Adherence to Treatment Guidelines in Ambulatory Heart Failure Patients with Reduced Ejection Fraction in a Latin-American Country: Observational Study of the Colombian Heart Failure Registry (RECOLFACA)
Bibliographic record
Abstract
INTRODUCTION: Although several guidelines recommend that patients with heart failure with reduced ejection fraction (HFrEF) be treated with angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers (ACEIs/ARBs) or angiotensin receptor-neprilysin inhibitors (ARNIs), beta-blockers, mineralocorticoid receptor antagonists (MRAs), and sodium-glucose cotransporter-2 inhibitor (SGLT2i), there are still several gaps in their prescription and dosage in Colombia. This study aimed to describe the use patterns of HFrEF treatments in the Colombian Heart Failure Registry (RECOLFACA). METHODS: Patients with HFrEF enrolled in RECOLFACA during 2017-2019 were included. Heart failure (HF) medication prescription and daily dose were assessed using absolute numbers and proportions. Therapeutic schemes of patients treated by internal medicine specialists were compared with those treated by cardiologists. RESULTS: Out of 2,528 patients in the registry, 1,384 (54.7%) had HFrEF. Among those individuals, 88.9% were prescribed beta-blockers, 72.3% with ACEI/ARBs, 67.9% with MRAs, and 13.1% with ARNIs. Moreover, less than a third of the total patients reached the target doses recommended by the European HF guidelines. No significant differences in the therapeutic schemes or target doses were observed between patients treated by internal medicine specialists or cardiologists. CONCLUSION: Prescription rates and target dose achievement are suboptimal in Colombia. Nevertheless, RECOLFACA had one of the highest prescription rates of beta-blockers and MRAs compared to some of the most recent HF registries. However, ARNIs remain underprescribed. Continuous registry updates can improve the identification of patients suitable for ARNI and SGLT2i therapy to promote their use in clinical practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".