Physiologic Nonepileptic Events Research Articles

Home video prediction of epileptic vs. nonepileptic seizures in US veterans

ObjectiveUsing video-EEG (v-EEG) diagnosis as a gold standard, we assessed the predictive diagnostic value of home videos of spells with or without additional limited demographic data in US veterans referred for evaluation of epilepsy. Veterans, in particular, stand to benefit from improved diagnostic tools given higher rates of PNES and limited accessibility to care. MethodsThis was a prospective, blinded diagnostic accuracy study in adults conducted at the Houston VA Medical Center from 12/2015–06/2019. Patients with a definitive diagnosis of epileptic seizures (ES), psychogenic nonepileptic seizures (PNES), or physiologic nonepileptic events (PhysNEE) from v-EEG monitoring were asked to submit home videos. Four board-certified epileptologists blinded to the original diagnosis formulated a diagnostic impression based upon the home video review alone and video plus limited demographic data. ResultsFifty patients (30 males; mean age 47.7 years) submitted home videos. Of these, 14 had ES, 33 had PNES, and three had PhysNEE diagnosed by v-EEG. The diagnostic accuracy by video alone was 88.0%, with a sensitivity of 83.9% and specificity of 89.6%. Providing raters with basic patient demographic information in addition to the home videos did not significantly improve diagnostic accuracy when comparing to reviewing the videos alone. Inter-rater agreement between four raters based on video was moderate with both videos alone (kappa = 0.59) and video plus limited demographic data (kappa = 0.60). SignificanceThis study demonstrated that home videos of paroxysmal events could be an important tool in reliably diagnosing ES vs. PNES in veterans referred for evaluation of epilepsy when interpreted by experts. A moderate inter-rater reliability was observed in this study.

Video quality using outpatient smartphone videos in epilepsy: Results from the OSmartViE study.

The aim of this study was to evaluate the quality of smartphone videos (SVs) of neurologic events in adult epilepsy outpatients. The use of home video recording in patients with neurological disease states is increasing. Experts interpretation of outpatient smartphone videos of seizures and neurological events has demonstrated similar diagnostic accuracy to inpatient video-electroencephalography (EEG) monitoring. A prospective, multicenter cohort study was conducted to evaluate SV quality in patients with paroxysmal neurologic events from August 15, 2015 through August 31, 2018. Epileptic seizures (ESs), psychogenic nonepileptic attacks (PNEAs), and physiologic nonepileptic events (PhysNEEs) were confirmed by video-EEG monitoring. Experts and senior neurology residents blindly viewed cloud-based SVs without clinical information. Quality ratings with regard to technical and operator-driven metrics were provided in responses to a survey. Forty-four patients (31 women, age 45.1years [r=20-82]) were included and 530 SVs were viewed by a mean of seven experts and six residents; one video per patient was reviewed for a mean of 133.8s (r=9-543). In all, 30 patients had PNEAs, 11 had ESs, and three had PhysNEEs. Quality was suitable in 70.8% of SVs (375/530 total views), with 36/44 (81.8%) patient SVs rated as adequate by the majority of reviewers. Accuracy improved with the presence of convulsive features from 72.4% to 98.2% in ESs and from 71.1% to 95.7% in PNEAs. An accurate diagnosis was given by all reviewers (100%) in 11/44 SVs (all PNEAs). Audio was rated as good by 86.2% of reviewers for these SVs compared with 75.4% for the remaining SVs (p=0.01). Lighting was better in SVs associated with high accuracy (p=0.06), but clarity was not (p=0.59). Poor video quality yielded unknown diagnoses in 24.2% of the SVs reviewed. Features hindering diagnosis were limited interactivity, restricted field of view and short video duration. Smartphone video quality is adequate for clinical interpretation in the majority of patients with paroxysmal neurologic events. Quality can be optimized by encouraging interactivity with the patient, adequate duration of the SV, and enlarged field of view during videography. Quality limitations were primarily operational though accuracy remained for SV review of ESs and PNEAs.

Assessment of the Predictive Value of Outpatient Smartphone Videos for Diagnosis of Epileptic Seizures

Misdiagnosis of epilepsy is common. Video electroencephalogram provides a definitive diagnosis but is impractical for many patients referred for evaluation of epilepsy. To evaluate the accuracy of outpatient smartphone videos in epilepsy. This prospective, masked, diagnostic accuracy study (the OSmartViE study) took place between August 31, 2015, and August 31, 2018, at 8 academic epilepsy centers in the United States and included a convenience sample of 44 nonconsecutive outpatients who volunteered a smartphone video during evaluation and subsequently underwent video electroencephalogram monitoring. Three epileptologists uploaded videos for physicians from the 8 epilepsy centers to review. Measures of performance (accuracy, sensitivity, specificity, positive predictive value, and negative predictive value) for smartphone video-based diagnosis by experts and trainees (the index test) were compared with those for history and physical examination and video electroencephalogram monitoring (the reference standard). Forty-four eligible epilepsy clinic outpatients (31 women [70.5%]; mean [range] age, 45.1 [20-82] years) submitted smartphone videos (530 total physician reviews). Final video electroencephalogram diagnoses included 11 epileptic seizures, 30 psychogenic nonepileptic attacks, and 3 physiologic nonepileptic events. Expert interpretation of a smartphone video was accurate in predicting a video electroencephalogram monitoring diagnosis of epileptic seizures 89.1% (95% CI, 84.2%-92.9%) of the time, with a specificity of 93.3% (95% CI, 88.3%-96.6%). Resident responses were less accurate for all metrics involving epileptic seizures and psychogenic nonepileptic attacks, despite greater confidence. Motor signs during events increased accuracy. One-fourth of the smartphone videos were correctly diagnosed by 100% of the reviewing physicians, composed solely of psychogenic attacks. When histories and physical examination results were combined with smartphone videos, correct diagnoses rose from 78.6% to 95.2%. The odds of receiving a correct diagnosis were 5.45 times greater using smartphone video alongside patient history and physical examination results than with history and physical examination alone (95% CI, 1.01-54.3; P = .02). Outpatient smartphone video review by experts has predictive and additive value for diagnosing epileptic seizures. Smartphone videos may reliably aid psychogenic nonepileptic attacks diagnosis for some people.

