Characteristics and Outcomes of Atrial Fibrillation in Chronic Heart Failure Patients: A Comprehensive Analysis of the Colombian Heart Failure Registry
Bibliographic record
Abstract
Background: Heart failure (HF) and atrial fibrillation (AF) represent conditions that commonly coexist. The impact of AF in HF has yet to be well studied in Latin America. This study aimed to characterize the sociodemographic and clinical features, along with patients' outcomes with AF and HF from the Colombian Heart Failure Registry (RECOLFACA). Methods: Patients with ambulatory HF and AF were included in RECOLFACA, mainly with persistent or permanent AF. A 6-month follow-up was performed. Primary outcome was all-cause mortality. To assess the impact of AF on mortality, we used a logistic regression model. A P value of < 0.05 was considered significant. All statistical tests were two-tailed. Results: Of 2,528 patients with HF in the registry, 2,514 records included information regarding AF diagnosis. Five hundred sixty (22.3%) were in AF (mean age 73 ± 11, 56% men), while 1,954 had no AF (mean age 66 ± 14 years, 58% men). Patients with AF were significantly older and had a different profile of comorbidities and implanted devices compared to non-AF patients. Moreover, AF diagnosis was associated with lower quality of life score (EuroQol-5D), mainly in mobility, personal care, and daily activity. AF was prevalent in patients with preserved ejection fraction (EF), while no significant differences in N-terminal prohormone of brain natriuretic peptide (NT-proBNP) levels were observed. Although higher mortality was observed in the AF group compared to individuals without AF (8.9% vs. 6.1%, respectively; P = 0.016), this association lost statistical significance after adjusting by age in a multivariate regression model (odds ratio (OR): 1.35; 95% confidence interval (CI): 0.95 - 1.92). Conclusions: AF is more prevalent in HF patients with higher EF, lower quality of life and different clinical profiles. Similar HF severity and non-independent association with mortality were observed in our cohort. These results emphasize the need for an improved understanding of the AF and HF coexistence phenomenon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".