Derivation and validation of a new prediction model for sudden unexpected death in epilepsy based on a longitudinal prospective population-based cohort

Abstract

ObjectiveWe conducted a population-based, prospective cohort study with a large sample size in Ningxia Province of the Northwest, a rural area in China, by developing a model to specifically assess risk factors of sudden unexpected death in epilepsy (SUDEP) in people with convulsive epilepsy by clinical variables. MethodsParticipants with convulsive epilepsy were recruited from January 1, 2008, to April 28, 2022, in rural Northwest China. They received regular assessments and management of epilepsy at the primary healthcare level and were followed up monthly. Information on the cause of death and relevant clinical details was obtained from death certificates or neurologist-conducted verbal autopsies. Survival analysis was employed to identify potential risk factors associated with SUDEP. ResultsFive variables were independently associated with SUDEP: generalized tonic-clonic seizures (GTCS) with ≥1 attack during the preceding month, GTCS with >3 attacks during the preceding year, body mass index (BMI) ≥24, age of onset ≤14 years, and duration >20 years. The area under receiver operator characteristic (ROC) curve (AUC) value (95% CI) of the model was 0.789 (0.735–0.843) in the derivation dataset and 0.830 (0.758–0.902) in the validation dataset. There was agreement between the observed and predicted probabilities of SUDEP. ConclusionsThis study establishes that high GTCS frequency, early age of onset, long duration of epilepsy, and being overweight are associated with an increased risk of SUDEP in individuals with convulsive epilepsy. The study also developed and validated a personalized prediction model to accurately assess the risk of SUDEP.