Epidemiology and Short-Term Outcomes of Heart Failure With Preserved and Mildly Reduced Ejection Fraction in Colombia: Insights of the Colombian Heart Failure Registry (RECOLFACA)
Bibliographic record
Abstract
Background: Heart failure with preserved or mildly reduced ejection fraction (HFpEF/HFmrEF) has differences in therapy and development when compared with HF with reduced EF (HFrEF). We aimed to describe the clinical characteristics and all-cause mortality of patients with HFpEF/HFmrEF compared to those with HFrEF from the Colombian Heart Failure Registry (RECOLFACA). Methods: RECOLFACA included Colombian adult patients with ambulatory HF recruited from 2017 to 2019. All-cause mortality was our main outcome. We used the Kaplan-Meier method, life table, and Cox proportional hazard models to evaluate the role of the comorbidities on mortality, with a significant P-value of < 0.05. All statistical tests were two-tailed. Results: We included 2,514 patients, and 1,139 (45.3%) had a diagnosis of HFpEF or HFmrEF. HFpEF/HFmrEF diagnosis was not significantly related to either higher or lower risk of mortality compared to an HFrEF diagnosis; however, the individual risk factors for this outcome varied between the two groups. Health-related quality of life (HRQL) was a common risk factor for both groups. Conclusion: Although the EF classification was not a significant risk factor for mortality, patients with HFpEF/HFmrEF exhibited a unique profile of risk factors for mortality, the HRQL, highlighting the relevance of an adequate classification of the HF patients.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".