Impaired heart rate variability triangular index to identify clinically silent strokes in patients with atrial fibrillation
Bibliographic record
Abstract
Abstract Introduction The identification of clinically silent strokes in patients with atrial fibrillation (AF) is of high clinical relevance as they have been linked to cognitive impairment. Overt strokes have been associated with disturbances of the autonomic nervous system. Purpose We therefore hypothesize that impaired heart rate variability (HRV) can identify AF patients with clinically silent strokes. Methods We enrolled 1358 patients with AF without a history of stroke or transient ischemic attack from the multicenter SWISS-AF cohort study who were in sinus rhythm (SR-group, n=816) or AF (AF-group, n=542) on a 5 minute resting ECG recording. HRV triangular index (HRVI), the standard deviation of normal-to-normal intervals (SDNN) and the mean heart rate (MHR) were calculated. Brain MRI was performed at baseline to assess the presence of large non-cortical or cortical infarcts, which were considered silent strokes without history of stroke or transient ischemic attack. We constructed binary logistic regression models to analyze the association between HRV parameters and silent strokes. Results At baseline, silent strokes were detected in 10.5% in the SR group and 19.9% in the AF group. In the SR-group, HRVI <15 was the only parameter independently associated with the presence of silent strokes (odds ratio (OR) 1.69; 95% confidence interval (CI): 1.04–2.72; p=0.033) after adjustment for various clinical covariates (age, sex, systolic blood pressure, history of hypertension, history of diabetes, history of heart failure, prior myocardial infarction, prior major bleeding, intake of oral anticoagulation, antiarrhythmics or betablockers). Similarly, in the AF-group, HRVI<15 was independently associated with the presence of silent strokes (OR 1.65, 95% CI: 1.05–2.57; p=0.028). SDNN<70ms and MHR<80 were not associated with silent strokes, neither in the SR group, nor in the AF group (Figure). Conclusions Reduced HRVI is independently associated with the presence of clinically silent strokes in an AF population, both when assessed during SR and during AF. Our data suggest that a short-term measurement of HRV in routine ECG recordings might contribute to identifying AF patients with clinically silent strokes. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Swiss National Science Foundation
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".