High sensitivity troponin-I threshold to predict perioperative myocardial infarction
Bibliographic record
Abstract
BACKGROUND: High-sensitivity Troponin I (hs-cTnI) has largely replaced conventional troponin assays in an effort to improve detection of myocardial infarction. However, the mean displacement of hs-cTnI following coronary artery bypass graft (CABG) and the optimal threshold to detect perioperative myocardial infarction (MI) is unclear. Our objective is to describe mean hs-cTnI values at 6-12 h post-CABG and to determine the highest specificity while maintaining 100% sensitivity hs-cTnI cut-off values for diagnosis of perioperative or type-5 MI. METHODS: Between 2016 and 2018, 374 patients underwent non-emergent, isolated CABG. Pre-operative and 6 h post-operative hs-cTnI values were recorded as well as ECG, echocardiographic and angiographic data. RESULTS: Of 374 patients, 151 (40.3%) had normal and 224 (59.7%) had elevated preoperative hs-cTnI. Patients with normal preoperative hs-cTnI had a mean 6 h hs-cTnI of 9193 ng/l or 270X the upper normal value. Eleven patients (7.3%) presented with post-operative MI with a mean 6 h hs-cTnI of 50,218 ng/l or 1477X the upper normal value. Patients with elevated preoperative hs-cTnI had a mean 6 h hs-cTnI of 9449 ng/l or 292X the upper normal value. Eleven patients (4.9%) who presented with post-operative MI had a mean 6 h hs-cTnI of 26,823 ng/l or 789X the upper normal value. CONCLUSIONS: We suggest hs-cTnI threshold of 80-fold in patients with normal pre-operative hs-cTnI and 2.7-fold in patients with elevated pre-operative hs-cTnI. These results have important implications for perioperative care and for surgical trial reporting.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".