Heart failure in younger patients: the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC)
Bibliographic record
Abstract
AIM: Our understanding of heart failure in younger patients is limited. The Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) database, which consisted of 24 prospective observational studies and 7 randomized trials, was used to investigate the clinical characteristics, treatment, and outcomes of younger patients. METHODS AND RESULTS: Patients were stratified into six age categories: <40 (n = 876), 40-49 (n = 2638), 50-59 (n = 6894), 60-69 (n = 12 071), 70-79 (n = 13 368), and ≥80 years (n = 6079). Of 41 926 patients, 2.1, 8.4, and 24.8% were younger than 40, 50, and 60 years of age, respectively. Comparing young (<40 years) against elderly (≥80 years), younger patients were more likely to be male (71 vs. 48%) and have idiopathic cardiomyopathy (63 vs. 7%). Younger patients reported better New York Heart Association functional class despite more severe left ventricular dysfunction (median ejection fraction: 31 vs. 42%, all P < 0.0001). Comorbidities such as hypertension, myocardial infarction, and atrial fibrillation were much less common in the young. Younger patients received more disease-modifying pharmacological therapy than their older counterparts. Across the younger age groups (<40, 40-49, and 50-59 years), mortality rates were low: 1 year 6.7, 6.6, and 7.5%, respectively; 2 year 11.7, 11.5, 13.0%; and 3 years 16.5, 16.2, 18.2%. Furthermore, 1-, 2-, and 3-year mortality rates increased sharply beyond 60 years and were greatest in the elderly (≥80 years): 28.2, 44.5, and 57.2%, respectively. CONCLUSION: Younger patients with heart failure have different clinical characteristics including different aetiologies, more severe left ventricular dysfunction, and less severe symptoms. Three-year mortality rates are lower for all age groups under 60 years compared with older patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".