Evidence map›Paper›PMID 40886105›Full record

ArticleJournal of the American Heart Association2025

Identification of Clinical Phenotypes in Type 2 Myocardial Infarction: Insights Into Characteristics, Prognosis, and Management Strategies.

Davide Bertolini, Matteo Armillotta, Francesco Angeli, Angelo Sansonetti, Francesca Bodega, Sara Amicone, Lisa Canton, Damiano Fedele, Nicole Suma, Andrea Impellizzeri and 14 more

Abstract read
In one paragraph

Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Periprocedural Myocardial Infarction: Do We Need an Updated Definition?Journal of cardiovascular development and disease · 2026
    Review
  5. Article
  6. Article
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

24 authors.

Davide BertoliniCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.ORCID 0009-0009-2554-2512
Matteo ArmillottaDepartment of Medical and Surgical Sciences DIMEC, Alma Mater Studiorum, University of Bologna Italy.ORCID 0000-0002-7971-8052
Francesco AngeliDepartment of Medical and Surgical Sciences DIMEC, Alma Mater Studiorum, University of Bologna Italy.ORCID 0000-0002-8732-4512
Angelo SansonettiCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.ORCID 0000-0002-4565-4310
Francesca BodegaCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.
Sara AmiconeDepartment of Medical and Surgical Sciences DIMEC, Alma Mater Studiorum, University of Bologna Italy.
Lisa CantonDepartment of Medical and Surgical Sciences DIMEC, Alma Mater Studiorum, University of Bologna Italy.
Damiano FedeleDepartment of Medical and Surgical Sciences DIMEC, Alma Mater Studiorum, University of Bologna Italy.ORCID 0000-0002-9198-2817
Nicole SumaCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.ORCID 0009-0003-6119-8890
Andrea ImpellizzeriCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.
Francesco Pio TattiloCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.ORCID 0009-0008-3709-5791
Daniele CavalloCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.ORCID 0009-0003-0782-8660
Ornella Di IuorioCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.ORCID 0009-0000-6352-0490
Khrystyna RyabenkoCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.ORCID 0000-0001-8254-1502
Virginia MarinelliCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.
Claudio AstaCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.
Mariachiara CiarlantiniCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.
Andrea RinaldiCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.
Nevio TaglieriCardiology Unit IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna Italy.ORCID 0000-0002-9593-4150
Gianni CasellaUnit of Cardiology, Maggiore Hospital Bologna Italy.ORCID 0000-0002-0701-7329
Paola RucciDivision of Hygiene and Biostatistics, Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum University of Bologna Italy.
Antonio CurcioDivision of Cardiology, Department of Pharmacy, Health and Nutritional Sciences University of Calabria Rende Italy.ORCID 0000-0003-3213-4436
Luca BergamaschiDepartment of Medical and Surgical Sciences DIMEC, Alma Mater Studiorum, University of Bologna Italy.ORCID 0000-0001-8186-9717
Carmine PizziDepartment of Medical and Surgical Sciences DIMEC, Alma Mater Studiorum, University of Bologna Italy.ORCID 0000-0002-4048-675X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundType 2 myocardial infarction (T2MI) accounts for a substantial share of acute coronary syndromes but remains challenging to diagnose and manage due to its varied presentations and underlying profiles. This study aims to identify key differences and distinct clinical phenotypes in a large T2MI population.

methodsAll consecutive patients with non-ST-segment-elevation myocardial infarction undergoing coronary angiography with a confirmed T2MI diagnosis between January 1, 2017, and March 31, 2023, were analyzed. Precipitating factors of supply-demand mismatch were identified, and coronary burden was assessed using the Gensini score. Latent class analysis was used to identify clinical phenotypes, and multivariable analyses were performed to determine prognostic predictors. A composite of major adverse cardiovascular events was assessed during follow-up, along with additional outcomes including cardiovascular death and nonfatal type 2 reinfarction.

resultsAmong 774 patients with T2MI, latent class analysis identified 2 phenotypes. Phenotype 1 (31.5%) was younger with a higher prevalence of nonatherosclerotic coronary causes and unknown pathogeneses. Phenotype 2 (68.5%) exhibited greater comorbidity and a higher atherosclerotic burden, reflected by elevated Gensini scores (median, 11 versus 1.5;

conclusionsThis study identified 2 clinical phenotypes in T2MI, highlighting differences in characteristics, precipitating factors, outcomes, and prognostic predictors, emphasizing the potential for phenotype-driven approaches in diagnosis and management.

Indexed as

Non-ST Elevated Myocardial InfarctionAgedCoronary AngiographyFemaleHumansMaleMiddle AgedPhenotypePrognosisRetrospective StudiesRisk AssessmentRisk Factorscardiovascular outcomesnon–ST‐segment–elevation myocardial infarctiontype 2 myocardial infarction

Identifiers

PMID40886105
PMCPMC12554420

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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.