The value of perioperative biomarker release for the assessment of myocardial injury or infarction in cardiac surgery
Bibliographic record
Abstract
OBJECTIVES: Cardiac biomarkers are indicators of irreversible cell damage. Current myocardial infarction (MI) definitions require concomitant clinical characteristics. For perioperative MI, a correlation of biomarker elevations and mortality has been suggested. Definitions emerged relying on cardiac biomarker release only. This approach is questionable as several clinical and experimental scenarios exist where relevant biomarker release can occur apart from MI. METHODS: We reviewed the clinical and basic science literature and revealed important aspects regarding the use and interpretation of cardiac biomarker release with special focus on their interpretation in the perioperative setting. RESULTS: Ischaemic biomarkers may be released without cell death in multiple conditions, such as after endurance runs in athletes, temporary inotropic stimulation in animal models and flow variations in in vitro cell models. In addition, access through atrial tissue during cannulation or concomitant valve procedures adds sources of enzyme release that may not be related to ventricular ischaemia (i.e. MI). Such non-cell death-related mechanisms may explain the lack of poor correlations of enzyme release and long-term outcomes in recent trials. In addition, the 3 main biomarkers, troponin T, I and creatine kinase myocardial band, differ in their release kinetics, which may differentially trigger MI events in trial patients. CONCLUSIONS: The identification of irreversible myocardial injury in cardiac surgery based only on biomarker release is unreliable. Cell death- and non-cell death-related mechanisms create a mix in the perioperative setting that requires additional markers for proper identification of MI. In addition, the 3 most common ischaemic biomarkers display different release kinetics adding to the confusion. We review the topic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".