title: Improved diagnosis in children with partial epilepsy using a multivariable prediction model based on EEG network characteristics.
abstract: BACKGROUND
  Electroencephalogram (EEG) acquisition is routinely performed to support an epileptic origin of paroxysmal events in patients referred with a possible diagnosis of epilepsy. However, in children with partial epilepsies the interictal EEGs are often normal. We aimed to develop a multivariable diagnostic prediction model based on electroencephalogram functional network characteristics.
  
  
  RESULTS
  Routinely performed interictal EEG recordings at first presentation of 35 children diagnosed with partial epilepsies, and of 35 children in whom the diagnosis epilepsy was excluded (control group), were used to develop the prediction model. Children with partial epilepsy were individually matched on age and gender with children from the control group. Periods of resting-state EEG, free of abnormal slowing or epileptiform activity, were selected to construct functional networks of correlated activity. We calculated multiple network characteristics previously used in functional network epilepsy studies and used these measures to build a robust, decision tree based, prediction model. Based on epileptiform EEG activity only, EEG results supported the diagnosis of with a sensitivity and specificity of 0.77 and 0.91 respectively. In contrast, the prediction model had a sensitivity of 0.96 [95% confidence interval: 0.78-1.00] and specificity of 0.95 [95% confidence interval: 0.76-1.00] in correctly differentiating patients from controls. The overall discriminative power, quantified as the area under the receiver operating characteristic curve, was 0.89, defined as an excellent model performance. The need of a multivariable network analysis to improve diagnostic accuracy was emphasized by the lack of discriminatory power using single network characteristics or EEG's power spectral density.
  
  
  CONCLUSIONS
  Diagnostic accuracy in children with partial epilepsy is substantially improved with a model combining functional network characteristics derived from multi-channel electroencephalogram recordings. Early and accurate diagnosis is important to start necessary treatment as soon as possible and inform patients and parents on possible risks and psychosocial aspects in relation to the diagnosis.
authors.affiliation: Rudolf Magnus Institute of Neuroscience, Department of Pediatric Neurology, University Medical Center Utrecht, Utrecht, The Netherlands. E.vanDiessen-3@umcutrecht.nl
authors.forename: Eric
authors.initials: E
authors.lastname: van Diessen
authors.forename: Willem M
authors.initials: WM
authors.lastname: Otte
authors.forename: Kees P J
authors.initials: KP
authors.lastname: Braun
authors.forename: Cornelis J
authors.initials: CJ
authors.lastname: Stam
authors.forename: Floor E
authors.initials: FE
authors.lastname: Jansen
mesh_terms: D000293:Adolescent; D000465:Algorithms; D016022:Case-Control Studies; D002648:Child; D004569:Electroencephalography; D004828:Epilepsies, Partial / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D012372:ROC Curve; D015203:Reproducibility of Results
references.citation: Epilepsia. 2007 Jun;48(6):1062-6
references.pmid: 17553117
references.citation: Epilepsia. 2011 Sep;52(9):1733-40
references.pmid: 21801165
references.citation: Hum Brain Mapp. 2011 Mar;32(3):413-25
references.pmid: 20589941
references.citation: Hum Brain Mapp. 2009 May;30(5):1580-91
references.pmid: 18661506
references.citation: Clin Neurophysiol. 2007 Apr;118(4):918-27
references.pmid: 17314065
references.citation: Clin Neurophysiol. 2010 Feb;121(2):172-85
references.pmid: 20045375
references.citation: Radiology. 1982 Apr;143(1):29-36
references.pmid: 7063747
references.citation: Stat Med. 1984 Apr-Jun;3(2):143-52
references.pmid: 6463451
references.citation: J Neurol Neurosurg Psychiatry. 2005 Jun;76 Suppl 2:ii2-7
references.pmid: 15961864
references.citation: Acad Radiol. 2003 Dec;10(12):1359-68
references.pmid: 14697004
references.citation: Phys Rev Lett. 2010 Mar 19;104(11):118701
references.pmid: 20366507
references.citation: Epilepsia. 2011 Jan;52(1):84-93
references.pmid: 21126244
references.citation: Prog Neurobiol. 2005 Sep-Oct;77(1-2):1-37
references.pmid: 16289760
references.citation: Nonlinear Biomed Phys. 2007 Jul 05;1(1):3
references.pmid: 17908336
references.citation: Clin Neurophysiol. 2004 May;115(5):1202-11
references.pmid: 15066546
references.citation: Curr Opin Neurol. 2009 Aug;22(4):340-7
references.pmid: 19494774
references.citation: Trends Cogn Sci. 2004 Sep;8(9):418-25
references.pmid: 15350243
references.citation: PLoS One. 2010 May 26;5(5):e10839
references.pmid: 20520774
references.citation: Neurology. 2011 Sep 6;77(10):938-44
references.pmid: 21832213
references.citation: Epilepsia. 2008 Feb;49(2):269-80
references.pmid: 17825075
references.citation: BMC Bioinformatics. 2011 Mar 17;12:77
references.pmid: 21414208
references.citation: Ann Neurol. 1990 Mar;27(3):330-4
references.pmid: 2327741
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Jun;71(6 Pt 2):065103
references.pmid: 16089800
references.citation: Neuroimage. 2010 Sep;52(3):1059-69
references.pmid: 19819337
references.citation: Clin Neurophysiol. 2003 Jan;114(1):50-5
references.pmid: 12495763
references.citation: J Clin Epidemiol. 2003 May;56(5):441-7
references.pmid: 12812818
references.citation: PLoS One. 2010 Jan 08;5(1):e8525
references.pmid: 20072616
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Feb;75(2 Pt 2):027105
references.pmid: 17358454