Are Cardiac Biomarkers the Key to Solving the Syncope Mystery?
Bibliographic record
Abstract
Are Cardiac Biomarkers the Key to Solving the Syncope Mystery?Article, see p 2403 S yncope is a common presenting symptom to the emergency department (ED), comprising 1.0% to 2.4% of all visits. 1 Establishing the cause can be difficult and results in expensive investigations, specialist evaluations, 2 and hospitalizations (ranging from 12% to 86%).3,4 Reflex syncope is the most frequent cause of syncope regardless of age and healthcare settings, 3 and cardiac causes of syncope may be responsible for only 5% to 21% of syncope events seen in the ED.5,6 Prompt identification of cardiac syncope has important diagnostic and prognostic implications.Biomarkers that reflect myocardial cell damage, such as cardiac troponin, and cardiac dysfunction, such as B-type natriuretic peptides, might be specific for cardiac syncope, but, because of the limitations of previous data, 7,8 the role of cardiac biomarkers in the investigation of syncope is unclear.1,3 More robust evidence is clearly needed.In this issue of Circulation, du Fay de Lavallaz et al 9 report on the results of a large, prospective, multicenter study evaluating the utility of B-type natriuretic peptide, N-terminal pro B-type natriuretic peptide, high-sensitivity cardiac troponin T, and high-sensitivity cardiac troponin I for diagnosing cardiac syncope and predicting short-and long-term mortality and major cardiovascular adverse events.The subjects were patients >45 years of age presenting to the ED within 12 hours of a syncope event.A blinded, standardized clinical assessment was performed on each patient, and detailed data (history, vital signs, physical examination, 12-lead ECG, laboratory tests, imaging, and any additional tests) were collected then adjudicated for a true syncope event and for the cause of syncope by using guideline-defined definitions.3 Outcome events during a follow-up of 2 years were determined through patient contact, hospital records, family physician records, and the national mortality registry.The key findings are that (1) elevated biomarkers alone or in combination have moderate-to-good diagnostic accuracy, as quantified by area under the curve, for cardiac syncope; (2) applying a predefined biomarker threshold for sensitivity and specificity of ≥95% allowed rule-in or rule-out of ≈30% of patients; and (3) biomarkers had high accuracy at predicting adverse outcomes and performed better than some existing syncope risk prediction tools.How should these results be viewed?Cardiac biomarkers are readily available, accurate, and inexpensive, and the results from this study suggest their potential to simplify diagnosis and risk stratify in challenging presentations.Before embracing the concept of ordering cardiac biomarkers routinely for every syncope presentation, we should provide some perspective to this report.First, how certain are the diagnoses?10 History-taking is key to identifying an underlying cause of syncope and, if performed properly, can by itself be a powerful diagnostic tool.11 It should contain historical details before, at the onset, dur-
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.032 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 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".