Abstract 4360749: Prevalence and Risk Markers for Heart Failure in Hypertrophic Cardiomyopathy: A Multicenter Cross-Sectional Study With Central Assessment of Biomarkers and Echocardiograms
Bibliographic record
Abstract
Background: Heart failure (HF) in hypertrophic cardiomyopathy (HCM) is poorly characterized, especially in patients with preserved left ventricular ejection fraction (LVEF). Clarifying HF characteristics in HCM is essential to guide patient selection for emerging therapies. Research Questions: What is the prevalence of HF in HCM patients? What are the risk markers of HF? Methods: The study is a cross-sectional analysis of HiRO-HCM, an ongoing multicenter registry. This study included HCM patients aged ≥ 16 years with available data to allow for central ascertainment of HF, defined as i) history of hospitalization for HF requiring diuretics, heart transplantation or mechanical circulatory support; and/or ii) New York Heart Association (NYHA) class ≥ II with increased filling pressure (E/e′ ratio ≥ 14 and/or a NT-proBNP ≥ 400 ng/L [≥800 ng/L in atrial fibrillation]). The association of risk markers with HF was assessed with logistic regression. Analyses were performed in the overall cohort, and in 3 subgroups: obstructive HCM (oHCM), non-obstructive HCM (nHCM) with LVEF≥50%, and nHCM with LVEF<50%. The presence of obstruction and LVEF were assessed from echocardiography performed within 4 years (median 0.6 year) of HF ascertainment, using corelab interpretation when images are available (59% of the study cohort). NT-proBNP was systematically measured in patients with available plasma (81% of the cohort). Results: We included 1,632 patients (33% females; age 58±14 years). The maximal left ventricular wall thickness was 18±4 mm, and 613 (45%) patients carried a causal genetic variant. The median NT-proBNP was 490 [189-1140] ng/L and mean E/e’ was 11±4. In the overall cohort, 510 (31%) patients met the definition of HF, including 93 hospitalized for HF and 26 transplanted. The prevalence of HF was 41% in the oHCM subgroup, 26% in nHCM with LVEF≥50%, and 65% in nHCM with LVEF<50% (P<0.001). Several variables were associated with increased odds for HF (Figure). In multivariable analysis, female sex emerged as a strong risk factor for HF in all 3 subgroups, with a 3-fold increased risk for HF (P<0.001). Conclusion: HF affects one-third of patients with HCM. Female sex is a robust and consistent predictor of HF across all phenotypes. The findings underscore the importance of sex-specific evaluation and earlier recognition of HF symptoms in HCM management. These findings may inform risk stratification and selection for novel therapies targeting HCM-related HF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".