Abnormal myocardial perfusion in hypertrophic cardiomyopathy: preliminary findings of a cardiovascular MRI study
Bibliographic record
Abstract
Reduced myocardial perfusion has been speculated as a potential mechanism for the development and/or propagation of myocardial fibrosis in hypertrophic cardiomyopathy (HCM). This study aims to evaluate the prevalence, distribution and extent of stress-induced perfusion abnormalities and their relationship to underlying fibrosis in patients with HCM using magnetic resonance imaging. 15 patients with echocardiographically diagnosed HCM have been enrolled. Cine imaging, first-pass stress perfusion imaging using vasodilator stress (Dipyridamole), and delayed gadolinium enhancement imaging were performed. Stress hypoperfusion and delayed enhancement images were assessed both quantitatively and visually using a 16-segment model. Conversion of segmental visual scoring to % of LV by volume was achieved for both hypoperfusion (HP) and late enhancement (LE) using a standardized scoring system. For quantitative assessment prospectively defined cut-offs for LE and HP were used. Maximal wall thickness ranged from 13 to 22 mm (mean 17 ± 2.6 mm). Non-ischemic pattern LE was present in 70% of patients. Perfusion abnormalities were identified on stress perfusion images in 80% of patients using visual analysis and 87% of patients using quantitative analysis. Perfusion abnormalities were predominantly subendocardial, and were regionally associated with segments containing LE (p < 0.01). Mean percent HP and mean percent LE were 17 ± 8.4% and 10 ± 9.3%, respectively by visual estimation and 20.0 ± 12.1% and 14.0 ± 7.4%, respectively by quantitative assessment. Figure 1 . Short axis cine (top) , delayed contrast (middle row) and stress perfusion (bottom row) images in a patient with apical hypertrophic cardiomyopathy . Stress perfusion abnormalities seen corresponding to (white arrows) and distinct from (yellow arrows) established fibrosis. These preliminary results suggest that patients with HCM have a high prevalence of stress-induced myocardial hypoperfusion as represented by reduced first-pass gadolinium enhancement during vasodilator stress. This hypoperfusion appears to extend beyond regions of established LE suggesting a potential contribution of ischemia in the development and/or propagation of myocardial fibrosis in patients with HCM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".