Validation of a multigenic model to predict seizure control in newly treated epilepsy

Validation of a multigenic model to predict seizure control in newly treated epilepsy

Highlights • Attempted validation of genomic classifier of treatment outcome in new-onset epilepsy. • Genetic and clinical data from UK & Australian cohorts analysed by machine-learning. • Australian classifier failed to directly predict treatment outcome in UK cohorts. • Cross-validation sugges...

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Veröffentlicht in:Epilepsy research 2014-12, Vol.108 (10), p.1797-1805
Hauptverfasser: Shazadi, Kanvel, Petrovski, Slavé, Roten, Annie, Miller, Hugh, Huggins, Richard M, Brodie, Martin J, Pirmohamed, Munir, Johnson, Michael R, Marson, Anthony G, O’Brien, Terence J, Sills, Graeme J
Format: Artikel
Sprache:eng
Schlagworte:
Adult
Algorithms
Anticonvulsants - therapeutic use
Antiepileptic drug
Artificial Intelligence
Australia
Biological and medical sciences
Biomarkers, Pharmacological
Carbamazepine - therapeutic use
Cohort Studies
Epilepsy
Epilepsy - drug therapy
Epilepsy - genetics
Female
Genetic association
Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy
Humans
k-Nearest neighbour
Lamotrigine
Logistic Models
Machine-learning
Male
Medical sciences
Models, Genetic
Nervous system (semeiology, syndromes)
Neurology
Polymorphism, Single Nucleotide
Seizures - drug therapy
Seizures - genetics
Treatment Outcome
Triazines - therapeutic use
United Kingdom
Valproic Acid - therapeutic use
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Beschreibung
Zusammenfassung:Highlights • Attempted validation of genomic classifier of treatment outcome in new-onset epilepsy. • Genetic and clinical data from UK & Australian cohorts analysed by machine-learning. • Australian classifier failed to directly predict treatment outcome in UK cohorts. • Cross-validation suggested prediction of response to carbamazepine and valproate. • Genetic variants may have predictive influence on outcome with specific drugs.
ISSN:0920-1211
1872-6844
DOI:10.1016/j.eplepsyres.2014.08.022