Hyperventilation testing in the diagnosis of vasospastic angina: A clinical review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Given the limited access to invasive vasospastic reactivity testing in Western Countries, there is a need to further develop alternative non-invasive diagnostic methods for vasospastic angina (VSA). Hyperventilation testing (HVT) is defined as a class IIa recommendation to diagnose VSA by the Japanese Society of Cardiology. METHODS: In this systematic review and meta-analysis reported according to the PRISMA statement, we review the mechanisms, methods, modalities and diagnostic accuracy of non-invasive HVT for the diagnostic of VSA. RESULTS: A total of 106 articles published between 1980 and 2022 about VSA and HVT were included in the systematic review, among which 16 were included in the meta-analysis for diagnostic accuracy. Twelve electrocardiogram-HVT studies including 804 patients showed a pooled sensitivity of 54% (95% confidence intervals [CI]; 30%-76%) and a pooled specificity of 99% (95% CI; 88%-100%). Four transthoracic echocardiography-HVT studies including 197 patients revealed a pooled sensitivity of 90% (95% CI; 82%-94%) and a pooled specificity of 98% (95% CI; 86%-100%). Six myocardial perfusion imaging-HVT studies including 112 patients yielded a pooled sensitivity of 95% (95% CI; 63%-100%) and a pooled specificity of 78% (95% CI; 19%-98%). Non-invasive HVT resulted in a low rate of adverse events, ventricular arrhythmias being the most frequently reported, and were resolved with the administration of nitroglycerin. CONCLUSIONS: Non-invasive HVT offers a safe alternative with high diagnostic accuracy to diagnose VSA in patients with otherwise undiagnosed causes of chest pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".