Rule-In and Rule-Out of Myocardial Infarction Using Cardiac Troponin and Glycemic Biomarkers in Patients with Symptoms Suggestive of Acute Coronary Syndrome
Bibliographic record
Abstract
Abstract BACKGROUND Early rule-in/rule-out of myocardial infarction (MI) in patients presenting to the emergency department (ED) is important for patient care and resource allocation. Given that dysglycemia is a strong risk factor for MI, we sought to explore and compare different combinations of cardiac troponin (cTn) cutoffs with glycemic markers for the early rule-in/rule-out of MI. METHODS We included ED patients (n = 1137) with symptoms suggestive of acute coronary syndrome (ACS) who had cTnI, high-sensitivity cTnI (hs-cTnI), hs-cTnT, glucose, and hemoglobin A1c (Hb A1c) measurements. We derived rule-in/rule-out algorithms using different combinations of ROC-derived and literature cutoffs for rule-in and rule-out of MI within 7 days after presentation. These algorithms were then tested for MI/cardiovascular death and ACS/cardiovascular death at 7 days. ROC curves, sensitivity, specificity, likelihood ratios, positive and negative predictive values (PPV and NPV), and CIs were determined for various biomarker combinations. RESULTS MI was diagnosed in 133 patients (11.7%; 95% CI, 9.8–13.8). The algorithms that included cTn and glucose produced the greatest number of patients ruled out/ruled in for MI and yielded sensitivity ≥99%, NPV ≥99.5%, specificity ≥99%, and PPV ≥80%. This diagnostic performance was maintained for MI/cardiovascular death but not for ACS/cardiovascular death. The addition of hemoglobin A1c (Hb A1c) (≥6.5%) to these algorithms did not change these estimates; however, 50 patients with previously unknown diabetes may have been identified if Hb A1c was measured. CONCLUSIONS Algorithms incorporating glucose with cTn may lead to an earlier MI diagnosis and rule-out for MI/cardiovascular death. Addition of Hb A1c into these algorithms allows for identification of diabetes. Future studies extending these findings are needed for ACS/cardiovascular death. ClinicalTrials.gov identifier: NCT01994577
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".