Myocardial blood flow in patients with low-flow, low-gradient aortic stenosis: differences between true and pseudo-severe aortic stenosis. Results from the multicentre TOPAS (Truly or Pseudo-Severe Aortic Stenosis) study
Bibliographic record
Abstract
BACKGROUND: Impairment of myocardial flow reserve (MFR) in aortic stenosis (AS) with normal left ventricular function relates to the haemodynamic severity. OBJECTIVES: To investigate whether myocardial blood flow (MBF) and MFR differ in low-flow, low-gradient AS depending on whether there is underlying true-severe AS (TSAS) or pseudo-severe AS (PSAS). METHODS: In 36 patients with low-flow, low-gradient AS, dynamic [13N]ammonia PET perfusion imaging was performed at rest (n = 36) and during dipyridamole stress (n = 20) to quantify MBF and MFR. Dobutamine echocardiography was used to classify patients as TSAS (n = 18) or PSAS (n = 18) based on the indexed projected effective orifice area (EOA) at a normal flow rate of 250 ml/s (EOAI(proj )<or= or >0.55 cm(2)/m(2)). RESULTS: Compared with healthy controls (n = 14), patients with low-flow, low-gradient AS had higher resting mean (SD) MBF (0.83 (0.21) vs 0.69 (0.09) ml/min/g, p = 0.001), reduced hyperaemic MBF (1.16 (0.31) vs 2.71 (0.50) ml/min/g, p<0.001) and impaired MFR (1.44 (0.44) vs 4.00 (0.91), p<0.001). Resting MBF and MFR correlated with indices of AS severity in low-flow, low-gradient AS with the strongest relationship observed for EOAI(proj) (r(s) = -0.50, p = 0.002 and r(s) = 0.61, p = 0.004, respectively). Compared with PSAS, TSAS had a trend to a higher resting MBF (0.90 (0.19) vs 0.77 (0.21) ml/min/g, p = 0.06), similar hyperaemic MBF (1.16 (0.31) vs 1.17 (0.32) ml/min/g, p = NS), but a significantly smaller MFR (1.19 (0.26) vs 1.76 (0.41), p = 0.003). An MFR <1.8 had an accuracy of 85% for distinguishing TSAS from PSAS. CONCLUSIONS: Low-flow, low-gradient AS is characterised by higher resting MBF and reduced MFR that relates to the AS severity. The degree of MFR impairment differs between TSAS and PSAS and may be of value for distinguishing these entities.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".