Longitudinal Changes in Cardiac Echocardiographic Parameters in Patients on Hemodialysis with and Without Atrial Fibrillation
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is common in hemodialysis (HD) patients but its impact on cardiac structure in this population remains unclear. We investigated the association between incident or prevalent AF and echocardiographic changes in an incident HD cohort. Methods: We conducted a retrospective, cohort study of patients initiating HD between 2017-2022. Eligible patients had ≥2 transthoracic echocardiograms (TTE): one before and at least one after HD start. AF status was classified as: No AF, Prevalent AF (onset before or ≤90 days from HD start), or Incident AF (>90 days post-HD start). Associations between AF status and TTE trajectories over time were assessed with linear mixed models. Inverse probability weights for developing AF were applied. Results: We included 109 patients (44 with AF). Baseline characteristics were balanced after weighting. At HD initiation, left atrial volume index (LAVI) was significantly higher in patients with prevalent AF versus those without AF (p<0.01), with both groups showing similar decline over time. Patients who developed incident AF had similar baseline LAVI to the No AF group (p=0.55) but experienced a significant increase over time (Figure 1). Left ventricular end-diastolic volume index (LVEDVI) was comparable across groups at baseline. LVEDVI declined significantly in the No AF group (p<0.01) and in the Prevalent AF group but remained unchanged in the Incident AF group, indicating a distinct remodeling pattern (Figure 1). Additional TTE parameters are shown in Figure 2. Conclusion: While prevalent AF was associated with higher baseline atrial volume, only incident AF showed progressive atrial enlargement and impaired ventricular unloading, suggesting a maladaptive, dialysis-intolerant phenotype warranting closer surveillance. Funding: Government Support – Non-U.S.Fig. 1Fig. 2
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".