Use of Guideline-Recommended Heart Failure Drugs in High-, Middle-, and Low-Income Countries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Optimal use of guideline-directed medical therapy (GDMT) can prevent hospitalization and mortality among patients with heart failure (HF). We aimed to assess the prevalence of GDMT use for HF across geographic regions and country-income levels. We systematically reviewed observational studies (published between January 2010 and October 2020) involving patients with HF with reduced ejection fraction. We conducted random-effects meta-analyses to obtain summary estimates. We included 334 studies comprising 1,507,849 patients (31% female). The majority (82%) of studies were from high-income countries, with Europe (45%) and the Americas (33%) being the most represented regions, and Africa (1%) being the least. Overall prevalence of GDMT use was 80% (95% CI 78%–81%) for β-blockers, 82% (80%–83%) for renin–angiotensin-system inhibitors, and 41% (39%–43%) for mineralocorticoid receptor antagonists. We observed an exponential increase in GDMT use over time after adjusting for country-income levels (p < 0.0001), but significant gaps persist in low- and middle-income countries. Multi-level interventions are needed to address health-system, provider, and patient-level barriers to GDMT use.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".