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Research Article| Volume 105, P10-13, February 2023

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Cluster analysis of a large dataset of patients with juvenile myoclonic epilepsy: Predicting response to treatment

Published:January 10, 2023DOI:https://doi.org/10.1016/j.seizure.2023.01.006

      Highlights

      • Cluster analysis showed that there were two distinct clusters of homogeneous subgroups of patients with JME.
      • Response to treatment was different between the clusters (as a trend) (p = 0.076).
      • It is ideal to apply this technique of statistical analysis on a very large dataset of patients in a multicenter study.

      Abstract

      Objectives

      The purpose of the current study was to apply Two-step cluster analysis on a large dataset of patients with juvenile myoclonic epilepsy (JME). We hypothesized that there are distinct subgroups of patients with similar clinical characteristics. We also hypothesized that the seizure outcome is different between these clusters.

      Methods

      This was a retrospective study of a prospectively developed database. All patients with a diagnosis of JME were studied at the epilepsy center at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2022. The Two-Step cluster analysis (Schwarz's Bayesian Criterion) was applied to the whole dataset. In the next step, the seizure outcome was compared between the clusters of patients.

      Results

      Two hundred and ninety-five patients were included. Two-Step cluster analysis showed that there were two distinct clusters of homogeneous subgroups of patients with JME, presenting with more or less similar clinical characteristics, with a fair (0.4) silhouette measure of cohesion and separation. One hundred and eighty-one patients had a follow up duration of 12 months or longer at our center. Response to treatment at 12 months of follow-up was different between the clusters (as a trend): 43 patients (39.1%) from cluster 1 and 18 people (25.4%) from cluster 2 were free of all seizure types (p = 0.076).

      Conclusion

      The Two-Step cluster analysis identified two distinct clusters of patients with JME. Individuals with JME, who also have absence seizures, are less likely to enjoy a seizure free state with ASMs.

      Keywords

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      References

        • Hirsch E.
        • French J.
        • Scheffer I.E.
        • Bogacz A.
        • Alsaadi T.
        • Sperling M.R.
        • et al.
        ILAE definition of the idiopathic generalized epilepsy syndromes: position statement by the ILAE task force on nosology and definitions.
        Epilepsia. Jun 2022; 63: 1475-1499https://doi.org/10.1111/epi.17236
        • Asadi-Pooya A.A.
        • Hashemzehi Z.
        • Emami M.
        Epidemiology and clinical manifestations of juvenile myoclonic epilepsy (JME) in Iran.
        Neurol Sci. May 2015; 36: 713-716https://doi.org/10.1007/s10072-014-2021-0
        • Chen Y.
        • Chen J.
        • Chen X.
        • Wang R.
        • Zeng J.
        • Wang F.
        • et al.
        Predictors of outcome in juvenile myoclonic epilepsy.
        Risk Manag Healthc Policy. Jun 19 2020; 13: 609-613https://doi.org/10.2147/RMHP.S244725
        • Zhang Y.
        • Chen J.
        • Ren J.
        • Liu W.
        • Yang T.
        • Zhou D.
        Clinical features and treatment outcomes of Juvenile myoclonic epilepsy patients.
        Epilepsia Open. 2019 Apr 19; 4: 302-308https://doi.org/10.1002/epi4.12321
        • 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. Jun 2019; 26: 856-864https://doi.org/10.1111/ene.13811
        • Höfler J.
        • Unterberger I.
        • Dobesberger J.
        • Kuchukhidze G.
        • Walser G.
        • Trinka E.
        Seizure outcome in 175 patients with juvenile myoclonic epilepsy–a long-term observational study.
        Epilepsy Res. Dec 2014; 108: 1817-1824https://doi.org/10.1016/j.eplepsyres.2014.09.008
        • Arntsen V.
        • Sand T.
        • Syvertsen M.R.
        • Brodtkorb E.
        Prolonged epileptiform EEG runs are associated with persistent seizures in juvenile myoclonic epilepsy.
        Epilepsy Res. Aug 2017; 134: 26-32https://doi.org/10.1016/j.eplepsyres.2017.05.003
        • Asadi-Pooya A.A.
        • Hashemzehi Z.
        • Emami M.
        Predictors of seizure control in patients with juvenile myoclonic epilepsy (JME).
        Seizure. Nov 2014; 23: 889-891https://doi.org/10.1016/j.seizure.2014.08.004
        • Pietrafusa N.
        • La Neve A.
        • de Palma L.
        • Boero G.
        • Luisi C.
        • Vigevano F.
        • et al.
        Juvenile myoclonic epilepsy: long-term prognosis and risk factors.
        Brain Dev. Jun 2021; 43: 688-697https://doi.org/10.1016/j.braindev.2021.02.005
        • Turco F.
        • Bonanni E.
        • Milano C.
        • Pizzanelli C.
        • Steinwurzel C.
        • Morganti R.
        • et al.
        Prolonged epileptic discharges predict seizure recurrence in JME: insights from prolonged ambulatory EEG.
        Epilepsia. May 2021; 62: 1184-1192https://doi.org/10.1111/epi.16875
        • 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. Feb 2001; 70: 240-243https://doi.org/10.1136/jnnp.70.2.240
        • Benassi M.
        • Garofalo S.
        • Ambrosini F.
        • Sant'Angelo R.P.
        • Raggini R.
        • De Paoli G.
        • et al.
        Using two-step cluster analysis and latent class cluster analysis to classify the cognitive heterogeneity of cross-diagnostic psychiatric inpatients.
        Front Psychol. 2020; 111085
        • Kent P.
        • Jensen R.K.
        • Kongsted A.
        A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS two step cluster analysis, latent Gold and SNOB.
        BMC Med Res Methodol. 2014; 14113
        • Stevelink R.
        • Al-Toma D.
        • Jansen F.E.
        • Lamberink H.J.
        • Asadi-Pooya A.A.
        • Farazdaghi M.
        • et al.
        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.
        EClinicalMedicine. Nov 11 2022; 53101732https://doi.org/10.1016/j.eclinm.2022.101732
        • Ascoli M.
        • Mastroianni G.
        • Gasparini S.
        • Striano P.
        • Cianci V.
        • Neri S.
        • et al.
        Diagnostic and therapeutic approach to drug-resistant juvenile myoclonic epilepsy.
        Expert Rev Neurother. Nov 2021; 21: 1265-1273https://doi.org/10.1080/14737175.2021.1931126