Risk Scores for Clinical Risk Stratification of Emergency Department Patients With Chest Pain but No Acute Myocardial Infarction: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Chest pain is a common cause for emergency department (ED) presentations. After myocardial infarction (MI) has been ruled out by means of electrocardiography and troponin testing, decisions around anatomic or functional testing may be informed by clinical risk scores. We conducted a systematic review to synthesize evidence of the prognostic performance of chest pain risk scores among ED patients who have had MI ruled out by means of a high-sensitivity troponin assay. METHODS: We queried multiple databases from inception to May 17, 2022. We included studies that quantified risk of 30-day major adverse cardiac events (MACE), at different cutoffs of clinical risk scores, among adult patients who had MI ruled out by means of a high-sensitivity troponin assay. Prognostic performance of each score was synthesized and described, but meta-analysis was not possible. RESULTS: Six studies met inclusion criteria. Short-term MACE risk among patients who had MI ruled out by means of high-sensitivity cardiac troponin assays was very low. The HEART score, with a cutoff of 3 or less, predicted a very low risk of MACE among the greatest proportion of patients. Other scores had lower sensitivity or classified fewer patients as low risk. CONCLUSIONS: The HEART score with a cutoff value of 3 or less accurately identified the greatest number of patients at low risk of 30-day MACE. However, MACE risk among patients who have MI ruled out by means of high-sensitivity troponin testing is sufficiently low that clinical risk stratification or noninvasive testing may be of little additional value in identifying patients with coronary disease.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".