Regional Response of Myocardial Acceleration During Isovolumic Contraction During Dobutamine Stress Echocardiography: A Color Tissue Doppler Study and Comparison with Angiocardiographic Findings
Bibliographic record
Abstract
BACKGROUND: Color tissue Doppler imaging permits noninvasive quantitation of regional wall motion. In experimental studies, a new marker, the slope of the isovolumic contraction wave, isovolumic acceleration (IVA) was more insensitive to ventricular loading conditions than myocardial velocities. This study compared the regional response IVA to dobutamine stress echocardiography to angiographic findings. METHODS: The Myocardial Doppler in Stress Echocardiography (MYDISE) study prospectively recruited 149 consecutive patients with chest pain for dobutamine stress tissue Doppler echocardiography prior to coronary angiography. This color tissue Doppler database was analyzed for IVA in 1,192 basal and mid segments at rest and again at peak stress. Angiographic findings were compared to IVA and peak systolic velocities (PSV) in corresponding cardiac segments. The diagnostic accuracy of IVA to predict coronary artery stenosis was determined using cut-off values for three representative segments and with the MYDISE diagnostic model including eight segments. RESULTS: Regional IVA increased in a dose-dependent manner during dobutamine infusion. The response was blunted in the supply territory of stenosed coronary artery branches. IVA performed slightly better than PSV as single marker for coronary artery stenosis. A diagnostic model incorporating IVA and PSV was 85-95% accurate (area under receiver operating characterstic curves). CONCLUSIONS: Regional changes of isovolumic acceleration during dobutamine stress echocardiography reflect regional wall motion and can be used to predict coronary artery stenosis with similar accuracy as a model based on systolic myocardial velocities. As a single marker, IVA performed better than myocardial velocities.
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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 | 0.000 | 0.002 |
| 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 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".