Characterization of Left Ventricular Hemodynamic Forces in Mitral Regurgitation Using 4-Dimensional-Flow Magnetic Resonance Imaging
Bibliographic record
Abstract
Background: Mitral regurgitation (MR) is one of the most common valvular heart diseases. Despite the recognized importance of hemodynamic force (HDF) in cardiology, its exploration in MR has been limited. Therefore, using a retrospective observational study design, we aimed to investigate the potential of noninvasively assessed systolic and diastolic HDF derived from 4-dimensional-flow magnetic resonance imaging (4D-flow MRI) as markers for determining MR. Method: The study cohort included 15 controls (19-61 years) and 26 MR patients (20 with primary and 6 cases with secondary MR, 33-75 years) classified into trivial-to-severe MR through cardiac MRI. The 3 T/4D-flow MRI sequence was used to assess left ventricular (LV) HDF (integral of intraventricular pressure gradients over the LV cavity) in 3 directions (lateral-septal, inferior-anterior, and apical-basal) using “Segment,” v2.2 R6410. Results: A negative coefficient for peak-systolic apical-basal HDF corresponded to decreased ejection fraction (β = −8.1, SE = 3.3, t = −2.5, P = .03), and a positive association was found between LV mass index and peak-systolic apical-basal HDF (β = 25.6, SE = 11.1, t = 2.3, P = .04). Peak-systolic apical-basal and E-wave apical-basal (diastolic HDF at E-wave time point) HDF emerged as significant predictors in detecting severe MR (Estimate = 2.2, P = .007; Estimate = 1.8, P = .015, respectively). The peak-systolic apical-basal HDF was also notably correlated with age in moderate-severe MR ( r = 0.88, P = .022). Conclusion: Mitral regurgitation affects early diastolic filling and peak systolic ejection, indicated by peak-systolic and E-wave HDF in the apical-basal direction, offering insights into dynamic flow patterns and LV remodeling. Hemodynamic force analysis could enhance risk stratification and guide therapeutic decisions, complementing traditional diagnostic methods for improved MR management.
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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 | 0.002 | 0.003 |
| 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".