Assessment of the European Society of Cardiology 0-Hour/1-Hour Algorithm to Rule-Out and Rule-In Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: The new European Society of Cardiology guidelines to rule-in and rule-out acute myocardial infarction (AMI) in the emergency department include a rapid assessment algorithm based on high-sensitivity cardiac troponin and sampling at 0 and 1 hour. Emergency department physicians require high sensitivity to confidently rule-out AMI, whereas cardiologists aim to minimize false-positive results. METHODS: High-sensitivity troponin I and T assays were used to measure troponin concentrations in patients presenting with chest-pain symptoms and being investigated for possible acute coronary syndrome at hospitals in New Zealand, Australia, and Canada. AMI outcomes were independently adjudicated by at least 2 physicians. The European Society of Cardiology algorithm performance with each assay was assessed by the sensitivity and proportion with AMI ruled out and the positive predictive value and proportion ruled-in. RESULTS: There were 2222 patients with serial high-sensitivity troponin T and high-sensitivity troponin I measurements. The high-sensitivity troponin T algorithm ruled out 1425 (64.1%) with a sensitivity of 97.1% (95% confidence interval [CI], 94.0%-98.8%) and ruled-in 292 (13.1%) with a positive predictive value of 63.4% (95% CI, 57.5%-68.9%).The high-sensitivity troponin I algorithm ruled out 1205 (54.2%) with a sensitivity of 98.8% (95% CI, 96.4%-99.7%)) and ruled-in 310 (14.0%) with a positive predictive value of 68.1% (95% CI, 62.6%-73.2%). CONCLUSIONS: The sensitivity of the European Society of Cardiology rapid assessment 0-/1-hour algorithm to rule-out AMI with high-sensitivity troponin may be insufficient for some emergency department physicians to confidently send patients home. These algorithms may prove useful to identify patients requiring expedited management. However, the positive predictive value was modest for both algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".