Functional Changes in Coronary Microcirculation After Valve Replacement in Patients With Aortic Stenosis
Bibliographic record
Abstract
BACKGROUND: Increased extravascular compression and reduced diastolic perfusion time (DPT), rather than vascular remodeling, influence coronary microcirculatory dysfunction in aortic stenosis (AS). However, alterations after aortic valve replacement (AVR) remain unclear. The aim of the present study was to quantify changes in transmural perfusion and coronary vasodilator reserve (CVR), a measure of microcirculatory function, after AVR and determine the relative contribution of left ventricular mass (LVM) regression, change in aortic valve area (AVA), and DPT. METHODS AND RESULTS: Twenty-two patients with AS were studied before and 1 year after AVR using echocardiography to measure AVA, cardiovascular magnetic resonance to assess LVM, and positron emission tomography to quantify resting and hyperemic myocardial blood flow (MBF) and CVR. Regression of LVM occurred in all patients (from 129+/-30 to 94+/-24 g/m2; P<0.0001), and there was a significant reduction in resting MBF and increase in CVR corrected for rate-pressure product after AVR, although these changes displayed marked heterogeneity. Regression of LVM was linearly related to change in resting total LV blood flow but not CVR. Increase in hyperemic MBF and CVR transmurally was directly related to the increase in AVA after AVR. A significant relationship existed between the change in hyperemic DPT (1.0+/-4.7 s/min [range, 6.8 to 9.6]) and change in transmural CVR (y=0.08x+0.18; r=0.44; P=0.04). CONCLUSIONS: Changes in coronary microcirculatory function in patients with AS after AVR are not directly dependent on regression of LVM. Reduced extravascular compression and increased DPT are proposed as the main mechanisms for improvement in MBF and CVR after AVR.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".