An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features.
Drug-refractory epilepsy
EEG
Phase lag index functional connectivity
Prediction model
Support vector machine
Journal
Journal of neurology
ISSN: 1432-1459
Titre abrégé: J Neurol
Pays: Germany
ID NLM: 0423161
Informations de publication
Date de publication:
Mar 2022
Mar 2022
Historique:
received:
14
05
2021
accepted:
17
07
2021
revised:
15
07
2021
pubmed:
27
7
2021
medline:
23
2
2022
entrez:
26
7
2021
Statut:
ppublish
Résumé
Although the use of antiepileptic drugs (AEDs) is routine, 30-40% of patients with epilepsy (PWEs) experience drug resistance. Thus, early identification of AED resistance will help optimize treatment regimens and improve patients' prognoses. However, there have been few studies on this topic to date. Here, we try to establish an integrative prediction model of AED resistance for drug-naive PWEs, and to identify the clinical and Electroencephalogram (EEG) factors that affect their outcomes. One hundred sixty-four PWEs naive to AEDs treated at a tertiary care center from January 2014 to June 2020 were retrospectively analyzed. A total of 113 of these patients were well controlled and 53 were drug refractory with regular AED treatment for more than one year. Eighty clinical characteristics and 684 EEG functional connectivity variables based on phase lag index before drug initiation were identified. Overall, 80% of each group was chosen to establish a support vector machine (SVM) model with ten-fold cross validation, and the other 20% were used to evaluate the model's performance. Absolute weight value was used to rank the features that had impacts on classification. An integrative algorithm was modeled to predict AED resistance for drug-naive PWEs by SVM based on clinical characteristics and EEG functional connectivity values. The model had an accuracy of 94% [95% confidence interval (CI) 0.85-1.0], sensitivity of 95% [95% CI 0.82-1.0], specificity of 93% [95% CI 0.77-1.0], and an area under the curve (AUC) of 0.98 [95% CI 0.91-1.0]. The p values of accuracy, sensitivity specificity and AUC were calculated as 0.001, 0.001, 0.01 and 0.001, respectively. The δ band fromT4-FZ and T3-PZ, α band from T3-T6 and β band from F7-CZ and FP2-F3 were the top five EEG features that impacted the SVM classifier. We constructed an integrative prediction algorithm of AED resistance for drug-naive PWEs. Its utility in clinical settings should be evaluated in the future.
Identifiants
pubmed: 34308506
doi: 10.1007/s00415-021-10718-z
pii: 10.1007/s00415-021-10718-z
doi:
Substances chimiques
Pharmaceutical Preparations
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1501-1514Subventions
Organisme : Henan Province's Gong Jian Program
ID : SB201901074
Organisme : 23456 Talent Engineering
ID : ZC20200371
Organisme : National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
ID : 81801291
Informations de copyright
© 2021. Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Thijs RD, Surges R, O’Brien TJ, Sander JW (2019) Epilepsy in adults. Lancet (Lon Engl) 393:689–701
Fiest KM, Sauro KM, Wiebe S, Patten SB, Kwon CS, Dykeman J, Pringsheim T, Lorenzetti DL, Jetté N (2017) Prevalence and incidence of epilepsy: a systematic review and meta-analysis of international studies. Neurology 88:296–303
pubmed: 27986877
pmcid: 5272794
Kwan P, Brodie MJ (2000) Early identification of refractory epilepsy. N Engl J Med 342:314–319
pubmed: 10660394
Engel J Jr, Wiebe S, French J, Sperling M, Williamson P, Spencer D, Gumnit R, Zahn C, Westbrook E, Enos B (2003) Practice parameter: temporal lobe and localized neocortical resections for epilepsy: report of the Quality Standards Subcommittee of the American Academy of Neurology, in association with the American Epilepsy Society and the American Association of Neurological Surgeons. Neurology 60:538–547
pubmed: 12601090
Haneef Z, Stern J, Dewar S, Engel J Jr (2010) Referral pattern for epilepsy surgery after evidence-based recommendations: a retrospective study. Neurology 75:699–704
pubmed: 20733145
pmcid: 2931651
Berg AT (2009) Identification of pharmacoresistant epilepsy. Neurol Clin 27:1003–1013
pubmed: 19853220
pmcid: 2827183
Shorvon SD (1996) The epidemiology and treatment of chronic and refractory epilepsy. Epilepsia 37(Suppl 2):S1-s3
