Heart Failure and Atrial Flutter: A Systematic Review of Current Knowledge and Practices
Bibliographic record
Abstract
While the interplay between heart failure (HF) and atrial fibrillation (AF) has been extensively studied, little is known regarding HF and atrial flutter (AFL), which may be managed differently. We reviewed the incidence, prevalence, and predictors of HF in AFL and vice versa, and the outcomes of treatment of AFL in HF. A systematic literature review of PubMed/Medline and EMBASE yielded 65 studies for inclusion and qualitative synthesis. No study described the incidence or prevalence of AFL in unselected patients with HF. Most cohorts enrolled patients with AF/AFL as interchangeable diagnoses, or highly selected patients with tachycardia-induced cardiomyopathy. The prevalence of HF in AFL ranged from 6% to 56%. However, the phenotype of HF was never defined by left ventricular ejection fraction (LVEF). No studies reported the predictors, phenotype, and prognostic implications of AFL in HF. There was significant variation in treatments studied, including the proportion that underwent ablation. When systolic dysfunction was tachycardia-mediated, catheter ablation demonstrated LVEF normalization in up to 88%, as well as reduced cardiovascular mortality. In summary, AFL and HF often coexist but are understudied, with no randomized trial data to inform care. Further research is warranted to define the epidemiology and establish optimal management.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".