The use of biomarkers to identify and prognosticate patients with a type 2 myocardial infarction (T2MI): a systematic review
Bibliographic record
Abstract
Abstract Background Myocardial infarction (MI) occurs when cardiac cells lack sufficient oxygen, leading to intracellular changes and eventual necrosis and death. However, differentiating type 1 myocardial infarction (T1MI) from type 2 myocardial infarction (T2MI) can be challenging based on clinical variables alone. Purpose We aimed to explore the utility of novel and traditional biomarkers to discriminate between T1MI and T2MI, and provide additional prognostic information. Methods A systematic review of observational studies and randomized controlled trials that examined the discriminatory or prognostic roles of either traditional cardiac biomarkers or non-traditional biomarkers was undertaken. Data sources included PubMed, SCOPUS, Web of Science, Embase, and ClinicalTrials.gov, and were last searched on November 15, 2024. All study types evaluating the ability of biomarkers to help discriminate between T1MI and T2MI and the prognostic utility of these identified biomarkers are reported. Results 28 studies with 15,892 individuals with T2MI were included. Of 12 studies that examined traditional cardiac biomarkers (troponin, creatinine kinase, and b-type natriuretic peptide), the ability to discriminate between T1MI and T2MI ranged from an area under the curve (AUC) of 0.61-0.71. Patients with T2MI exhibited significantly lower baseline values, peaks, and relative changes across all traditional cardiac biomarkers, however, with only fair discrimination. Studies that added traditional cardiac biomarkers to clinical variables (n = 4) demonstrated a diagnostic accuracy AUC of 0.71-0.82. The prognostic value of these biomarkers was infrequently assessed (n=4) and inconsistently demonstrated a correlation with subsequent cardiovascular events. Studies including non-traditional biomarkers (n=12) demonstrated that various markers of inflammation (CRP, procalcitonin), hemodynamic stress (MR-proANP, myosin-binding protein-C), and endothelial dysfunction (CT-proET1) generally demonstrate fair diagnostic accuracy. However, when combined in a multi biomarker model (with or without clinical variables), can achieve excellent discriminatory ability (AUC 0.82-0.92). CRP was consistently elevated in T2MI, and the CRP/troponin ratio had high specificity (90%) in discriminating between T2MI over T1MI. Among studies exploring non-traditional biomarkers, prognostic utility was infrequently assessed. Conclusion Integrating novel biomarkers, metabolomic profiles and proteomic profiles in clinical assessments may aid in the diagnosis and prognostication of T2MI. Identifying these novel biomarkers is crucial for improving diagnostic accuracy and guiding treatment strategies to optimize patient outcomes with T2MI.PRISMA Diagram Potential Biomarkers for T2MI
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".