SUDEP risk prediction to improve with new tool


By Mardi Chapman

17 May 2021

Researchers have proposed a SUDEP personalised risk prediction tool that may improve communication in the clinical consultation and enhance clinical decision making.

The research, published in Neurology, reanalysed data from a US cohort and three case-controlled studies comprising 1,273 people with epilepsy (287 SUDEP, 986 controls).

Twenty-two clinical predictors including age, disease duration, seizure types, seizure frequency and anti-seizure medications were also factored into the risk prediction model..

The study found individual risk could be predicted more accurately with the new model than either a model based on generalised tonic-clonic seizure (GTCS) frequency alone or one based on the population-level average even when generalised to unseen subjects.

“Its ability to discriminate SUDEP from controls was reasonable (leave-one-subject-out AUROC=0.71 (0.68 to 0.74),” the study said.

“Over and above the power of the model to discriminate SUDEP cases from controls within an observation period, we can also view the model output as an estimate of latent stochastic SUDEP risk.”

They said the model found evidence that lamotrigine, benzodiazepines and carbamazepine are associated with increased SUDEP risk.

“Our model also identified a novel association of focal-onset seizure frequency with SUDEP risk and confirmed previously reported associations with increased GTCS frequency, younger age of epilepsy onset and male sex.”

Medication adherence was associated with reduced risk, whilst alcohol/drug abuse was associated with increased risk.

“This highlights two modifiable behaviours that, if causal, could reduce risk if addressed,” the study said.

A history of epilepsy in first degree relatives also increased SUDEP risk.

“This may be partially explained by mutations that cause epileptic encephalopathies and treatment resistant epilepsies such as SCN1A and SCN8A but other more highly prevalent (e.g., DEPDC5) and possibly non-mendelian mechanisms may also contribute.”

Despite study limitations such as variations in how some predictors were defined, the researchers concluded their tool could help identify high-risk groups.

“We developed and validated an individualised SUDEP prediction model, which relies on information available during a clinical consultation.”

“This will be developed into an online risk prediction calculator for clinical research use. This prediction remains uncertain, but has potential utility in clinical and research settings.”


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