A Twin Study of Genetic Influences on Epilepsy Outcome

Johnson, Michael R.; Milne, Roger L.; Torn-Broers, Yvonne; Hopper, John L.; Scheffer, Ingrid E.; Berkovic, Samuel F.

doi:10.1375/136905203321536263

Cited by 18 publications

(10 citation statements)

References 20 publications

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“…Pragmatic trials will usually recruit a heterogeneous group of patients, and while this approach has been criticised,26 27 this paper illustrates the strength of this approach as it can allow an investigation of sources of heterogeneity of outcome. In this analysis of arm B, we have been able to categorise patients according to their seizure types at presentation and assess the influence of this and other factors upon treatment outcome.…”

Section: Discussionmentioning

confidence: 99%

“…Although there is increasing interest in stratified medicine and pharmacogenetics, it remains unknown as to whether genetic factors might explain the unexplained variability in our predictive models. However, given the assumed genetic aetiology of the idiopathic generalised epilepsies, the prognostic importance of having a first degree relative with epilepsy is of great interest and raises the question as to whether some epilepsies are inherently more drug refractory,26 rather than refractoriness being expressed via another mechanism 27. Better understanding of the genetic aetiology of these epilepsies may lead to novel targets and new treatments, although the prospect of preventing the epilepsy developing in the first place remains a distant hope, likely requiring some form of gene therapy.…”

Section: Discussionmentioning

confidence: 99%

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Time to 12-month remission and treatment failure for generalised and unclassified epilepsy

Bonnett

Smith

et al. 2013

Journal of Neurology, Neurosurgery & Psychiatry

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ObjectivesTo develop prognostic models for time to 12-month remission and time to treatment failure after initiating antiepileptic drug monotherapy for generalised and unclassified epilepsy.MethodsWe analysed data from the Standard and New Antiepileptic Drug (arm B) study, a randomised trial that compared initiating treatment with lamotrigine, topiramate and valproate in patients diagnosed with generalised or unclassified epilepsy. Multivariable regression modelling was used to investigate how clinical factors affect the probability of achieving 12-month remission and treatment failure.ResultsSignificant factors in the multivariable model for time to 12-month remission were having a relative with epilepsy, neurological insult, total number of tonic-clonic seizures before randomisation, seizure type and treatment. Significant factors in the multivariable model for time to treatment failure were treatment history (antiepileptic drug treatment prior to randomisation), EEG result, seizure type and treatment.ConclusionsThe models described within this paper can be used to identify patients most likely to achieve 12-month remission and most likely to have treatment failure, aiding individual patient risk stratification and the design and analysis of future epilepsy trials.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Time to 12-month remission and treatment failure for generalised and unclassified epilepsy

Bonnett

Smith

et al. 2013

Journal of Neurology, Neurosurgery & Psychiatry

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“…Pragmatic clinical trials usually recruit a heterogeneous group of participants. Although some have criticised this approach [ 33 , 34 ] the strength of this method has been highlighted here as it allows an investigation of sources of heterogeneity of outcome. Other limitations of SANAD have been discussed elsewhere.…”

Section: Discussionmentioning

confidence: 99%

Breakthrough seizures—Further analysis of the Standard versus New Antiepileptic Drugs (SANAD) study

et al. 2017

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ObjectivesTo develop prognostic models for risk of a breakthrough seizure, risk of seizure recurrence after a breakthrough seizure, and likelihood of achieving 12-month remission following a breakthrough seizure. A breakthrough seizure is one that occurs following at least 12 months remission whilst on treatment.MethodsWe analysed data from the SANAD study. This long-term randomised trial compared treatments for participants with newly diagnosed epilepsy. Multivariable Cox models investigated how clinical factors affect the probability of each outcome. Best fitting multivariable models were produced with variable reduction by Akaike’s Information Criterion. Risks associated with combinations of risk factors were calculated from each multivariable model.ResultsSignificant factors in the multivariable model for risk of a breakthrough seizure following 12-month remission were number of tonic-clonic seizures by achievement of 12-month remission, time taken to achieve 12-month remission, and neurological insult. Significant factors in the model for risk of seizure recurrence following a breakthrough seizure were total number of drugs attempted to achieve 12-month remission, time to achieve 12-month remission prior to breakthrough seizure, and breakthrough seizure treatment decision. Significant factors in the model for likelihood of achieving 12-month remission after a breakthrough seizure were gender, age at breakthrough seizure, time to achieve 12-month remission prior to breakthrough, and breakthrough seizure treatment decision.ConclusionsThis is the first analysis to consider risk of a breakthrough seizure and subsequent outcomes. The described models can be used to identify people most likely to have a breakthrough seizure, a seizure recurrence following a breakthrough seizure, and to achieve 12-month remission following a breakthrough seizure. The results suggest that focussing on achieving 12-month remission swiftly represents the best therapeutic aim to reduce the risk of a breakthrough seizure and subsequent negative outcomes. This will aid individual patient risk stratification and the design of future epilepsy trials.

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“…Our overarching hypothesis is that genetic variation influences the likelihood of response to specific AEDs. While the study design and discovery paradigms are agnostic about whether the variants that are most relevant to prognosis also influence disease predisposition, we postulate a genetically based interrelatedness of disease etiology and outcome (i.e., specific susceptibility variants increase the risk of subtypes of epilepsy that are inherently less responsive to currently available AEDs) (Johnson et al., 2003; Rogawski & Johnson, 2008).…”

Section: Epi4k Project 3: Prognosismentioning

confidence: 99%

Epi4K: Gene discovery in 4,000 genomes

Sherr¹,

Berkovic²,

Cossette³

et al. 2012

Epilepsia

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A major challenge in epilepsy research is to unravel the complex genetic mechanisms underlying both common and rare forms of epilepsy, as well as the genetic determinants of response to treatment. To accelerate progress in this area, the National Institute of Neurological Disorders and Stroke (NINDS) recently offered funding for the creation of a “Center without Walls” to focus on the genetics of human epilepsy. This article describes Epi4K, the collaborative study supported through this grant mechanism and having the aim of analyzing the genomes of a minimum 4,000 subjects with highly selected and well-characterized epilepsy.

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A Twin Study of Genetic Influences on Epilepsy Outcome

Cited by 18 publications

References 20 publications

Time to 12-month remission and treatment failure for generalised and unclassified epilepsy

Time to 12-month remission and treatment failure for generalised and unclassified epilepsy

Breakthrough seizures—Further analysis of the Standard versus New Antiepileptic Drugs (SANAD) study

Epi4K: Gene discovery in 4,000 genomes

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