Comparison between cardiovascular magnetic resonance and transthoracic doppler echocardiography for the estimation of effective orifice area in aortic stenosis
Bibliographic record
Abstract
BACKGROUND: The effective orifice area (EOA) estimated by transthoracic Doppler echocardiography (TTE) via the continuity equation is commonly used to determine the severity of aortic stenosis (AS). However, there are often discrepancies between TTE-derived EOA and invasive indices of stenosis, thus raising uncertainty about actual definite severity. Cardiovascular magnetic resonance (CMR) has emerged as an alternative method for non-invasive estimation of valve EOA. The objective of this study was to assess the concordance between TTE and CMR for the estimation of valve EOA. METHODS AND RESULTS: 31 patients with mild to severe AS (EOA range: 0.72 to 1.73 cm2) and seven (7) healthy control subjects with normal transvalvular flow rate underwent TTE and velocity-encoded CMR. Valve EOA was calculated by the continuity equation. CMR revealed that the left ventricular outflow tract (LVOT) cross-section is typically oval and not circular. As a consequence, TTE underestimated the LVOT cross-sectional area (ALVOT, 3.84 ± 0.80 cm2) compared to CMR (4.78 ± 1.05 cm2). On the other hand, TTE overestimated the LVOT velocity-time integral (VTILVOT: 21 ± 4 vs. 15 ± 4 cm). Good concordance was observed between TTE and CMR for estimation of aortic jet VTI (61 ± 22 vs. 57 ± 20 cm). Overall, there was a good correlation and concordance between TTE-derived and CMR-derived EOAs (1.53 ± 0.67 vs. 1.59 ± 0.73 cm2, r = 0.92, bias = 0.06 ± 0.29 cm2). The intra- and inter- observer variability of TTE-derived EOA was 5 ± 5% and 9 ± 5%, respectively, compared to 2 ± 1% and 7 ± 5% for CMR-derived EOA. CONCLUSION: Underestimation of ALVOT by TTE is compensated by overestimation of VTILVOT, thereby resulting in a good concordance between TTE and CMR for estimation of aortic valve EOA. CMR was associated with less intra- and inter- observer measurement variability compared to TTE. CMR provides a non-invasive and reliable alternative to Doppler-echocardiography for the quantification of AS severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| 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".