Loss of base-to-apex circumferential strain gradient assessed by cardiovascular magnetic resonance in Fabry disease: relationship to T1 mapping, late gadolinium enhancement and hypertrophy
Bibliographic record
Abstract
Background Cardiac involvement is common and is the leading cause of mortality in Fabry disease (FD). We explored the association between cardiovascular magnetic resonance (CMR) myocardial strain, T1 mapping, late gadolinium enhancement (LGE) and left ventricular hypertrophy (LVH) in patients with FD. Methods In this prospective study, 38 FD patients (45.0 ± 14.5 years, 37% male) and 8 healthy controls (40.1 ± 13.7 years, 63% male) underwent 3 T CMR including cine balanced steady-state free precession (bSSFP), LGE and modified Look-Locker Inversion recovery (MOLLI) T1 mapping. Global longitudinal (GLS) and circumferential (GCS) strain and base-to-apex longitudinal strain (LS) and circumferential strain (CS) gradients were derived from cine bSSFP images using feature tracking analysis. Results Among FD patients, 8 had LVH (FD LVH+, 21%) and 17 had LGE (FD LGE+, 45%). Nineteen FD patients (50%) had neither LVH nor LGE (FD LVH- LGE-). None of the healthy controls had LVH or LGE. FD patients and healthy controls did not differ significantly with respect to GLS (− 15.3 ± 3.5% vs. − 16.3 ± 1.5%, p = 0.45), GCS (− 19.4 ± 3.0% vs. -19.5 ± 2.9%, p = 0.84) or base-to-apex LS gradient (7.5 ± 3.8% vs. 9.3 ± 3.5%, p = 0.24). FD patients had significantly lower base-to-apex CS gradient (2.1 ± 3.7% vs. 6.5 ± 2.2%, p = 0.002) and native T1 (1170.2 ± 37.5 ms vs. 1239.0 ± 18.0 ms, p < 0.001). Base-to-apex CS gradient differentiated FD LVH- LGE- patients from healthy controls (OR 0.42, 95% CI: 0.20 to 0.86, p = 0.019), even after controlling for native T1 (OR 0.24, 95% CI: 0.06 to 0.99, p = 0.049). In a nested logistic regression model with native T1, model fit was significantly improved by the addition of base-to-apex CS gradient (χ 2 (df = 1) = 11.04, p < 0.001). Intra- and inter-observer agreement were moderate to good for myocardial strain parameters: GLS (ICC 0.849 and 0.774, respectively), GCS (ICC 0.831 and 0.833, respectively), and base-to-apex CS gradient (ICC 0.737 and 0.613, respectively). Conclusions CMR reproducibly identifies myocardial strain abnormalities in FD. Loss of base-to-apex CS gradient may be an early marker of cardiac involvement in FD, with independent and incremental value beyond native T1.
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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 | 0.001 |
| 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 | 0.001 | 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".