Myocardial strain analysis by 2-dimensional speckle tracking echocardiography improves diagnostics of coronary artery stenosis in stable angina pectoris
Bibliographic record
Abstract
Purpose: To determine if 2-Dimensional Strain Echocardiography (2DSE) performed at rest in patients with suspected Stable Angina Pectoris (SAP) is able to improve the diagnosis of the presence of significant Coronary Artery Disease (CAD). Methods: In total 296 consecutive patients with clinically suspected SAP, no previous cardiac history and a normal ejection fraction were included. All patients were examined by 2DSE, Exercise Electrocardiogram (ECG) and coronary angiography. 2DSE was performed in the three apical projections. Peak Regional Longitudinal Systolic strain (RLS) was measured in 18 myocardial sites and averaged to provide Global Longitudinal Systolic strain (GLS). Duke Score (DS), including ST-depression, chest pain and exercise capacity, was used as the outcome of the exercise ECG. Results: Patients with an area stenosis ≥70% in at least one epicardial coronary artery were categorized as having significant CAD (n=108). GLS was significantly lower in patients with CAD compared to patients without CAD (17.1% vs. 18.8%, p<0.001), and remained an independent predictor of CAD after multivariable adjustment for baseline data, exercise ECG and conventional echocardiographic parameters (OR 1.17 (1.0-1.3, p=0.012) per 1% decrease). Area under receiver operating characteristics curve (AUC) for exercise ECG and GLS in combination was significantly higher than AUC for exercise ECG alone (0.84 vs. 0.78, p<0.004). Furthermore, RLS predicted significant stenosis in corresponding coronary arteries (Figure 1). Figure 1. RLS in stenotic vs non-stenotic segments Conclusion: In patients with suspected SAP GLS at rest is an independent predictor of significant coronary artery disease and significantly improves the diagnostic performance of exercise ECG. Additionally, RLS can identify which coronary artery that suffers from significant stenosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".