Global variations in heart failure etiology, management, and outcomes
Bibliographic record
Abstract
Importance: Most epidemiological studies of heart failure (HF) have been conducted in high-income countries with limited comparable data from middle- or low-income countries. Objective: To examine differences in HF etiology, treatment, and outcomes between groups of countries at different levels of economic development. Design, setting, and participants: Multinational HF registry of 23 341 participants in 40 high-income, upper-middle-income, lower-middle-income, and low-income countries, followed up for a median period of 2.0 years. Main outcomes and measures: HF cause, HF medication use, hospitalization, and death. Results: Mean (SD) age of participants was 63.1 (14.9) years, and 9119 (39.1%) were female. The most common cause of HF was ischemic heart disease (38.1%) followed by hypertension (20.2%). The proportion of participants with HF with reduced ejection fraction taking the combination of a β-blocker, renin-angiotensin system inhibitor, and mineralocorticoid receptor antagonist was highest in upper-middle-income (61.9%) and high-income countries (51.1%), and it was lowest in low-income (45.7%) and lower-middle-income countries (39.5%) (P < .001). The age- and sex- standardized mortality rate per 100 person-years was lowest in high-income countries (7.8 [95% CI, 7.5-8.2]), 9.3 (95% CI, 8.8-9.9) in upper-middle-income countries, 15.7 (95% CI, 15.0-16.4) in lower-middle-income countries, and it was highest in low-income countries (19.1 [95% CI, 17.6-20.7]). Hospitalization rates were more frequent than death rates in high-income countries (ratio = 3.8) and in upper-middle-income countries (ratio = 2.4), similar in lower-middle-income countries (ratio = 1.1), and less frequent in low-income countries (ratio = 0.6). The 30-day case-fatality rate after first hospital admission was lowest in high-income countries (6.7%), followed by upper-middle-income countries (9.7%), then lower-middle-income countries (21.1%), and highest in low-income countries (31.6%). The proportional risk of death within 30 days of a first hospital admission was 3- to 5-fold higher in lower-middle-income countries and low-income countries compared with high-income countries after adjusting for patient characteristics and use of long-term HF therapies. Conclusions and relevance: This study of HF patients from 40 different countries and derived from 4 different economic levels demonstrated differences in HF etiologies, management, and outcomes. These data may be useful in planning approaches to improve HF prevention and treatment globally
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".