Improving Stress Echocardiography for Enhanced Detection of Left Main and Multivessel Coronary Artery Disease
Bibliographic record
Abstract
Background Stress echocardiography (SE) is a well-established method for diagnosing and assessing coronary artery disease (CAD). However, accurately identifying high-risk patients remains a challenge. The aim of the study is to explore the potential of novel predictors to enhance diagnostic precision of SE for detecting left-main or triple-vessel CAD. Methods We included consecutive patients who underwent invasive coronary angiography (ICA) within six months of SE between January 2018 and April 2022. Traditional high-risk SE features included extensive wall motion abnormalities, a reduction in left ventricular ejection fraction ≥ 10% or LV dilation at peak stress, and low ischemic threshold. Wall motion score index and global longitudinal strain (GLS) at rest and peak stress, as well as the change from rest to peak in both were considered as potential additional indicators of high-risk anatomy, defined as significant stenoses in the left main and/or triple-vessel CAD. Results Of the 257 patients (mean age 66 ± 9 years) included in the analysis, 53 (21%) had high-risk CAD. Multivariate analyses identified traditional high-risk SE features, as well as ≥ 5% absolute reduction in GLS as independent predictors of high-risk anatomy. Integrating Δ GLS ≥ 5% into standard stress echocardiography evaluation significantly improved sensitivity from 69% to 90% (p=0.003), with an associated specificity of 72% and improving the AUC of SE increasing from 0.77 to 0.81 for detection of high-risk CAD. Conclusion In a real-world cohort, adding the change in GLS with exercise stress can improve the performance of SE for the detection of high-risk CAD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".