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 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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".