Evidence map›Paper›PMID 37724318›Full record

ArticleJournal of inflammation research2023

An Easy-to-Use Nomogram Based on SII and SIRI to Predict in-Hospital Mortality Risk in Elderly Patients with Acute Myocardial Infarction.

Yan Chen, Kailing Xie, Yuanyuan Han, Qing Xu, Xin Zhao

Abstract read
In one paragraph

Article in Journal of inflammation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yan Chen *Department of Cardiology, the Second Hospital of Dalian Medical University, Dalian, People's Republic of China.ORCID 0000-0003-0371-5776
Kailing Xie *Department of Second Clinical College, China Medical University, Shenyang, People's Republic of China.ORCID 0009-0001-9447-149X
Yuanyuan HanDepartment of Cardiology, the Second Hospital of Dalian Medical University, Dalian, People's Republic of China.
Qing XuDepartment of Cardiology, the Second Hospital of Dalian Medical University, Dalian, People's Republic of China.
Xin ZhaoDepartment of Cardiology, the Second Hospital of Dalian Medical University, Dalian, People's Republic of China.ORCID 0000-0003-3437-2120

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: Inflammatory response is closely associated with poor prognosis in elderly patients with acute myocardial infarction (AMI). The aim of this study was to develop an easy-to-use predictive model based on medical history data at admission, systemic immune inflammatory index (SII), and systemic inflammatory response index (SIRI) to predict the risk of in-hospital mortality in elderly patients with AMI. Methods: We enrolled 1550 elderly AMI patients (aged ≥60 years) with complete medical history data and randomized them 5:5 to the training and validation cohorts. Univariate and multivariate logistic regression analyses were used to screen risk factors associated with outcome events (in-hospital death) and to establish a nomogram. The discrimination, calibration, and clinical application value of nomogram were evaluated based on receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. Results: The results of multivariate logistic regression showed that age, body mass index (BMI), previous stroke, diabetes, SII, and SIRI were associated with in-hospital death, and these indicators will be included in the final prediction model, which can be obtained by asking the patient's medical history and blood routine examination in the early stage of admission and can improve the utilization rate of the prediction model. The areas under the ROC curve for the training and validation cohorts nomogram were 0.824 (95% CI 0.796 to 0.851) and 0.809 (95% CI 0.780 to 0.836), respectively. Calibration curves and DCA showed that nomogram could better predict the risk of in-hospital mortality in elderly patients with AMI. Conclusion: The nomogram constructed by combining SII, SIRI, and partial medical history data (age, BMI, previous stroke, and diabetes) at admission has a good predictive effect on the risk of in-hospital death in elderly patients with AMI.

Indexed as

coronary artery diseaseelderlynomogramprediction modelsystemic inflammatory markers

Identifiers

PMID37724318
PMCPMC10505402

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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.