Cardiac Biomarkers, Echocardiography, and Outpatient Cardiac Monitoring for Evaluation of Emergency Department Patients With Syncope: A Systematic Review and Analysis of Direct Evidence for <scp>SAEM GRACE</scp>
Bibliographic record
Abstract
BACKGROUND: Syncope places a significant burden on emergency departments (EDs), often prompting extensive testing to exclude life-threatening conditions. However, the diagnostic utility of troponin, B-type natriuretic peptide (BNP), transthoracic echocardiography (TTE), and outpatient cardiac monitoring remains unclear. METHODS: This systematic review assessed the diagnostic accuracy of these tests in adults presenting with syncope. The research question was: In ED patients with syncope, does TTE, cardiac biomarkers (troponin, BNP), or outpatient arrhythmia monitoring, compared with no testing, improve outcomes within 30 days? Primary outcomes included adverse events (death, arrhythmias, structural/ischemic heart disease, and select non-cardiac causes such as pulmonary embolism or aortic dissection) for biomarkers and diagnostic yield for TTE and monitoring. Sensitivity, specificity, and likelihood ratios (LR+ and LR-) were calculated for biomarkers, while diagnostic yield with 95% CI was reported for TTE and monitoring. Risk of bias was assessed using JBI and QUADAS-2. RESULTS: > 90%) precluded meta-analysis. For BNP, LR+ ranged 1.4-47 and LR- 0.06-0.4; for troponin, LR+ 1.9-11.2 and LR- 0.2-0.9. TTE diagnostic yield was 0%-29% overall and 8%-28% in high-risk groups. Outpatient monitoring yielded 1%-59% overall and 12%-42% in high-risk patients. CONCLUSION: In ED patients with syncope, the diagnostic accuracy and yield of cardiac biomarkers, TTE, and outpatient monitoring show substantial variability, largely due to differences in patient populations, outcome measures, and study methodologies. Based on the existing evidence, these modalities in isolation cannot be recommended for routine use in syncope evaluation. Among these tests, the diagnostic yield of TTE and outpatient monitoring is greater in patients with cardiac risk factors and could potentially contribute to a more accurate diagnosis.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.005 | 0.005 |
| 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".