ICNA President-Elect Elections 2024 are currently underway. All eligible voters (ICNA Full Members) have been emailed their unique voting credentials. All voting is done via the secure platform at https://icnapedia.org/pe2024. The voting site will remain open until 2400hrs GMT on 1 May 2024.
Didem Ardicli1, MD and Mirac Yildirim2, MD*
1Department of Pediatric Neurology, Ankara City Hospital, Ankara, Turkey
2Department of Pediatric Neurology, Ankara University Faculty of Medicine, Ankara, Turkey
*Correspondence: Dr. Mirac Yildirim, E-mail: mrcyildirim@ankara.edu.tr
Investigators from multiple institutions in the U.S. and Europe collaborated to develop an individualized prediction tool for sudden unexpected death in epilepsy (SUDEP) risk, which relies on information collected during a clinical evaluation. The authors reanalyzed data from 1 cohort and 3 case–control studies, including 1,273 epilepsy cases (287 SUDEP, 986 controls) and investigated the influence of 22 clinical variables on SUDEP risk. Based on their findings, they generated a prediction tool, which they validated using statistical Bayesian logistic regression modelling. They then applied the tool to 10 patients with epilepsy to demonstrate clinical utility. Clinical variables significantly associated with an increased risk of SUDEP included a higher frequency of uncontrolled seizures, including both generalized tonic clonic seizures (GTCS) and focal-onset seizures, epilepsy of unknown etiology, family history of epilepsy, alcohol and drug use, and younger age at epilepsy onset. Male gender was associated with slightly higher risk of SUDEP. Variables associated with a reduced risk of SUDEP included good anti-seizure medication adherence and prior epilepsy surgery. Compared to other antiseizure medications, lamotrigine, benzodiazepines, and carbamazepine were associated with increased SUDEP risk. Learning disability did not independently increase the risk of SUDEP.
The prediction tool generated based on these variables yielded a mean cross-validated (95% bootstrap confidence interval) area under the receiver operating curve of 0.71 (0.68–0.74). This was significantly higher than models based on baseline average seizure frequency (0.38, 0.33–0.42) and GTC seizure frequency (0.63, 0.59–0.67). Performance of the model was weaker when applied to non-represented populations COMMENTARY. Sudden unexpected death in epilepsy is the most common cause of epilepsy-related deaths with a reported incidence of 1.2 per 1,000 epilepsy patients per year in adults. Diagnosis is based on exclusion of other potential causes of death [1]. The exact pathophysiology of SUDEP is currently unknown and predicting factors are not fully characterized [2].
This study confirmed increased risk of SUDEP in association with the presence and frequency of GTCSs, early age of seizure onset, longer duration of epilepsy, polytherapy, and male sex. In contradiction to previous reports, increased SUDEP risk was also associated with frequent focal-onset seizures. This novel finding requires further investigation. Also in contrast to previous studies, no association was found between presence of a learning disability and SUDEP risk.[3]. The authors postulated that this may have been due to variation in the data or the strategies used for analysis.
While the prediction tool is not available for general use, this study is widely relevant to practitioners of pediatric neurology, potentially allowing them to identify new high-risk patients that may benefit from increased observation by caregivers or seizure detection devices. Future prospective and larger longitudinal studies are required to improve the model and to establish its accuracy, especially in non-represented populations.
Disclosures
The authors have declared that no competing interests exist.