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Record W4224247617 · doi:10.1097/mej.0000000000000921

The performance of HEAR score for identification of low-risk chest pain: a systematic review and meta-analysis

2022· review· en· W4224247617 on OpenAlexaff
Mahsan Khaleghi Rad, Mohammad Mahdi Pirmoradi, Amin Doosti‐Irani, Venkatesh Thiruganasambandamoorthy, Hadi Mirfazaelian

Bibliographic record

VenueEuropean Journal of Emergency Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineChest painMeta-analysisMEDLINEEmergency departmentConfidence intervalEmergency medicineInternal medicineAcute coronary syndromeTroponinPhysical therapyMyocardial infarction

Abstract

fetched live from OpenAlex

Chest pain is one of the most common presentations to the emergency department (ED) and HEART score (history, ECG, age, risk factors, and cardiac troponin) is recommended for risk stratification. It has been proposed that the sum of four items with no troponin (HEAR score) below 2 can be used safely to lower testing and reduce length of stay. To assess the performance of the HEAR score in hospital and prehospital settings, we performed a systematic review and meta-analysis. English studies on the performance of the HEAR score in patients with acute chest pain were included. They were excluded if data are inaccessible. MEDLINE, Embase, Evidence-Based Medicine Reviews, Scopus, and web of science were searched from 1946 to July 2021. The quality of studies was assessed using Quality Assessment of Diagnostic Accuracy Studies version 2. Acute coronary syndrome or major adverse cardiac events prediction were outcomes of interest. The performance indices with 95% confidence intervals (CIs) were extracted. Inverse variance and the random-effects model were used to report the results. Of the 692 articles on the HEAR score, 10 studies were included in the analysis with 33 843 patients. Studies were at low to moderate risk of bias. Three studies were in prehospital and three were retrospective. The pooling of data on the HEAR score showed that the sensitivity at the HEAR<2, <3, and <4 cutoffs in the ED were 99.03% (95% CI, 98.29-99.77), 97.54% (95% CI, 94.50-100), and 91.80% (95% CI, 84.62-98.98), respectively. The negative predictive values (NPVs) for the above cutoffs were 99.84% (95% CI, 99.72-99.95), 99.75% (95% CI, 99.65-99.85), and 99.57% (95% CI, 99.11-100), respectively. Of note, for the HEAR<2, negative likelihood ratio was 0.07 (95% CI, 0.02-0.12). In the prehospital, at the HEAR<4 cutoff, the pooled sensitivity and NPV were 85.01% (95% CI, 80.56-89.47) and 91.48% (95% CI, 87.10-95.87), respectively. This study showed that in the ED, the HEAR score<2 can be used for an early discharge strategy. Currently, this score cannot be recommended in prehospital setting. Prospero (CRD42021273710).

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.049
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0240.047
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.218
GPT teacher head0.415
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

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