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NEUROLOGY 2009;72:1646-1652
© 2009 American Academy of Neurology

A self-administered screening instrument for psychogenic nonepileptic seizures

T. U. Syed, MD, MPH, A. M. Arozullah, MD, MPH, K. L. Loparo, PhD, R. Jamasebi, MS, G. P. Suciu, PhD, MSPH, C. Griffin, MD, R. Mani, MD, I. Syed, BS, T. Loddenkemper, MD and A. V. Alexopoulos, MD, MPH

From the Epilepsy Center (T.U.S., C.G., R.M.), Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH; Sections of General Internal Medicine and Health Promotion Research of the Department of Medicine (A.M.A.), University of Illinois at Chicago, and Center for Management of Complex Chronic Care, Jesse Brown VA Medical Center, Chicago, IL; Department of Electrical Engineering and Computer Science (K.L.L., R.J.), Case Western Reserve University, Cleveland; Public Health Program (G.P.S.), Nova Southeastern University, Fort Lauderdale, FL; Florida International University (I.S.), Miami; Children’s Hospital Boston (T.L.), Harvard Medical School, Boston, MA; and Department of Neurology (T.L., A.V.A.), Cleveland Clinic Foundation, Cleveland.

Address correspondence and reprint requests to Dr. Tanvir U. Syed, Hanna House, 5th Floor, Room 540, 11100 Euclid Ave., Cleveland, OH 44106 tanvir.syed{at}uhhospitals.org

Background: Delay in distinguishing psychogenic nonepileptic seizures (PNES) from epilepsy may result in significant health and economic burdens. Screening tools are needed to facilitate earlier identification of patients with PNES, thereby maximizing cost-effective use of video electroencephalography (VEEG), the expensive gold standard for differentiating PNES from epilepsy. We developed and prospectively validated a self-administered PNES screening questionnaire using variables known to distinguish PNES from epilepsy patients.

Methods: Adults referred for inpatient VEEG monitoring at two epilepsy centers were prospectively invited to complete a preliminary 209-item questionnaire assessing demographic, clinical, seizure-related, and psychosocial information that appeared in the literature as potentially useful indicators of PNES. A hybrid neural–bayesian classifier was trained to predict PNES using a sample at one center, and was prospectively validated on a separate set of naive patients from both centers.

Results: Of 211 enrolled subjects from the training center, 181 met the study criteria for either PNES (n = 48, 27%), epilepsy (n = 116, 64%), or coexisting PNES and epilepsy (n = 17, 9%). Variable reduction procedures identified 53 questionnaire items that were necessary to accurately predict PNES diagnosis. The hybrid classifier predicted PNES diagnosis with 94% sensitivity and 83% specificity at the training center, and 85% sensitivity and 85% specificity at the second center (n = 46; 17 PNES, 26 epilepsy, 3 with coexisting PNES and epilepsy).

Conclusions: We developed and prospectively validated a self-administered psychogenic nonepileptic seizure screening questionnaire that could hasten referral for video electroencephalography and reduce the health and economic burdens from delayed diagnosis or misdiagnosis.

Abbreviations: ANN = artificial neural network; AUROC = area under the receiver operating characteristic curve; BRIQ = Behavioral Reaction to Illness; CASE = Communication and Attitudinal Self-Efficacy; EMU = epilepsy monitoring unit; ES = epileptic seizures; HPLP = Health-Promoting Lifestyle Profile; LR+ = sensitivity/(1 – specificity); LR– = (1 – sensitivity)/specificity; MHLC = Multidimensional Locus of Control; PAI = Personality Assessment Inventory; MOS-SSQ = Medical Outcomes Study Social Support Questionnaire; NES = nonepileptic seizures; NPV = negative predictive value; PNES = psychogenic nonepileptic seizures; PPV = positive predictive value; QOLIE = Quality of Life in Epilepsy; ROC = receiver operating characteristic; VEEG = video electroencephalography.

Supplemental data at www.neurology.org

Disclosure: The authors report no disclosures.

Received September 5, 2008. Accepted in final form February 12, 2009.




Correspondence:

Read all Correspondence

Mobile phone video camera to diagnose nonepileptic seizures
Dinesh Chand Khandelwal
Neurology Online, 17 Aug 2009 [Full text]
Reply from the authors
Tanvir U. Syed, et al.
Neurology Online, 17 Aug 2009 [Full text]

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