Abstract
AbstractObjectiveSimilarities 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.Methods628 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.ResultsEvidence 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]).SignificanceCompared 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.
Publisher
Cold Spring Harbor Laboratory