A nationwide study of the economic burden of obstructive hypertrophic cardiomyopathy in France
Bibliographic record
Abstract
AIM: This study describes economic burden of obstructive hypertrophic cardiomyopathy (HCM) in France, with consideration of disease severity as measured by New York Heart Association (NYHA) class. METHODS AND RESULTS: This observational, retrospective study used data from the French National Health Data System. Adults (≥18) with at least one disease related hospitalization during 2012-18 were included. Patients with <1-year follow-up or phenocopy disorders were excluded. Patients were stratified by disease severity class based on disease specific treatments and symptoms. Healthcare resources use, and costs were estimated per patient-year (PY). Annual cost before and after septal reduction therapies (SRT) was estimated. Overall, 6823 patients were identified (baseline NYHA class I-IV: 4%, 32%, 60%, and 4%, respectively). Mean (standard deviation) follow-up was 4.4 (2.5) years, comprising 30 228 PYs. Total burden was €388 million (€12 824 per PY), and higher NYHA class was associated with higher cost per patient year: €8881 and €22 818 for classes I and IV, respectively. Hospitalizations accounted for most costs (54%); 71% were cardiovascular-related hospitalizations, 46% disease-related. Mean cost per PY was lower 1 year before vs. after SRT, including the intervention (€13 726 vs. €18 565). Mean sick-leave-related costs per PY were €310, €673, €757, and €774 for classes I-IV, respectively. CONCLUSION: Obstructive HCM has a high economic burden driven by cardiovascular hospitalizations. Higher disease severity yielded higher costs associated with medical care and sick leave than lower classes. Results support need for new therapies to improve both symptoms and disease severity.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".