Evidence map›Paper›PMID 40834835›Full record

ArticleThe American journal of emergency medicine2025

Differentiating Type 1 and Type 2 myocardial infarction using a machine learning algorithm and biomarkers.

Anna Snavely, Laurel Jackson, Christian John Hunter, Gillian Murtagh, Jason P Stopyra, Nicklaus P Ashburn, Michael W Supples, Robert Christenson, Chadwick D Miller, Simon A Mahler

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The American journal of emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

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

  1. Pooled it
  2. Trial
  3. Review
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

10 authors.

Anna SnavelyDepartment of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Laurel JacksonCore Diagnostics, Abbott Laboratories, Abbott Park, IL, USA.
Christian John HunterCore Diagnostics, Abbott Laboratories, Abbott Park, IL, USA.
Gillian MurtaghCore Diagnostics, Abbott Laboratories, Abbott Park, IL, USA.
Jason P StopyraDepartment of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Nicklaus P AshburnDepartment of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Michael W SupplesDepartment of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Robert ChristensonDepartment of Pathology, University of Maryland, Baltimore, MD, USA.
Chadwick D MillerDepartment of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Simon A MahlerDepartment of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Department of Implementation Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA. Electronic address: smahler@wakehealth.edu.

Funding

CMR-IMPACTR01HL118263 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI MILLER, CHADWICK DAVID · 2013 to 2018
$3.7M
NHLBI NIH HHS R01 HL118263
6 · The paper itself

Abstract

backgroundTreatment of myocardial infarction (MI) differs based on MI type, which can be difficult to determine in the emergency department. This study sought to evaluate whether a machine learning algorithm (MI

methodsWe conducted a secondary analysis of the multisite CMR-IMPACT trial, which prospectively enrolled adults with symptoms of acute coronary syndrome and an initial indeterminate troponin. Patients with an adjudicated diagnosis of MI and an initial high-sensitivity cardiac troponin I (hs-cTnI; Abbott Laboratories) measure were included. Incidence of MI and MI type were adjudicated by expert reviewers. Receiver operator characteristic curves for MI-type were created and area under the curve (AUC) calculated for MI

resultsAmong 123 patients with adjudicated MI, the mean age was 60±12 years and 37.4% (46/123) were female. Type 1 MI occurred in 58.5% (72/123) and type 2 MI in 41.5% (51/123). MI

conclusionAdding NT-proBNP and Gal-3 to the MI

Indexed as

Galectin 3Machine LearningMyocardial InfarctionNatriuretic Peptide, BrainPeptide FragmentsAgedAlgorithmsBiomarkersDiagnosis, DifferentialFemaleHumansMaleMiddle AgedProspective StudiesROC CurveTroponin IBiomarkersGalectin 3Natriuretic Peptide, BrainPeptide Fragmentspro-brain natriuretic peptide (1-76)Troponin IGalectin-3Machine learningMyocardial infarctionNatriuretic peptideTroponin

Identifiers

PMID40834835
PMCPMC13244885

What OpenQuestion holds

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Registered trials

None linked

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.