Sudden Unexpected Death in Epilepsy: A Personalized Prediction Tool.

ABSTRACT:

Objective

To develop and validate a tool for individualized prediction of sudden unexpected death in epilepsy (SUDEP) risk, we reanalyzed data from 1 cohort and 3 case-control studies undertaken from 1980 through 2005.

Methods

We entered 1,273 epilepsy cases (287 SUDEP, 986 controls) and 22 clinical predictor variables into a Bayesian logistic regression model.

Results

Cross-validated individualized model predictions were superior to baseline models developed from only average population risk or from generalized tonic-clonic seizure frequency (pairwise difference in leave-one-subject-out expected log posterior density = 35.9, SEM ± 12.5, and 22.9, SEM ± 11.0, respectively). The mean cross-validated (95% bootstrap confidence interval) area under the receiver operating

SUBMITTER: Jha A 

PROVIDER: S-EPMC8205449 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature