multivariable model demonstrated a good fit and calibration and provided positive net benefits than the model without SII. The predictive value of the SII-based multivariable model was greater than that of the GRACE scoring system (P<0.001). Conclusion: SII is a promising, reliable biomarker for identifying AMI patients at high risk of in-hospital MACEs, and SII-based multivariable model may serve as a quick and easy tool to identify these patients.

Indexed as

acute myocardial infarctioninflammationmajor adverse cardiovascular eventssystemic immune-inflammatory index

Identifiers

PMID38410422
PMCPMC10895983
OpenAlexW4392059531

What OpenQuestion holds

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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.