TY - JOUR
T1 - Sudden Unexpected Death in Epilepsy
T2 - A Personalized Prediction Tool
AU - Jha, Ashwani
AU - Oh, Cheongeun
AU - Hesdorffer, Dale
AU - Diehl, Beate
AU - Devore, Sasha
AU - Brodie, Martin J.
AU - Tomson, Torbjörn
AU - Sander, Josemir W.
AU - Walczak, Thaddeus S.
AU - Devinsky, Orrin
N1 - Publisher Copyright:
Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - ObjectiveTo 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.MethodsWe entered 1,273 epilepsy cases (287 SUDEP, 986 controls) and 22 clinical predictor variables into a Bayesian logistic regression model.ResultsCross-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 curve was 0.71 (0.68-0.74) for our model vs 0.38 (0.33-0.42) and 0.63 (0.59-0.67) for the baseline average and generalized tonic-clonic seizure frequency models, respectively. Model performance was weaker when applied to nonrepresented populations. Prognostic factors included generalized tonic-clonic and focal-onset seizure frequency, alcohol excess, younger age at epilepsy onset, and family history of epilepsy. Antiseizure medication adherence was associated with lower risk.ConclusionsEven when generalized to unseen data, model predictions are more accurate than population-based estimates of SUDEP. Our tool can enable risk-based stratification for biomarker discovery and interventional trials. With further validation in unrepresented populations, it may be suitable for routine individualized clinical decision-making. Clinicians should consider assessment of multiple risk factors, and not focus only on the frequency of convulsions.
AB - ObjectiveTo 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.MethodsWe entered 1,273 epilepsy cases (287 SUDEP, 986 controls) and 22 clinical predictor variables into a Bayesian logistic regression model.ResultsCross-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 curve was 0.71 (0.68-0.74) for our model vs 0.38 (0.33-0.42) and 0.63 (0.59-0.67) for the baseline average and generalized tonic-clonic seizure frequency models, respectively. Model performance was weaker when applied to nonrepresented populations. Prognostic factors included generalized tonic-clonic and focal-onset seizure frequency, alcohol excess, younger age at epilepsy onset, and family history of epilepsy. Antiseizure medication adherence was associated with lower risk.ConclusionsEven when generalized to unseen data, model predictions are more accurate than population-based estimates of SUDEP. Our tool can enable risk-based stratification for biomarker discovery and interventional trials. With further validation in unrepresented populations, it may be suitable for routine individualized clinical decision-making. Clinicians should consider assessment of multiple risk factors, and not focus only on the frequency of convulsions.
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U2 - 10.1212/WNL.0000000000011849
DO - 10.1212/WNL.0000000000011849
M3 - Article
C2 - 33910939
AN - SCOPUS:85107089091
SN - 0028-3878
VL - 96
SP - E2627-E2638
JO - Neurology
JF - Neurology
IS - 21
ER -