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Long-term prognosis after ST-elevation myocardial infarction according to the Canadian Cardiovascular Society classification of tissue injury severity in acute myocardial infarction

2024· article· en· W4403806377 on OpenAlexaffabout
Jaclyn Carberry, D Carrick, Margaret McEntegart, Hany Eteiba, Stuart Watkins, Martin M. Lindsay, Ahmed Mahrous, Ian Ford, Keith G Oldroyd, Mark C. Petrie, Kieran F. Docherty, Andreas Kumar, Rohan Dharmakumar, Colin Berry

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsNOSM University
Fundersnot available
KeywordsMedicineMyocardial infarctionCardiologyInternal medicineInfarctionTerm (time)

Abstract

fetched live from OpenAlex

Abstract Background The recently proposed Canadian Cardiovascular Society (CCS) classification of acute myocardial infarction (MI) describes 4 stages of tissue injury identified by cardiac magnetic resonance imaging (CMR); 1) oedema without late gadolinium enhancement (LGE); 2) LGE without microvascular obstruction (MVO); 3) MVO; and 4) intramyocardial haemorrhage (IMH). Prior studies examining the relationship between these characteristics and outcomes are limited by variations in imaging and measurement techniques, in particular the distinction between MVO and IMH. The relationship between the proposed CCS classification of MI and outcomes has not been described. Purpose To explore the prognostic significance of the CCS classification of MI tissue injury in patients with ST-elevation MI (STEMI). Methods The British Heart Foundation MR-MI study was a prospective single-centre CMR cohort study in patients with STEMI (July 2011-November 2012). CMR was performed on a single Siemens MAGNETOM Avanto 1.5-Tesla scanner at 2 days post-STEMI. The imaging protocol included LGE and T2* mapping, allowing for the identification of infarct, MVO, and IMH. Follow-up for the occurance of major adverse cardiovascular events (MACE) (recurrent MI, ischaemic stroke and cardiovascular death) and a composite outcome of heart failure hospitalization/ICD implantation (HFH) or all-cause death was performed via electronic case note review. Results 246 patients had complete data for this analysis. Of these, 6 (2%) were CCS Stage 1, 105 (43%) were Stage 2, 33 (13%) were Stage 3, and 102 (41%) were Stage 4. Due to the low number in CCS stage 1, these patients were pooled with Stage 2 for the purposes of this analysis (CCS Stage 1/2). Average age was 58 (SD 11) years and 188 (76%) were male. With higher CCS stage, more patients were Killip class III/IV at presentation, had the LAD as culprit artery and had TIMI flow 0/1 pre-PCI. Patients with higher CCS stage had higher peak troponin-I and NT-proBNP, lower left ventricular function, higher left ventricular volumes and larger infarct size (Table). Median follow-up was 11.8 years. HFH or all-cause death occurred in 80 (33%) patients (22 HFH and 58 all-cause deaths), and MACE occurred in 63 (26%) patients (41 recurrent MI, 11 ischaemic stroke events, and 11 cardiovascular deaths). There were no significant differences in outcomes between CCS Stage 3 and CCS Stage 1/2 (Figure). Patients with CCS Stage 4 had a higher risk of HFH or all-cause death (HR 1.73, 95%CI 1.08-2.77; p=0.022) and MACE (HR 1.88, 95%CI 1.10-3.20; p=0.021) as compared with those in CCS Stage 1/2 (Figure). Conclusion The novel CCS classification of MI tissue injury identifies patients at the highest risk of adverse outcomes following STEMI. The relationship between the presence of IMH (CCS Stage 4) and adverse outcomes highlights the importance of T2* imaging in post-MI CMR protocols in order to identify this prognostically important biomarker.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.334
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes2
Has abstractyes

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