Cardiac autonomic function and cognitive performance in patients with atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is associated with loss of cognition and dementia. Cardiac autonomic dysfunction has been linked to cognitive decline. We aimed to investigate if reduced cardiac autonomic function (CAF) is associated with cognitive impairment in AF patients. METHODS: Patients with paroxysmal, persistent and permanent AF were enrolled from a multicenter cohort study if they had AF ("AF group") or sinus rhythm ("SR group") on a baseline 5 min ECG recording. Parameters quantifying CAF (heart rate variability triangular index (HRVI), mean heart rate (MHR), RMSSD, SDNN, total power and power in the VLF, LF, HF ranges) were calculated. We used the Montreal Cognitive Assessment (MoCA) to assess global cognitive function. RESULTS: 1685 AF patients with a mean age of 73 ± 8 years, 29% females, were included. MoCA score was 24.5 ± 3.2 in the AF group (N = 710 patients) and 25.4 ± 3.2 in the SR group (N = 975 patients). After adjusting for multiple confounders, lower HRVI was associated with lower MoCA scores, both in the SR group [β = 0.049; 95% confidence interval (CI) 0.016-0.081; p = 0.003] and in the AF group (β = 0.068; 95% CI 0.020-0.116; p = 0.006). In the AF group, higher MHR was associated with a poorer performance in the MoCA (β = - 0.008; 95% CI - 0.014 to - 0.002; p = 0.014). We found no convincing evidence of association for other CAF parameters with cognition. CONCLUSION: Our data suggest that impaired CAF is associated with worse cognitive performance in patients with AF. Among standard HRV parameters, HRVI might be the most promising ECG index. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02105844.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".