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Derivation and validation of a new prediction model for sudden unexpected death in epilepsy based on a longitudinal
Qiang Liu1, Bofei Tan2, Jie Zhang2
1Department of Neurology, General Hospital of Ningxia Medical University, Ningxia Key Laboratory of Cerebrocranial Diseases, Incubation Base of National Key Laboratory, Yinchuan 750004, Ningxia Province, China; Graduate College of Ningxia Medical University, Yinchuan 750004, Ningxia Province, China.
Objective:
We 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.
Methods:
Participants 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.
Results:
Five 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.
Conclusions:
This 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.
