Prognostic Accuracy of the <scp>HEART</scp> Score for Prediction of Major Adverse Cardiac Events in Patients Presenting With Chest Pain: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: The HEART score has been proposed for emergency department (ED) prediction of major adverse cardiac events (MACE). We sought to summarize all studies assessing the prognostic accuracy of the HEART score for prediction of MACE in adult ED patients presenting with chest pain. METHODS: We searched MEDLINE, PubMed, EMBASE, Scopus, Web of Science, and the Cochrane Database of Systematic Reviews from inception through May 2018 and included studies using the HEART score for the prediction of short-term MACE in adult patients presenting to the ED with chest pain. The main outcome was short-term (i.e., 30-day or 6-week) incidence of MACE. We secondarily evaluated the prognostic accuracy of the HEART score for prediction of mortality and myocardial infarction (MI). Where available, accuracy of the Thrombolysis in Myocardial Infarction (TIMI) score was determined. RESULTS: We included 30 studies (n = 44,202) in analysis. A HEART score above the low-risk threshold (≥4) had a sensitivity of 95.9% (95% confidence interval [CI] = 93.3%-97.5%) and specificity of 44.6% (95% CI = 38.8%-50.5%) for MACE. A high-risk HEART score (≥7) had a sensitivity of 39.5% (95% CI = 31.6%-48.1%) and specificity of 95.0% (95% CI = 92.6%-96.6%) for MACE, whereas a TIMI score above the low-risk threshold (≥2) had a sensitivity of 87.8% (95% CI = 80.2%-92.8%) and specificity of 48.1% (95% CI = 38.9%-57.5%) for MACE. A high-risk TIMI score (≥6) was 2.8% sensitive (95% CI = 0.8%-9.6%), but 99.6% (95% CI = 98.5%-99.9%) specific for MACE. A HEART score ≥ 4 had a sensitivity of 95.0% (95% CI = 87.2%-98.2%) for prediction of mortality and 97.5% (95% CI = 93.7%-99.0%) for prediction of MI. CONCLUSIONS: The HEART score has excellent performance for prediction of MACE (particularly mortality and MI) in chest pain patients and should be the primary clinical decision instrument used for the risk stratification of this patient population.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".