TY - JOUR
T1 - Distinct psychopathology profiles in patients with epileptic seizures compared to non-epileptic psychogenic seizures
AU - Wang, Albert D.
AU - Leong, Michelle
AU - Johnstone, Benjamin
AU - Rayner, Genevieve
AU - Kalincik, Tomas
AU - Roos, Izanne
AU - Kwan, Patrick
AU - O'Brien, Terence J.
AU - Velakoulis, Dennis
AU - Malpas, Charles B.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Objective: 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. Methods: 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. Results: 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]). Significance: 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.
AB - Objective: 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. Methods: 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. Results: 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]). Significance: 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.
KW - Epilepsy
KW - Epileptic seizures
KW - Machine learning
KW - Psychiatric comorbidity
KW - Psychogenic non-epileptic seizures
KW - Psychopathology
UR - https://www.scopus.com/pages/publications/85074405027
U2 - 10.1016/j.eplepsyres.2019.106234
DO - 10.1016/j.eplepsyres.2019.106234
M3 - Article
C2 - 31706137
AN - SCOPUS:85074405027
SN - 0920-1211
VL - 158
JO - Epilepsy Research
JF - Epilepsy Research
M1 - 106234
ER -