Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy: A systematic review and individual participant data meta-analysis
[en] Summary Background A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME. Methods We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed – last updated on March 11, 2021 – including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/). Findings 368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68–0·73). Interpretation We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools. Funding MING fonds.
Disciplines :
Neurology
Author, co-author :
Stevelink, Remi
Al-Toma, Dania
Jansen, Floor E.
Lamberink, Herm J.
Asadi-Pooya, Ali A.
Farazdaghi, Mohsen
Cação, Gonçalo
Jayalakshmi, Sita
Patil, Anuja
Özkara, Çiğdem
Aydın, Şenay
Gesche, Joanna
Beier, Christoph P.
Stephen, Linda J.
Brodie, Martin J.
Unnithan, Gopeekrishnan
Radhakrishnan, Ashalatha
Höfler, Julia
Trinka, Eugen
KRAUSE, Roland ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy: A systematic review and individual participant data meta-analysis
Proposal for revised classification of epilepsies and epileptic syndromes. Commission on classification and terminology of the international League against epilepsy. Epilepsia 30 (1989), 389–399.
Koutroumanidis, M., Arzimanoglou, A., Caraballo, R., et al. The role of EEG in the diagnosis and classification of the epilepsy syndromes: a tool for clinical practice by the ILAE Neurophysiology Task Force (Part 1). Epileptic Disord 19 (2017), 233–298.
Stevelink, R., Koeleman, B.P.C., Sander, J.W., Jansen, F.E., Braun, K.P.J., Refractory juvenile myoclonic epilepsy: a meta-analysis of prevalence and risk factors. Eur J Neurol 26 (2019), 856–864.
Kasteleijn-Nolst Trenité, D.G.A., Schmitz, B., Janz, D., et al. Consensus on diagnosis and management of JME: from founder's observations to current trends. Epilepsy Behav 28:Suppl 1 (2013), S87–S90.
Vorderwülbecke, B.J., Wandschneider, B., Weber, Y., Holtkamp, M., Genetic generalized epilepsies in adults - challenging assumptions and dogmas. Nat Rev Neurol, 2021, 10.1038/s41582-021-00583-9 published online Nov 26.
Sillanpää, M., Haataja, L., Shinnar, S., Perceived impact of childhood-onset epilepsy on quality of life as an adult. Epilepsia 45 (2004), 971–977.
Perucca, P., Carter, J., Vahle, V., Gilliam, F.G., Adverse antiepileptic drug effects: toward a clinically and neurobiologically relevant taxonomy. Neurology 72 (2009), 1223–1229.
Kamitaki, B.K., Janmohamed, M., Kandula, P., et al. Clinical and EEG factors associated with antiseizure medication resistance in idiopathic generalized epilepsy. Epilepsia, 2021, 10.1111/epi.17104 published online Oct 27.
Choi, H., Detyniecki, K., Bazil, C., et al. Development and validation of a predictive model of drug-resistant genetic generalized epilepsy. Neurology 95 (2020), e2150–e2160.
Lamberink, H.J., Otte, W.M., Geerts, A.T., et al. Individualised prediction model of seizure recurrence and long-term outcomes after withdrawal of antiepileptic drugs in seizure-free patients: a systematic review and individual participant data meta-analysis. Lancet Neurol 16 (2017), 523–531.
Stewart, L.A., Clarke, M., Rovers, M., et al. Preferred reporting items for systematic review and meta-analyses of individual participant data: the PRISMA-IPD statement. JAMA 313 (2015), 1657–1665.
Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.M., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 350, 2015, g7594.
Gelisse, P., Genton, P., Thomas, P., Rey, M., Samuelian, J.C., Dravet, C., Clinical factors of drug resistance in juvenile myoclonic epilepsy. J Neurol Neurosurg Psychiatry 70 (2001), 240–243.
Wells, G., Shea, B., Robertson, J., et al. The Newcastle-Ottawa scale (NOS) for Assessing The Quality of Nonrandomized Studies in Meta-Analysis. 2000 http://www3.med.unipmn.it/dispense_ebm/2009-2010/Corso%20Perfezionamento%20EBM_Faggiano/NOS_oxford.pdf. (Accessed 27 December 2021)
Kwan, P., Arzimanoglou, A., Berg, A.T., et al. Definition of drug resistant epilepsy: consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies. Epilepsia 51 (2010), 1069–1077.
Azur, M.J., Stuart, E.A., Frangakis, C., Leaf, P.J., Multiple imputation by chained equations: what is it and how does it work?. Int J Methods Psychiatr Res 20 (2011), 40–49.
