Association Between Electrocardiographic Changes and Myocardial Injury or Death After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: The relationship between myocardial injury after cardiac surgery (MICS), ischemia on electrocardiogram (ECG), and mortality is uncertain. In this study we aimed to determine whether potential ischemic ECG changes after cardiac surgery are associated with 30-day mortality. METHODS: In a cohort of adults who underwent cardiac surgery, experts interpreted ECGs preoperatively; on postoperative days 0, 1, 2, and 3; and on the last day before discharge (59,539 total ECGs reviewed) for new potential ischemic ECG changes. RESULTS: Among 12,594 patients, 9097 (72.2%) had potential ischemic ECG changes; 259 (2.1%) died within 30 days after surgery. Among patients with troponin elevation meeting MICS criteria, in models adjusting for EuroSCORE II, the hazard ratio (HR) for 30-day mortality was 0.57 (95% confidence interval [CI] 0.35-0.94, P = 0.03) for new Q waves, 2.17 (95% CI 1.14-4.13, P = 0.02) for ST depression ≥ 2 mm, and 0.58 (95% CI 0.39-0.87, P = 0.007) for T-wave inversion 1-1.9 mm. ST elevation was not significantly associated with 30-day mortality. The only ECG change for which coronary artery bypass grafting (CABG) was an effect modifier was new left bundle branch block (LBBB), with an HR of 2.78 (95% CI 1.69-4.60, P = 0.0001) with CABG and an HR of 1.10 (95% CI 0.54-2.21, P = 0.27) without CABG (P value for interaction = 0.03). CONCLUSIONS: After cardiac surgery, potential ischemic ECG changes are common and have divergent associations with mortality. ST depression was associated with a higher risk of death, whereas new Q waves and T-wave inversions were associated with a lower risk of death. A new LBBB was associated with a higher risk of death only among patients who underwent CABG. Potential ischemic ECG changes are common after cardiac surgery and lack specificity for the diagnosis of myocardial infarction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".