European Association of Cardio-Thoracic Surgery (EACTS) expert consensus statement on perioperative myocardial infarction after cardiac surgery
Bibliographic record
Abstract
Cardiac surgery may lead to myocardial damage and release of cardiac biomarkers through various mechanisms such as cardiac manipulation, systemic inflammation, myocardial hypoxia, cardioplegic arrest and ischaemia caused by coronary or graft occlusion. Defining perioperative myocardial infarction (PMI) after cardiac surgery presents challenges, and the association between the current PMI definitions and postoperative outcomes remains uncertain. To address these challenges, the European Association of Cardio-Thoracic Surgery (EACTS) facilitated collaboration among a multidisciplinary group to evaluate the existing evidence on the mechanisms, diagnosis and prognostic implications of PMI after cardiac surgery. The review found that the postoperative troponin value thresholds associated with an increased risk of mortality are markedly higher than those proposed by all the current definitions of PMI. Additionally, it was found that large postoperative increases in cardiac biomarkers are prognostically relevant even in absence of additional supportive signs of ischaemia. A new algorithm for PMI detection after cardiac surgery was also proposed, and a consensus was reached within the group that establishing a prognostically relevant definition of PMI is critically needed in the cardiovascular field and that PMI should be included in the primary composite outcome of coronary intervention trials.
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 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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".