Fawcett, T., An introduction to ROC analysis. Pattern Recogn Lett 27 (2006), 861–874.
Uno, H., Cai, T., Pencina, M.J., D'Agostino, R.B., Wei, L.J., On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med 30 (2011), 1105–1117.
Steyerberg, E.W., Harrell, F.E. Jr., Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol 69 (2016), 245–247.
Debray, T.P.A., Moons, K.G.M., Ahmed, I., Koffijberg, H., Riley, R.D., A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med 32 (2013), 3158–3180.
Mandrekar, J.N., Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 5 (2010), 1315–1316.
Viloria Alebesque, A., Bellosta Diago, E., Santos Lasaosa, S., Mauri Llerda, J.A., [Juvenile myoclonic epilepsy: long-term prognosis and antiepileptic drug withdrawal]. An Sist Sanit Navar 43 (2020), 43–49.
Asadi-Pooya, A.A., Hashemzehi, Z., Emami, M., Predictors of seizure control in patients with juvenile myoclonic epilepsy (JME). Seizure 23 (2014), 889–891.
Baykan, B., Altindag, E.A., Bebek, N., et al. Myoclonic seizures subside in the fourth decade in juvenile myoclonic epilepsy. Neurology 70 (2008), 2123–2129.
Cação, G., Parra, J., Mannan, S., Sisodiya, S.M., Sander, J.W., Juvenile myoclonic epilepsy refractory to treatment in a tertiary referral center. Epilepsy Behav 82 (2018), 81–86.
Cerulli Irelli, E., Morano, A., Barone, F.A., et al. Persistent treatment resistance in genetic generalized epilepsy: a long-term outcome study in a tertiary epilepsy center. Epilepsia 61 (2020), 2452–2460.
Chowdhury, A., Brodie, M.J., Pharmacological outcomes in juvenile myoclonic epilepsy: support for sodium valproate. Epilepsy Res 119 (2016), 62–66.
Silvennoinen, K., de Lange, N., Zagaglia, S., et al. Comparative effectiveness of antiepileptic drugs in juvenile myoclonic epilepsy. Epilepsia Open 4 (2019), 420–430.
Gesche, J., Christensen, J., Hjalgrim, H., Rubboli, G., Beier, C.P., Epidemiology and outcome of idiopathic generalized epilepsy in adults. Eur J Neurol 27 (2020), 676–684.
Gürer, R., Aydın, Ş., Özkara, Ç., Outcomes of low-dose valproic acid treatment in patients with juvenile myoclonic epilepsy. Seizure 70 (2019), 43–48.
Hernández-Vanegas, L.E., Jara-Prado, A., Ochoa, A., et al. High-dose versus low-dose valproate for the treatment of juvenile myoclonic epilepsy: going from low to high. Epilepsy Behav 61 (2016), 34–40.
Jayalakshmi, S., Vooturi, S., Bana, A.K., Sailaja, S., Somayajula, S., Mohandas, S., Factors associated with lack of response to valproic acid monotherapy in juvenile myoclonic epilepsy. Seizure 23 (2014), 527–532.
Karakis, I., Pathmanathan, J.S., Chang, R., Cook, E.F., Cash, S.S., Cole, A.J., Prognostic value of EEG asymmetries for development of drug-resistance in drug-naïve patients with genetic generalized epilepsies. Clin Neurophysiol 125 (2014), 263–269.
Pietrafusa, N., La Neve, A., de Palma, L., et al. Juvenile myoclonic epilepsy: long-term prognosis and risk factors. Brain Dev 43 (2021), 688–697.
Vijai, J., Cherian, P.J., Stlaja, P.N., Anand, A., Radhakrishnan, K., Clinical characteristics of a South Indian cohort of juvenile myoclonic epilepsy probands. Seizure 12 (2003), 490–496.
Schneider-von Podewils, F., Gasse, C., Geithner, J., et al. Clinical predictors of the long-term social outcome and quality of life in juvenile myoclonic epilepsy: 20-65 years of follow-up. Epilepsia 55 (2014), 322–330.
Sun, Y., Seneviratne, U., Perucca, P., et al. Generalized polyspike train: an EEG biomarker of drug-resistant idiopathic generalized epilepsy. Neurology 91 (2018), e1822–e1830.
Syvertsen, M., Fløgstad, I., Enger, U., Landmark, C.J., Koht, J., Antiepileptic drug withdrawal in juvenile myoclonic epilepsy. Acta Neurol Scand 139 (2019), 192–198.
