Heart rate variability in epilepsy: A potential biomarker of <scp>sudden unexpected death in epilepsy</scp> risk
Bibliographic record
Abstract
OBJECTIVE: Sudden unexpected death in epilepsy (SUDEP) is a tragic and devastating event for which the underlying pathophysiology remains poorly understood; this study investigated whether abnormalities in heart rate variability (HRV) are linked to SUDEP in patients with epilepsy due to mutations in sodium channel (SCN) genes. METHODS: We retrospectively evaluated HRV in epilepsy patients using electroencephalographic studies to study the potential contribution of autonomic dysregulation to SUDEP risk. We extracted HRV data, in wakefulness and sleep, from 80 patients with drug-resistant epilepsy, including 40 patients with mutations in SCN genes and 40 control patients with non-SCN drug-resistant epilepsy. From the SCN group, 10 patients had died of SUDEP. We compared HRV between SUDEP and non-SUDEP groups, specifically studying awake HRV and sleep:awake HRV ratios. RESULTS: The SUDEP patients had the most severe autonomic dysregulation, showing lower awake HRV and either extremely high or extremely low ratios of sleep-to-awake HRV in a subgroup analysis. A secondary analysis comparing the SCN and non-SCN groups indicated that autonomic dysfunction was slightly worse in the SCN epilepsy group. SIGNIFICANCE: These findings suggest that autonomic dysfunction is associated with SUDEP risk in patients with epilepsy due to sodium channel mutations. The relationship of HRV to SUDEP merits further study; HRV may eventually have potential as a biomarker of SUDEP risk, which would allow for more informed counseling of patients and families, and also serve as a useful outcome measure for research aimed at developing therapies and interventions to reduce SUDEP risk.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".