Abstract 2884: Elevated Postoperative Cardiac Troponin I Levels Independently Predict Higher Long-Term Morbidity and Mortality Following Off-Pump CABG
Bibliographic record
Abstract
Background- Cardiac enzyme elevations after CABG are frequent, but little is known about the long-term outcomes of patients. The aims of the present study were to evaluate the prognostic significance of postoperative cTnI and CK-MB elevations on long-term morbidity and mortality following off-pump CABG. Methods- Between 1996 and 2004, 1004 consecutive off-pump CABG procedures were performed. Major adverse cardiac events (MACE) were defined as cardiac death or death of unknown cause, myocardial infarction, repeat revascularization and recurring angina. Mean length of follow-up was 4.6 ± 1.9 years (up to 9 years). Follow-up was 100% complete. Time-specific effects were estimated using parametric multiphase hazard regression. Results- Elevated cTnI (>0.3 ng/mL) and CK-MB (>50 ng/mL) were observed in 166/719 (23%) and 85/1001 (8%) of patients, respectively. Thirty-day mortality was 1.8%. The incidence of perioperative myocardial infarction was 2%. Overall 8-year survival was 71 ± 3% and cardiac survival was 93 ± 1%. Long-term mortality was independently predicted by cTnI ( p < .001 , respectively; see figure ), but not by CK-MB ( p > .05 ). Long-term incidence of MACE was independently predicted by postoperative cTnI ( p = 0.02 ), not by CKMB ( p > .05 ). MACE-free survival at 5 years ranged from 86 ± 3% for the lowest cTnI quartile to 69 ± 4% in the highest ( p < .001 ). Conclusion- Postoperative levels of cTnI bear significant prognostic value on long-term morbidity and mortality following off-pump CABG. cTnI is a more sensitive marker of long-term outcomes than CK-MB. Patients with mild cTnI elevations after CABG should be closely followed up despite an uneventful postoperative recovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".