Incidence and outcomes of perioperative myocardial infarction/injury diagnosed by high-sensitivity cardiac troponin I
Bibliographic record
Abstract
Abstract Background Perioperative myocardial infarction/injury (PMI) diagnosed by high-sensitivity troponin (hs-cTn) T is frequent and a prognostically important complication of non-cardiac surgery. We aimed to evaluate the incidence and outcome of PMI diagnosed using hs-cTnI, and compare it to PMI diagnosed using hs-cTnT. Methods We prospectively included 2455 patients at high cardiovascular risk undergoing 3111 non-cardiac surgeries, for whom hs-cTnI and hs-cTnT concentrations were measured before surgery and on postoperative days 1 and 2. PMI was defined as a composite of perioperative myocardial infarction (PMI Infarct ) and perioperative myocardial injury (PMI Injury ), according to the Fourth Universal Definition of Myocardial Infarction. All-cause mortality was the primary endpoint. Results Using hs-cTnI, the incidence of overall PMI was 9% (95% confidence interval [CI] 8–10%), including PMI Infarct 2.6% (95% CI 2.0–3.2) and PMI Injury 6.1% (95% CI 5.3–6.9%), which was lower versus using hs-cTnT: overall PMI 15% (95% CI 14–16%), PMI Infarct 3.7% (95% CI 3.0–4.4) and PMI Injury 11.3% (95% CI 10.2–12.4%). All-cause mortality occurred in 52 (2%) patients within 30 days and 217 (9%) within 1 year. Using hs-cTnI, both PMI Infarct and PMI Injury were independent predictors of 30-day all-cause mortality (adjusted hazard ratio [aHR] 2.5 [95% CI 1.1–6.0], and aHR 2.8 [95% CI 1.4–5.5], respectively) and, 1-year all-cause mortality (aHR 2.0 [95% CI 1.2–3.3], and aHR 1.8 [95% CI 1.2–2.7], respectively). Overall, the prognostic impact of PMI diagnosed by hs-cTnI was comparable to the prognostic impact of PMI using hs-cTnT. Conclusions Using hs-cTnI, PMI is less common versus using hs-cTnT. Using hs-cTnI, both PMI Infarct and PMI Injury remain independent predictors of 30-day and 1-year mortality. Graphic abstract
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".