Impact of comorbid sleep disorders in patients with epilepsy on mortality risk
Bibliographic record
Abstract
Background: Pooled mortality is nearly three times higher in people with epilepsy (PWE), with epilepsy-related deaths, including sudden unexpected death in epilepsy (SUDEP), status epilepticus, and drowning, being more prevalent than other causes. Approximately 80% of SUDEP events occur during sleep, and sleep abnormalities are common in PWE, including disruptions in quality and architecture. Primary sleep disorders like sleep-disordered breathing and insomnia disorder are prevalent in both the general population and PWE. Currently, research is extensively exploring SUDEP risk evaluation and biomarkers, with limited studies investigating the connection between sleep and SUDEP/all-cause mortality risk in epilepsy. This study aims to assess the relationship between mortality risk in epilepsy and the diagnosis of primary sleep disorders. Methods: The study included consecutive patients with active epilepsy from a Canadian epilepsy clinic over a 4-year period. The database collected demographic, clinical, neurophysiological, and sleep information, along with accident and hospitalization records. Mortality risk was evaluated using the modified SUDEP-7 scale, and primary sleep disorder diagnoses were made by a qualified sleep physician based on ICSD-3 criteria. Patients were categorized into two groups: those without comorbid sleep disorders (Group 1) and those with sleep disorders (Group 2). Statistical analyses were conducted to compare mortality risk-related variables between the two groups. Results: The study enrolled 1506 PWE, with Group 1 (1130 patients) and Group 2 (376 patients). Both groups showed similar average epilepsy duration, epilepsy type distribution, and the percentage of PWE seizure-free for over a year. However, Group 2 exhibited a significantly higher proportion of PWE with high mortality risk, increased incidence of all-cause mortality, accidents, and poorer employment status. The most common sleep disorder in Group 2 was obstructive sleep apnea (54%), followed by insomnia disorder (29%) and restless legs syndrome (12%). Conclusion: Comorbid primary sleep disorders are associated with significantly higher SUDEP risk and all-cause mortality risk in epilepsy. PWE with these sleep disorders also face an elevated risk of accidents and unemployment. This study offers valuable insights for developing more accurate predictive models and tools for assessing mortality 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".