pubmed: 8641240
Wirrell EC (2013) Predicting pharmacoresistance in pediatric epilepsy. Epilepsia 54(Suppl 2):19–22
pubmed: 23646966
Wassenaar M, Leijten FS, Egberts TC, Moons KG, Uijl SG (2013) Prognostic factors for medically intractable epilepsy: a systematic review. Epilepsy Res 106:301–310
pubmed: 23880113
Kim LG, Johnson TL, Marson AG, Chadwick DW (2006) Prediction of risk of seizure recurrence after a single seizure and early epilepsy: further results from the MESS trial. Lancet Neurol 5:317–322
pubmed: 16545748
Kennett R (2012) Modern electroencephalography. J Neurol 259:783–789
pubmed: 22314552
Dlugos D, Shinnar S, Cnaan A, Hu F, Moshé S, Mizrahi E, Masur D, Sogawa Y, Le Pichon JB, Levine C, Hirtz D, Clark P, Adamson PC, Glauser T (2013) Pretreatment EEG in childhood absence epilepsy: associations with attention and treatment outcome. Neurology 81:150–156
pubmed: 23719147
pmcid: 3770172
Colic S, Wither RG, Min L, Zhang L, Eubanks JH, Bardakjian BL (2015) Support vector machines using EEG features of cross-frequency coupling can predict treatment outcome in Mecp2-deficient mice. Ann Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2015:5606–5609
Cabrerizo M, Ayala M, Goryawala M, Jayakar P, Adjouadi M (2012) A new parametric feature descriptor for the classification of epileptic and control EEG records in pediatric population. Int J Neural Syst 22:1250001
pubmed: 23627587
Lin LC, Ouyang CS, Chiang CT, Yang RC, Wu RC, Wu HC (2014) Early prediction of medication refractoriness in children with idiopathic epilepsy based on scalp EEG analysis. Int J Neural Syst 24:1450023
pubmed: 25164248
Sporns O (2011) The human connectome: a complex network. Ann N Y Acad Sci 1224:109–125
pubmed: 21251014
Sporns O (2013) The human connectome: origins and challenges. Neuroimage 80:53–61
pubmed: 23528922
De Martino F, Valente G, Staeren N, Ashburner J, Goebel R, Formisano E (2008) Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage 43:44–58
pubmed: 18672070
Negishi M, Martuzzi R, Novotny EJ, Spencer DD, Constable RT (2011) Functional MRI connectivity as a predictor of the surgical outcome of epilepsy. Epilepsia 52:1733–1740
pubmed: 21801165
pmcid: 3169719
Fingelkurts AA, Fingelkurts AA, Rytsälä H, Suominen K, Isometsä E, Kähkönen S (2007) Impaired functional connectivity at EEG alpha and theta frequency bands in major depression. Hum Brain Mapp 28:247–261
pubmed: 16779797
Briels CT, Schoonhoven DN, Stam CJ, de Waal H, Scheltens P, Gouw AA (2020) Reproducibility of EEG functional connectivity in Alzheimer’s disease. Alzheimer’s Res Ther 12:68
Sargolzaei S, Cabrerizo M, Goryawala M, Eddin AS, Adjouadi M (2015) Scalp EEG brain functional connectivity networks in pediatric epilepsy. Comput Biol Med 56:158–166
pubmed: 25464357
Lee HW, Arora J, Papademetris X, Tokoglu F, Negishi M, Scheinost D, Farooque P, Blumenfeld H, Spencer DD, Constable RT (2014) Altered functional connectivity in seizure onset zones revealed by fMRI intrinsic connectivity. Neurology 83:2269–2277
pubmed: 25391304
pmcid: 4277677
Kim J, Lee WG, Park S, Park KM (2020) Can we predict drug response by functional connectivity in patients with juvenile myoclonic epilepsy? Clin Neurol Neurosurg 198:106119
pubmed: 32763668
Zhao Z, Wang C, Yuan Q, Zhao J, Ren Q, Xu Y, Li J, Yu Y (2020) Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia. Brain Res 1746:146979
pubmed: 32544500
Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28:1178–1193
pubmed: 17266107
pmcid: 6871367
Abbasi B, Goldenholz DM (2019) Machine learning applications in epilepsy. Epilepsia 60:2037–2047
pubmed: 31478577
Park Y, Luo L, Parhi KK, Netoff T (2011) Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 52:1761–1770
pubmed: 21692794
Zhang T, Chen W (2017) LMD based features for the automatic seizure detection of EEG signals using SVM. IEEE Trans Neural Syst Rehabilit Eng Publ IEEE Eng Med Biol Soc 25:1100–1108
Dian JA, Colic S, Chinvarun Y, Carlen PL, Bardakjian BL (2015) Identification of brain regions of interest for epilepsy surgery planning using support vector machines. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2015:6590–6593
