Possible Risk Factors Contributing to Atrial Fibrillation Occurrence in Heart Failure With Mildly Reduced Ejection Fraction
Bibliographic record
Abstract
Background: Heart failure (HF) is often accompanied by atrial fibrillation (AF), which significantly worsens the outcome of both diseases. Half of individuals with HF has AF, and HF occurs in more than one-third of individuals with AF. Thus, HF and AF are commonly encountered together and are closely interrelated with similar risk factors. The aim of this study was to investigate the impact of potential risk factors on the occurrence of paroxysmal/persistent AF in patients with heart failure with moderately reduced ejection fraction (HFmrEF). Methods: The study included 193 patients with HFmrEF and nonvalvular paroxysmal/persistent AF after successful cardioversion. As a control group the similar 76 patients without AF were examined. All patients underwent the examination, including electrocardiography (ECG), echocardiography, ambulatory blood pressure monitoring and Holter ECG monitoring. Levels of inflammatory markers, such as high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and the fibrotic marker transforming growth factor-β1 (TGF-β1) were measured using the enzyme-linked immunosorbent assay (ELISA) method. The obtained results were modeled using binary logistic regression using the odds ratio (OR). Results: It was shown that frequent episodes of hypertensive crisis (HC) and increased body mass index (BMI) were possible risk factors for paroxysmal/persistent AF. An increased OR of diastolic and systolic parameters of the left ventricle was associated with significant atrial and ventricular remodeling. Statistically, higher OR of inflammatory markers levels, such as hs-CRP, IL-6 and TNF-α were associated with an increased risk of paroxysmal/persistent AF occurrence in HFmrEF patients compared to similar patients without AF. An increase of the fibrosis marker TGF-β1 OR was statistically significant in patients with persistent AF. Conclusions: It could be considered that frequency of HC, BMI, atrial and ventricular remodeling, as well as an increase of inflammation markers were possible risk factors for the occurrence of paroxysmal/persistent AF in HFmrEF patients. Moreover, fibrosis factor level significantly increased the likelihood of persistent AF in these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".