Role of Non-Invasive Hemodynamic Forces through Four-Dimensional-Flow Magnetic Resonance Imaging (4D-Flow MRI) in Evaluating Mitral Regurgitation with Preserved Ejection Fraction: Seeking Novel Biomarkers
Bibliographic record
Abstract
Mitral regurgitation (MR) is the systolic retrograde flow from the left ventricle (LV) to the left atrium. Despite the recognized importance of hemodynamic force (HDF) in cardiology, its exploration in MR has been limited. Therefore, we aimed to explore non-invasively assessed HDF as a novel biomarker for evaluating MR utilizing 4D-flow MRI. The study cohort comprised 15 healthy controls (19–61 years, 53% men) and 26 MR patients with preserved ejection fraction (EF) (33–75 years, trivial–severe, 54% men). The HDF analysis involved the semi-automatic calculation of systolic–diastolic root mean square (RMS), average, and transverse/longitudinal ratio across three directions (S-L: septal–lateral, I-A: inferior–anterior, and B-A: basal–apical) using Segment, v2.2 R6410 (Lund, Sweden, Medviso). A noticeable trend shift emerged in HDF as the MR severity increased (p-value < 0.05). The MR severity demonstrated a noteworthy correlation with systolic RMS B-A, average B-A, diastolic average B-A, systolic average S-L, B-A, and systolic–diastolic ratio (rho = 0.621, 0.457, 0.317, 0.318, 0.555, −0.543, −0.35, respectively; p-value < 0.05). HDF significantly correlated with LV function (end-diastolic volume, end-systolic volume, EF, and mass; p-value < 0.05). Systolic RMS B-A and diastolic RMS S-L emerged as significant predictors of MR (Beta, 95% CI [3.253, 1.204–5.301], [5.413, 0.227–10.6], p-value < 0.05). This study emphasizes HDF as a potential hemodynamic biomarker for evaluating MR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".