Abstract 15667: The Transvalvular Pressure Gradient to Valve Area Ratio: A Novel, Simple Doppler Echocardiographic Parameter to Adjudicate Low-Gradient Aortic Stenosis Severity
Bibliographic record
Abstract
Background: In patients with low-gradient (LG) aortic stenosis (AS), dobutamine stress echocardiography (DSE) or aortic valve calcium scoring (AVCS) by computed tomography is required to confirm AS severity and the indication for intervention. Hypothesis: We hypothesized that the mean transvalvular gradient to effective orifice area ratio (MG/EOA, in mmHg/cm 2 ) measured during rest echocardiography predicts true-severe AS (TSAS) and clinical outcomes in LG-AS patients. The present study aimed to assess the diagnostic accuracy and prognostic value of the MG/EOA ratio in LG-AS patients. Methods: The diagnostic accuracy of the MG/EOA ratio to predict TSAS was assessed in: i) an in vitro dataset obtained in a circulatory model, and ii) an in vivo dataset collected in 198 patients recruited in the prospective observational cohort TOPAS study (NCT01835028), using receiver operating characteristic (ROC) curve analysis. The prognostic value of this parameter was assessed using spline curve analysis and multivariate Cox regression, using a composite of aortic valve replacement or all-cause mortality at 2-years as clinical endpoint. Results: The MG/EOA ratio at rest predicted TSAS with an area under the ROC curve of 0.99 [0.98-1.00] in the in vitro dataset and 0.92 [0.87-0.95] in the in vivo dataset of LG-AS patients. In the in vivo dataset predominantly composed of patients with a low flow state, the optimal cut-off of MG/EOA ratio was 29 mmHg/cm 2 with a normal flow state (percentage of correct classification: 95%) and 25 mmHg/cm 2 with a low flow state (85%). In LG-AS patients, a MG/EOA ratio ≥ 23 mmHg/cm 2 was associated with increased risk of the composite endpoint (unadjusted HR=2.16 [1.58-3.06], p<0.001; adjusted HR=2.80 [1.94-4.03], p<0.001). The net reclassification index (NRI) of the resting parameter MG/EOA was 0.57 (p<0.001), which was similar to projected EOA (NRI=0.61, p<0.001) measured by DSE. Conclusion: We propose a new, easily obtainable parameter from standard echocardiography that outperforms other rest echocardiographic parameters and is non-inferior to DSE with respect to diagnostic accuracy and prognostic value. The MG/EOA ratio can be useful in LG-AS to confirm AS severity and may thus avoid the use of additional tests such as DSE or AVCS.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".