Evaluation of energy loss in patients with severe primary valvular heart disease before cardiac valve intervention
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background. Valvular heart disease (VHD) determines non-physiological, inefficient blood flow within the left ventricle, resulting in abnormal vortex formation and energy loss (EL). EL evaluation could provide valuable insights in addition to more common parameters of left ventricle systolic and diastolic dysfunction. Vector flow mapping (VFM) is a novel, non-invasive echocardiographic technique that measures EL through the study of intraventricular flow. Purpose. To assess EL throughout the whole cardiac cycle in patients with severe primary left-sided VHD before cardiac valve intervention. Methods. VFM is based on the continuity equation applied to colour Doppler and speckle tracking echocardiography, acquired from the apical long-axis view. VFM estimates blood flow velocity and vortex characteristics to quantify energy dissipation (i.e., EL) due to blood viscosity in a turbulent flow. EL was calculated frame by frame and averaged over three beats. Results. We enrolled 20 healthy controls (55 ± 19 years old, 65% male) and 73 patients (70 ± 17 years old, 59% male) with severe VHD before cardiac surgery: 30 with primary mitral regurgitation (MR), 8 with mitral stenosis (MS), 15 with aortic regurgitation (AR), 20 with aortic stenosis (AS). All patients had a left ventricle (LV) ejection fraction ≥50% and no wall motion abnormalities. We observed an increased number of vortexes in patients with VHD when compared to controls, especially in mid-diastole (p = 0.003). This is reflected in a significantly higher EL during the whole cardiac cycle in VHD patients than controls (p < 0.0001), with the highest values observed in MS and AR (post-hoc test: all p < 0.0.1; Figure 1 and Figure 2). The differences were driven by the diastolic EL (p < 0.0001), while the systolic EL values were similar between patients with VHD and controls (p = ns). Conclusions. In addition to standard baseline echocardiography, VFM can quantitatively evaluate the energy dissipation in different subsets of VHD. EL is not uniform during the cardiac cycle, as diastole seems significantly more affected than systole. The assessment of EL after valve intervention is ongoing. VFM could provide further insights into the pathophysiology of heart valve disease and help to evaluate the efficacy of the procedure (repair/replacement) performed. Abstract Figure 1 Abstract Figure 2
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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 | 0.002 |
| 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) | 0.002 | 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".