Abstract 18065: Combining Echo Doppler and CT Derived LVOT Measurements Improve Accuracy of Continuity Equation Based Aortic Valve Area Determination
Bibliographic record
Abstract
Background: Left ventricular outflow tract (LVOT) measurement by Echo falsely assumes circular LVOT with implications for aortic valve area (AVA) determined using continuity equation. In this study, we investigated the utility of combining Echo Doppler and computed tomography (CT) based LVOT area for precise AVA assessment. Methods: AVA-echo was calculated using continuity equation and standard echocardiography techniques. CT-LVOT area was measured by 3 independent readers. Blinded measurements were obtained in candidates for percutaneous aortic valve replacement. AVA-CT was calculated using CT-LVOT area and echo Doppler measurements. The inter-observer variability for CT-LVOT area and correlation between AVA-echo and AVA-CT was analyzed using intra-class (ICC) and Spearman correlation coefficient. Results: Complete data for 66 patients was analyzed. The ICC for CT derived LVOT measurement by three observers was 0.96 demonstrating excellent reliability. The LVOT area by echo was significantly smaller than that obtained by CT (3.43±0.8 vs. 4.45±1.2 cm2, p<0.0001) and showing moderate correlation (r=0.66, p=0.006). Similarly, the AVA-echo was significantly lower compared to AVA-CT (0.76±0.46 vs. 0.97±0.53 cm2, p<0.0001). The correlation coefficient for AVA-echo and AVA-CT was 0.7 (p < 0.0001), 0.4 (p=0.06) and 0.9 (p<0.0001) for AVA-echo of 0.8 cm2 respectively. The regression analysis showed that AVA-CT could be predicted by applying a correction factor to the AVA-echo as AVA-CT = AVA-echo x 1.1+0.14. Conclusion: CT provides accurate and reliable LVOT assessment. Combining CT-LVOT and echo Doppler measurements result in a larger AVA compared to AVA-echo representing a true anatomic measurement. These findings have important implications for grading of aortic stenosis and management of patients with moderate to severe aortic 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".