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Title: Distinct psychopathology profiles in patients with epileptic seizures compared to non-epileptic psychogenic seizures.
Austin Authors: Wang, Albert D;Leong, Michelle;Johnstone, Benjamin;Rayner, Genevieve ;Kalincik, Tomas;Roos, Izanne;Kwan, Patrick;O'Brien, Terence J;Velakoulis, Dennis;Malpas, Charles B
Affiliation: Clinical Outcomes Research Unit (CORe), Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Australia
Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Australia
Melbourne School of Psychological Sciences, The University of Melbourne, Australia
Department of Neurology, Alfred Health, Australia
Department of Neurosciences, Monash University, Australia
Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Department of Psychiatry, The University of Melbourne, Australia
Department of Psychiatry, Royal Melbourne Hospital, Australia
Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Australia
Department of Neurology, Royal Melbourne Hospital, Australia
Issue Date: Dec-2019
Date: 2019-11-01
Publication information: Epilepsy research 2019; 158: 106234
Abstract: Similarities in clinical presentations between epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) produces a risk of misdiagnosis. Video-EEG monitoring (VEM) is the diagnostic gold standard, but involves significant cost and time commitment, suggesting a need for efficient screening tools. 628 patients were recruited from an inpatient VEM unit; 293 patients with ES, 158 with PNES, 31 both ES and PNES, and 146 non-diagnostic. Patients completed the SCL-90-R, a standardised 90-item psychopathology instrument. Bayesian linear models were computed to investigate whether SCL-90-R domain scores or the overall psychopathology factor p differed between groups. Receiver operating characteristic (ROC) curves were computed to investigate the PNES classification accuracy of each domain score and p. A machine learning algorithm was also used to determine which subset of SCL-90-R items produced the greatest classification accuracy. Evidence was found for elevated scores in PNES compared to ES groups in the symptom domains of anxiety (b = 0.47, 95%HDI = [0.10, 0.80]), phobic anxiety (b = 1.32, 95%HDI = [0.98, 1.69]), somatisation (b = 0.84, 95%HDI = [0.49, 1.20]), and the general psychopathology factor p (b = 1.35, 95%HDI = [0.86, 1.82]). Of the SCL-90-R domain scores, somatisation produced the highest classification accuracy (AUC = 0.74, 95%CI = [0.69, 0.79]). The genetic algorithm produced a 6-item subset from the SCL-90-R, which produced comparable classification accuracy to the somatisation scores (AUC = 0.73, 95%CI = [0.64, 0.82]). Compared to patients with ES, patients with PNES report greater symptoms of somatisation, general anxiety, and phobic anxiety against a background of generally elevated psychopathology. While self-reported psychopathology scores are not accurate enough for diagnosis in isolation, elevated psychopathology in these domains should raise the suspicion of PNES in clinical settings.
DOI: 10.1016/j.eplepsyres.2019.106234
Journal: Epilepsy research
PubMed URL: 31706137
Type: Journal Article
Subjects: Epilepsy
Epileptic seizures
Machine learning
Psychiatric comorbidity
Psychogenic non-epileptic seizures
Appears in Collections:Journal articles

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