The HEART score in predicting major adverse cardiac events in patients presenting to the emergency department with possible acute coronary syndrome: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Acute coronary syndrome (ACS) is a common, sometimes difficult to diagnose spectrum of diseases occurring after abrupt reduction in blood flow through a coronary artery. Given the diagnostic challenge, it is sensible for emergency physicians to have an approach to prognosticate patients with possible ACS. Multiple prediction models have been developed to help identify patients at increased risk of adverse outcomes. The HEART score is the first model to be derived, validated, and undergo clinical impact studies in emergency department (ED) patients with possible ACS. OBJECTIVE: To develop a protocol for a prognostic systematic review of the literature evaluating the HEART score as a predictor of major adverse cardiac events (MACE) in patients presenting to the ED with possible ACS. METHODS/DESIGN: This protocol is reported according to the PRISMA-P statement and is registered on PROSPERO. All methodological tools to be used are endorsed by the Cochrane Prognosis Methods Group. Pre-defined eligibility criteria are provided. Multiple strategies will be used to identify potentially relevant studies. Studies will be selected and data extracted using standardised forms based on the CHARMS checklist. The QUIPS tool will be used to assess the risk of bias within individual studies. Outcome measures will include prevalence, risk ratio, and absolute risk reduction for MACE within 6 weeks of ED evaluation, comparing HEART scores 0-3 versus 4-10. HEART score prognostic performance will be evaluated with the concordance (C) statistic (model discrimination), observed to expected events ratio (model calibration), and a decision curve analysis. Reporting biases and methodological, clinical, and statistical heterogeneity will be scrutinised. Unless deemed inappropriate, a meta-analysis and pre-defined subgroup and sensitivity analyses will be performed. Overall judgements about evidence quality and strength of recommendations will be summarised using the GRADE approach. DISCUSSION: This review will identify, select, and appraise studies evaluating the prognostic performance of the HEART score, producing results of interest to emergency physicians. These results may encourage shared clinical decision-making in the ED by facilitating risk communication with patients and health care providers. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2017 CRD42017084400 .
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 | 0.057 | 0.094 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.004 |
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".