Evidence map›Paper›PMID 41510263›Full record

ArticleResearch square2025

Predicting cardiovascular outcomes in elderly patients with acute coronary syndrome: a nomogram approach.

Hamidreza Soleimani, Reza Nikfar, Sahand Siami, Farhad Shaker, Parisa Fallahtafti, Mehdi Mehrani, Yaser Jenab, Sajjad Hosseini, Adrian V Hernandez, Diaa Hakim and 2 more

Abstract readPreprint
In one paragraph

Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Hamidreza SoleimaniImam Khomeini Hospital Complex Tehran University of Medical Science.
Reza NikfarTehran Heart Center Research Institute, Tehran University of Medical Sciences.
Sahand SiamiTehran Heart Center Research Institute, Tehran University of Medical Sciences.
Farhad ShakerTehran Heart Center Research Institute, Tehran University of Medical Sciences.
Parisa FallahtaftiTehran Heart Center Research Institute, Tehran University of Medical Sciences.
Mehdi MehraniTehran Heart Center Research Institute, Tehran University of Medical Sciences.
Yaser JenabTehran Heart Center Research Institute, Tehran University of Medical Sciences.
Sajjad HosseiniImam Khomeini Hospital Complex Tehran University of Medical Science.
Adrian V HernandezUniversity of Connecticut School of Pharmacy.
Diaa HakimHarvard Medical School.
Michael G NannaYale School of Medicine.
Kaveh HosseiniTehran Heart Center Research Institute, Tehran University of Medical Sciences.

Funding

Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External ModulesP30AG021342 · NIA · YALE UNIVERSITY · PI Lauren Ferrante · 2002 to 2026
$37.9M
Estimating the treatment effect of cardiovascular medications to modify the risk for future cognitive decline in older adultsK76AG088428 · NIA · YALE UNIVERSITY · PI Michael Nanna · 2024 to 2026
$729k
NIA NIH HHS K76 AG088428NIA NIH HHS P30 AG021342
6 · The paper itself

Abstract

Background: Although ST Elevation Myocardial Infarction (STEMI) diagnosis and therapy have improved, high-risk categories like elderly persons still have a significant chance of MACE despite treatment. Objectives: This study attempts to construct a predictive nomogram for MACE incidence using clinical data from a STEMI registry. Methods: Tehran Heart Center's computerized record recognized all 65-year-old STEMI primary PCI patients consecutively. This retrospective study examined demographic, laboratory, clinical, and intra-procedural factors. Post-PCI univariate and multivariate analyses identified MACE risk variables. Decision curve analysis, ROC, and calibration plots validated predictive nomograms. R Studio and R used "tidyverse" and "rms" packages for all analyses. Results: The 1946 study included 70% training and 30% testing patients. Basic demographic and clinical variables were identical for both groups. The average follow-up was 17 months. 8 factors were selected for the nomogram after univariate and multivariate analysis: left-ventricular ejection fraction (LVEF), serum creatinine, hemoglobin, and fasting blood glucose levels, presence of valvular heart disease, post-PCI TIMI flow grade, diameter of the culprit lesion stent, and presence or absence of shock after PCI. The post-PCI MACE prediction AUC was 71%. Calibration plots showed that the nomogram model was well-calibrated and close to observed outcomes. Decision curve analysis also revealed that the model predicted MACE discriminatively. Conclusion: A nomogram successfully predicts MACE risk in older STEMI patients using laboratory, clinical, and procedural parameters. This algorithm may identify vulnerable high-risk patients for more aggressive preventative interventions. Clinical trial number: not applicable.

Indexed as

MACEModel PredictionPCISTEMI

Identifiers

PMID41510263
PMCPMC12776514

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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.