#2506 Impact of atrial fibrillation on cardiac remodeling in patients on dialysis: insights from a longitudinal study
Bibliographic record
Abstract
Abstract Background and Aims Atrial Fibrillation (AF) is the most common arrhythmia among patients on hemodialysis (HD), with a prevalence ranging between 15 and 40%. In the general population, AF is associated with cardiac remodeling and structural changes. In patients on HD, AF episodes mostly occur on dialysis days and may specifically be triggered by the HD procedure, when fluid and electrolytes shifts are taking place. Thus, whether AF in patients receiving HD is related to the development of structural changes in the heart remains unclear. Our aim was to study the association between AF and cardiac remodeling in incident HD patients, using echocardiographic parameters. Method A retrospective, longitudinal, cohort study was conducted. Patients started on maintenance HD at McGill University Health Center in Montreal, Canada between 1/1/2017 and 31/12/2022 were included. They were divided into AF and non-AF groups according to the presence or not of at least one AF episode on file. In the AF group, patients were considered as prevalent if the first AF episode was documented before dialysis initiation, otherwise AF was considered incident. The cohort was limited to patients who had ≥2 transthoracic echocardiograms (TTE), of which at least one before and one after dialysis initiation. For incident AF patients, at least one TTE after the first AF documentation was required. Patients were followed until the date of death, date of kidney transplantation, or September 1, 2024. Two continuous variables were created, AF burden and dialysis vintage. AF burden represents time (in years) since AF onset. For patients with prevalent AF, the date of dialysis initiation was considered as the date of onset. Dialysis vintage was defined as the time between dialysis initiation and AF onset (0 for prevalent AF patients). A complete case analysis was conducted. Linear mixed effects models were used to explore the association of AF burden and dialysis vintage with echocardiographic outcomes of interest, adjusting for use of renin angiotensin inhibitors. Inverse probability treatment weights (derived from propensity scores based on baseline covariates) were applied to reduce confounding. Results A total of 110 patients (49 with AF) were included in the analysis. Baseline characteristics of the patients with and without AF before and after inverse probability weighting are shown in Table 1. The weighted cohort was well balanced for all baseline characteristics. We found a significant association between AF burden and left ventricular end-diastolic volume index (LVEDVI), with a one-year increase in AF burden being associated with a reduction of 2.70 mL/m2 in LVEDVI (β = −2.70, 95% CI: −4.51 to −0.89, P < 0.01). In addition, dialysis vintage was significantly associated with a drop in LVEDVI (β = −2.08, 95% CI: −3.97 to −0.18, P = 0.03). In contrast, no significant association was found between AF burden or dialysis vintage and left atrial volume index (LAVI; β = 0.79, 95% CI: −1.36 to 2.95, P = 0.47). Furthermore, AF burden was not associated with changes in left ventricular ejection fraction (LVEF; β = −0.04 95% CI: −1.43 to 1.35, P = 0.96), left ventricular mass index (LVMI; β = −0.12, 95% CI: −4.64 to 4.40, P = 0.96), or pulmonary artery systolic pressure (PASP; β = 0.36, 95% CI: −1.57 to 2.28, P = 0.72). A trend towards lower LAVI and PASP was seen with longer dialysis vintage but this trend was not observed with increasing AF burden (Table 2). Conclusion In this study, both AF burden and dialysis vintage were significantly associated with a reduction in LVEDVI, suggesting potential ventricular remodeling or impaired filling which was more pronounced in patients on HD with AF. A trend towards lower LAVI and PASP was seen with longer dialysis vintage, possibly due to more effective decongestion, but this trend was no longer observed with increased AF burden, suggesting impaired left ventricular filling in patients with AF. No significant associations were observed between AF burden or dialysis vintage and LVMI or LVEF. These findings highlight the impact of AF on left ventricular remodeling in patients on maintenance HD and raise the question of a rhythm control strategy to prevent those changes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".