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 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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".