Magnetic Resonance Left Ventricle Mass-Index/Fibrosis: Long-Term Predictors for Ventricular Arrhythmia in Hypertrophic Cardiomyopathy—A Retrospective Registry
Bibliographic record
Abstract
Objective: We aimed to study the long-term association of LV mass index (LVMI) and myocardial fibrosis with ventricular arrhythmia (VA) in a population of patients with confirmed hypertrophic cardiomyopathy (HCM) using cardiac magnetic resonance imaging (CMR). Methods: We retrospectively analyzed the data in consecutive HCM patients confirmed on CMR referred to an HCM clinic between January 2008 and October 2018. Patients were followed up yearly following diagnosis. Baseline demographics, risk factors and clinical outcomes from cardiac monitoring and an implanted cardioverter defibrillator (ICD) were analyzed for association of LVMI and LV late gadolinium enhancement (LVLGE) with VA. Patients were then allocated to one of two groups according to the presence of VA (Group A) or absence of VA (Group B) during the follow-up period. The transthoracic echocardiogram (TTE) and CMR parameters were compared between the two groups. Results: A total of 247 patients with confirmed HCM (age 56.2 ± 16.6, male = 71%) were studied over the follow-up period of 7 ± 3.3 years (95% CI = 6.6–7.4 years). LVMI derived from CMR was higher in Group A (91.1 ± 28.1 g/m2 vs. 78.8 ± 28.3 g/m2, p = 0.003) when compared to Group B. LVLGE was higher in Group A (7.3 ± 6.3% vs. 4.7 ± 4.3%, p = 0.001) when compared to Group B. Multivariable Cox regression analysis showed LVMI (hazard ratio (HR) = 1.02, 95% CI = 1.001–1.03, p = 0.03) and LVLGE (HR = 1.04, 95% CI = 1.001–1.08, p = 0.04) to be independent predictors for VA. Receiver operative curves showed higher LVMI and LVLGE with a cut-off of 85 g/m2 and 6%, respectively, to be associated with VA. Conclusions: LVMI and LVLGE are strongly associated with VA over long-term follow-up. LVMI requires more thorough studies to consider it as a risk stratification tool 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".