Association between common cardiovascular risk factors and clinical phenotype in patients with hypertrophic cardiomyopathy from the European Society of Cardiology (ESC) EurObservational Research Programme (EORP) Cardiomyopathy/Myocarditis registry
Bibliographic record
Abstract
AIMS: The interaction between common cardiovascular risk factors (CVRF) and hypertrophic cardiomyopathy (HCM) is poorly studied. We sought to explore the relation between CVRF and the clinical characteristics of patients with HCM enrolled in the EURObservational Research Programme (EORP) Cardiomyopathy registry. METHODS AND RESULTS: 1739 patients with HCM were studied. The relation between hypertension (HT), diabetes (DM), body mass index (BMI), and clinical traits was analysed. Analyses were stratified according to the presence or absence of a pathogenic variant in a sarcomere gene. The prevalence of HT, DM, and obesity (Ob) was 37, 10, and 21%, respectively. HT, DM, and Ob were associated with older age (P<0.001), less family history of HCM (HT and DM P<0.001), higher New York Heart Association (NYHA) class (P<0.001), atrial fibrillation (HT and DM P<0.001; Ob p = 0.03) and LV (left ventricular) diastolic dysfunction (HT and Ob P<0.001; DM P = 0.003). Stroke was more frequent in HT (P<0.001) and mutation-positive patients with DM (P = 0.02). HT and Ob were associated with higher provocable LV outflow tract gradients (HT P<0.001, Ob P = 0.036). LV hypertrophy was more severe in Ob (P = 0.018). HT and Ob were independently associated with NYHA class (OR 1.419, P = 0.017 and OR 1.584, P = 0.004, respectively). Other associations, including a higher proportion of females in HT and of systolic dysfunction in HT and Ob, were observed only in mutation-positive patients. CONCLUSION: Common CVRF are associated with a more severe HCM phenotype, suggesting a proactive management of CVRF should be promoted. An interaction between genotype and CVRF was observed for some traits.
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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 | 0.001 | 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".