The Utility of Systolic and Diastolic Echocardiographic Parameters for Predicting Coronary Artery Disease Burden as Defined by the <scp>SYNTAX</scp> Score
Bibliographic record
Abstract
BACKGROUND: Early identification of high-grade ischemia based on echocardiographic diastolic abnormalities may be clinically useful in the acute coronary syndrome (ACS) setting. This could provide the clinician with an awareness of the burden of coronary artery disease (CAD) before angiography is performed to allow for early intervention of suspected ischemic lesions. The objective of the study was to assess whether 2D transthoracic echocardiography (TTE)-derived tissue Doppler imaging parameters can predict the severity of CAD in comparison with the cardiac catheterization-derived SYNTAX score. METHODS: A retrospective study of 74 stable angina or patients with ACS was performed in 2012 at a single tertiary care center. In all study subjects, TTE and angiography were performed within 6 months of each other without revascularization in the interim. RESULTS: The study population included a total of 74 patients (mean age 67 ± 12 years) with 77% presenting with an ACS. The median SYNTAX score was 24.0 (6.0-35.0). The E-wave velocity was higher, and deceleration time (DT) was lower in the high SYNTAX group in comparison with the low/intermediate SYNTAX group (P = 0.045 and P = 0.001, respectively). Septal mitral annular S' was lower in the high SYNTAX group (P = 0.02). After multivariate analysis, E/A ratio (OR 0.03, 95% 0.00-0.36, P = 0.0067), DT (OR 0.93, 95% CI 0.89-0.97, P = 0.0001) and septal annular S'-wave velocity (OR 0.34, 95% CI 0.16-0.71, P = 0.0038) remained strong predictors of a high SYNTAX score. CONCLUSION: Early identification of systolic and diastolic dysfunction based on echocardiographic parameters may be of important clinical significance for predicting CAD burden prior to invasive angiography.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| 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".