Zhang JH, Han X, Zhao HW, Zhao D, Wang N, Zhao T, He GN, Zhu XR, Zhang Y, Han JY, Huang DL (2018) Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine. Br J Clin Pharmacol 84:2615–2624
pubmed: 30043454
pmcid: 6177722
Fisher RS, Acevedo C, Arzimanoglou A, Bogacz A, Cross JH, Elger CE, Engel J Jr, Forsgren L, French JA, Glynn M, Hesdorffer DC, Lee BI, Mathern GW, Moshé SL, Perucca E, Scheffer IE, Tomson T, Watanabe M, Wiebe S (2014) ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55:475–482
pubmed: 24730690
Gandy M, Sharpe L, Perry KN, Miller L, Thayer Z, Boserio J, Mohamed A (2013) Rates of DSM-IV mood, anxiety disorders, and suicidality in Australian adult epilepsy outpatients: a comparison of well-controlled versus refractory epilepsy. Epile Behav E&B 26:29–35
Kwan P, Arzimanoglou A, Berg AT, Brodie MJ, Allen Hauser W, Mathern G, Moshé SL, Perucca E, Wiebe S, French J (2010) Definition of drug resistant epilepsy: consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies. Epilepsia 51:1069–1077
pubmed: 19889013
pmcid: 19889013
Fisher RS, Cross JH, French JA, Higurashi N, Hirsch E, Jansen FE, Lagae L, Moshé SL, Peltola J, Roulet Perez E, Scheffer IE, Zuberi SM (2017) Operational classification of seizure types by the international league against epilepsy: position paper of the ILAE commission for classification and terminology. Epilepsia 58:522–530
pubmed: 28276060
Leguia MG, Andrzejak RG, Rummel C, Fan JM, Mirro EA, Tcheng TK, Rao VR, Baud MO (2021) Seizure cycles in focal epilepsy. JAMA Neurol 78:454–463
pubmed: 33555292
Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21
pubmed: 15102499
Luts J, Ojeda F, Van de Plas R, De Moor B, Van Huffel S, Suykens JA (2010) A tutorial on support vector machine-based methods for classification problems in chemometrics. Anal Chim Acta 665:129–145
pubmed: 20417323
Gromski PS, Xu Y, Correa E, Ellis DI, Turner ML, Goodacre R (2014) A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data. Anal Chim Acta 829:1–8
pubmed: 24856395
Henderson AR (2006) Testing experimental data for univariate normality. Clin Chim Acta Int J Clin Chem 366:112–129
Kwan P, Schachter SC, Brodie MJ (2011) Drug-resistant epilepsy. N Engl J Med 365:919–926
pubmed: 21899452
Löscher W, Potschka H, Sisodiya SM, Vezzani A (2020) Drug resistance in epilepsy: clinical impact, potential mechanisms, and new innovative treatment options. Pharmacol Rev 72:606–638
pubmed: 32540959
pmcid: 7300324
Kalilani L, Sun X, Pelgrims B, Noack-Rink M, Villanueva V (2018) The epidemiology of drug-resistant epilepsy: a systematic review and meta-analysis. Epilepsia 59:2179–2193
pubmed: 30426482
Semah F, Ryvlin P (2005) Can we predict refractory epilepsy at the time of diagnosis? Epile Disord Int Epile J Videotape 7(Suppl 1):S10-13
Yao L, Cai M, Chen Y, Shen C, Shi L, Guo Y (2019) Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning. Epile Behav E&B 96:92–97
Yang S, Han X, Wang N, Gu R, Chen W, Wang E, Chen Y, He G, Zhu X, Ma B, Li M, Zhao P, Han J, Fang Y, Zeng J, Bai Y, Zhao T, Chen Y (2020) Predicting seizure freedom with AED treatment in newly diagnosed patients with MRI-negative epilepsy: a large cohort and multicenter study. Epile Behav E&B 106:107022
Douw L, de Groot M, van Dellen E, Heimans JJ, Ronner HE, Stam CJ, Reijneveld JC (2010) Functional connectivity’ is a sensitive predictor of epilepsy diagnosis after the first seizure. PLoS ONE 5:e10839
pubmed: 20520774
pmcid: 2877105
van Diessen E, Otte WM, Braun KP, Stam CJ, Jansen FE (2013) Improved diagnosis in children with partial epilepsy using a multivariable prediction model based on EEG network characteristics. PLoS ONE 8:e59764
pubmed: 23565166
pmcid: 3614973
van Diessen E, Zweiphenning WJ, Jansen FE, Stam CJ, Braun KP, Otte WM (2014) Brain network organization in focal epilepsy: a systematic review and meta-analysis. PLoS ONE 9:e114606
pubmed: 25493432
pmcid: 4262431
Worrell GA, So EL, Kazemi J, O’Brien TJ, Mosewich RK, Cascino GD, Meyer FB, Marsh WR (2002) Focal ictal beta discharge on scalp EEG predicts excellent outcome of frontal lobe epilepsy surgery. Epilepsia 43:277–282
pubmed: 11906513
Gutierrez Nuno RA, Maharatna K (2019) A phase lag index hardware calculation for real-time electroencephalography studies. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2019:644–647