Viswanathan, L.G., Mundlamuri, R.C., Raghavendra, K., et al. Long-Term seizures outcome in juvenile myoclonic epilepsy (JME): a retrospective cohort study in an Indian population. Int J Epilepsy 7 (2021), 15–21.
Vorderwülbecke, B.J., Kirschbaum, A., Merkle, H., Senf, P., Holtkamp, M., Discontinuing antiepileptic drugs in long-standing idiopathic generalised epilepsy. J Neurol 266 (2019), 2554–2559.
Zhang, Y., Chen, J., Ren, J., Liu, W., Yang, T., Zhou, D., Clinical features and treatment outcomes of Juvenile myoclonic epilepsy patients. Epilepsia Open 4 (2019), 302–308.
Szaflarski, J.P., Lindsell, C.J., Zakaria, T., Banks, C., Privitera, M.D., Seizure control in patients with idiopathic generalized epilepsies: EEG determinants of medication response. Epilepsy Behav 17 (2010), 525–530.
Chen, Y., Chen, J., Chen, X., et al. Predictors of outcome in juvenile myoclonic epilepsy. Risk Manag Healthc Pol 13 (2020), 609–613.
Shakeshaft, A., Panjwani, N., Collingwood, A., et al. Sex-specific disease modifiers in juvenile myoclonic epilepsy. Sci Rep, 12, 2022, 2785.
Marson, A., Burnside, G., Appleton, R., et al. The SANAD II study of the effectiveness and cost-effectiveness of valproate versus levetiracetam for newly diagnosed generalised and unclassifiable epilepsy: an open-label, non-inferiority, multicentre, phase 4, randomised controlled trial. Lancet 397 (2021), 1375–1386.
Ascoli, M., Mastroianni, G., Gasparini, S., et al. Diagnostic and therapeutic approach to drug-resistant juvenile myoclonic epilepsy. Expert Rev Neurother 21 (2021), 1265–1273.
International League Against Epilepsy Consortium on Complex Epilepsies. Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies. Nat Commun, 9, 2018, 5269.
Dalrymple, J., Appleby, J., Cross sectional study of reporting of epileptic seizures to general practitioners. BMJ 320 (2000), 94–97.
Amudhan, S., Gururaj, G., Satishchandra, P., Epilepsy in India II: impact, burden, and need for a multisectoral public health response. Ann Indian Acad Neurol 18 (2015), 369–381.
International League Against Epilepsy Consortium on Complex Epilepsies. Genome-wide meta-analysis of over 29,000 people with epilepsy reveals 26 loci and subtype-specific genetic architecture. medRxiv, 2022, 10.1101/2022.06.08.22276120 published online June 14.
Mars, N., Lindbohm, J.V., Briotta Parolo, P.D., et al. Systematic comparison of family history and polygenic risk across 24 common diseases. bioRxiv, 2022, 10.1101/2022.07.06.22277333 published online July 7.
Berg, A.T., Levy, S.R., Testa, F.M., D'Souza, R., Remission of epilepsy after two drug failures in children: a prospective study. Ann Neurol 65 (2009), 510–519.
Chen, Z., Brodie, M.J., Liew, D., Kwan, P., Treatment outcomes in patients with newly diagnosed epilepsy treated with established and new antiepileptic drugs: a 30-year longitudinal cohort study. JAMA Neurol 75 (2018), 279–286.
Del Felice, A., Beghi, E., Boero, G., et al. Early versus late remission in a cohort of patients with newly diagnosed epilepsy. Epilepsia 51 (2010), 37–42.
Berg, A.T., Vickrey, B.G., Testa, F.M., et al. How long does it take for epilepsy to become intractable? A prospective investigation. Ann Neurol 60 (2006), 73–79.
Téllez-Zenteno, J.F., Hernández-Ronquillo, L., Buckley, S., Zahagun, R., Rizvi, S., A validation of the new definition of drug-resistant epilepsy by the International League against Epilepsy. Epilepsia 55 (2014), 829–834.
Cummings, P., Missing data and multiple imputation. JAMA Pediatr 167 (2013), 656–661.
Shakeshaft, A., Panjwani, N., McDowall, R., et al. Trait impulsivity in juvenile myoclonic epilepsy. Ann Clin Transl Neurol 8 (2021), 138–152.
Vale, C.L., Rydzewska, L.H.M., Rovers, M.M., et al. Uptake of systematic reviews and meta-analyses based on individual participant data in clinical practice guidelines: descriptive study. BMJ, 350, 2015, h1088.