Rationale:
The current gold standard for diagnosis of psychogenic nonepileptic seizures (PNES) is the use of continuous video EEG to capture typical events. PNES has historically been considered a diagnosis of exclusion, but there is utility in the recognition of positive signs and typical semiology features to aid in its diagnosis and to avoid potentially iatrogenic interventions. However, there is a lack of consensus regarding classification of PNES semiology and the implications of different PNES types on diagnosis and management of the disorder. This study identifies common positive signs seen in PNES patients admitted to the epilepsy monitoring unit. It classifies all patients by semiology and evaluates whether patient self-report can accurately predict PNES classification.
Methods:
A retrospective chart review was performed on all patients who presented to the Rush University Medical Center Epilepsy Monitoring Unit (RUMC EMU) in 2019 without a prior confirmed diagnosis of PNES. Patients were included if at least one typical PNES was captured on video EEG. The presence of ten different semiological signs were documented, and PNES were classified using Asadi-Pooya’s system (Epilepsy Behav. 2019) first by patients’ descriptions of seizure at initial outpatient visit, and then by review of video EEG of captured PNES. Interrater reliability and Kappa coefficient was calculated to determine concordance between patient self-report and video EEG.
Results:
The study included 55 patients: 36 (65%) were female. The median number of PNES captured was one but ranged up to thirty. Most patients (67%) had a suspected diagnosis of PNES prior to admission. The most common semiological sign captured in the RUMC EMU was eye closure, followed by waxing and waning quality, then ictal hyperventilation, and susceptibility to external influence. The most frequent class of PNES identified were non-motor subjective (42%), followed by motor (29%), then non-motor akinetic (20%) and mixed (9%). There was little concordance between semiological signs observed on video EEG and patient self-report. Kappa interrater agreement for PNES classification was fair (0.25).
Conclusions:
PNES semiology can be evaluated by video analysis of typical events which are amenable to semiological classification. Non-motor subjective was the most common class of PNES. The accuracy of patient descriptions of semiological signs is poor in PNES, and there is only fair agreement between patient report and video EEG on semiology classification. Despite unreliable self-reporting of the phenomenology of the events, a majority of patients were suspected to have PNES prior to diagnostic evaluation.
Funding: N/A