Intra-cardiac pressure drop and flow distribution of bicuspid aortic valve disease in preserved ejection fraction
Bibliographic record
Abstract
Background Bicuspid aortic valve (BAV) is more than a congenital defect since it is accompanied by several secondary complications that intensify induced impairments. Hence, BAV patients need lifelong evaluations to prevent severe clinical sequelae. We applied 4D-flow magnetic resonance imaging (MRI) for in detail visualization and quantification of in vivo blood flow to verify the reliability of the left ventricular (LV) flow components and pressure drops in the silent BAV subjects with mild regurgitation and preserved ejection fraction (pEF). Materials and methods A total of 51 BAV patients with mild regurgitation and 24 healthy controls were recruited to undergo routine cardiac MRI followed by 4D-flow MRI using 3T MRI scanners. A dedicated 4D-flow module was utilized to pre-process and then analyze the LV flow components (direct flow, retained inflow, delayed ejection, and residual volume) and left-sided [left atrium (LA) and LV] local pressure drop. To elucidate significant diastolic dysfunction in our population, transmitral early and late diastolic 4D flow peak velocity (E-wave and A-wave, respectively), as well as E/A ratio variable, were acquired. Results The significant means differences of each LV flow component (global measurement) were not observed between the two groups (p > 0.05). In terms of pressure analysis (local measurement), maximum and mean as well as pressure at E-wave and A-wave timepoints at the mitral valve (MV) plane were significantly different between BAV and control groups (p: 0.005, p: 0.02, and p: 0.04 and p: <0.001; respectively). Furthermore, maximum pressure and pressure difference at the A-wave timepoint at left ventricle mid and left ventricle apex planes were significant. Although we could not find any correlation between LV diastolic function and flow components, Low but statistically significant correlations were observed with local pressure at LA mid, MV and LV apex planes at E-wave timepoint (R: −0.324, p: 0.005, R: −0.327, p: 0.004, and R: −0.306, p: 0.008, respectively). Conclusion In BAV patients with pEF, flow components analysis is not sensitive to differentiate BAV patients with mild regurgitation and healthy control because flow components and EF are global parameters. Inversely, pressure (local measurement) can be a more reliable biomarker to reveal the early stage of diastolic dysfunction.
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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 | 0.000 | 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 | 0.000 | 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".