Analyzing reliability of seizure diagnosis based on semiology

This study aimed to determine the accuracy of seizure diagnosis by semiological analysis and to assess the factors that affect diagnostic reliability. A total of 150 video clips of seizures from 50 patients (each with three seizures of the same type) were observed by eight epileptologists, 12 neurologists, and 20 physicians (internists). The videos included 37 series of epileptic seizures, eight series of physiologic nonepileptic events (PNEEs), and five series of psychogenic nonepileptic seizures (PNESs). After observing each video, the doctors chose the diagnosis of epileptic seizures or nonepileptic events for the patient; if the latter was chosen, they further chose the diagnosis of PNESs or PNEEs. The overall diagnostic accuracy rate for epileptic seizures and nonepileptic events increased from 0.614 to 0.660 after observations of all three seizures (p<0.001). The diagnostic sensitivity and specificity of epileptic seizures were 0.770 and 0.808, respectively, for the epileptologists. These values were significantly higher than those for the neurologists (0.660 and 0.699) and physicians (0.588 and 0.658). A wide range of diagnostic accuracy was found across the various seizures types. An accuracy rate of 0.895 for generalized tonic–clonic seizures was the highest, followed by 0.800 for dialeptic seizures and then 0.760 for automotor seizures. The accuracy rates for myoclonic seizures (0.530), hypermotor seizures (0.481), gelastic/dacrystic seizures (0.438), and PNESs (0.430) were poor. The reliability of semiological diagnosis of seizures is greatly affected by the seizure type as well as the doctor's experience. Although the overall reliability is limited, it can be improved by observing more seizures.

Video-EEG Monitoring in a Geriatric Veteran Population

Video-EEG monitoring is a standard tool to evaluate possible seizures. However, there are limited data available for the elderly population, and no data are available for the elderly veteran population. The records of 71 veterans ages 60 and older who underwent video-EEG monitoring at the Michael E. DeBakey Veterans Affairs Medical Center from 1999 to 2006 were reviewed. The average age was 68 years, and 94% were male. Typical events occurred in 34 of 71 patients (48%). Twelve patients (35%) had epileptic seizures; most were temporal lobe seizures (75%). The remaining 22 patients (65%) had a nonepileptic event. Of these, 10 (45%) had psychogenic nonepileptic seizures and 12 (55%) had other physiologic nonepileptic events. AEDs were previously prescribed in 14 of the 22 nonepileptic patients (64%). In our study, most events were nonepileptic, partly reflecting the unique nature of the VA population. There was a slightly higher number of physiologic nonepileptic events; however, psychogenic seizures are also frequently seen. Most of the patients with nonepileptic events were previously treated with AEDs. Most of the epileptic events captured were temporal lobe seizures. Video-EEG monitoring in the elderly is useful in the characterization of paroxysmal events.

Are we overusing the diagnosis of psychogenic non-epileptic events?

In order to determine how often results of video/EEG (V-EEG) studies may change the clinical diagnosis of paroxysmal events, we prospectively studied 100 consecutive patients (75 females, 25 males) admitted for diagnosis of recurrent paroxysmal spells. The presumed diagnosis of the referring physician was obtained. Episodes were classified as epileptic seizures (ES), psychogenic non-epileptic events (PNEE), or physiologic non-epileptic events (PhysNEE). Eighty-seven patients had diagnostic events. A final diagnosis of ES was made in 21 patients, PNEE in 39, PNEE + ES in 20, and PhysNEE in seven. All PhysNEE were unsuspected. ES were misdiagnosed as PNEE more frequently than the reverse (57% vs. 12%, P< 0.001). Among the 64 patients with recorded events who had been suspected of having PNEE, 14 (21.9%) were misdiagnosed: two had PhysNEE and 12 (18.75%) had ES. Among the 23 patients with recorded events who were thought to have ES, 12 (39.1%) were misdiagnosed: seven had PNEE, five PhysNEE. V-EEG changed the clinical diagnosis in 29.8% of the patients with recorded events. Our data suggests that clinicians have become more aware of PNEE since the advent of V-EEG and have little problem recognizing them. However, they may be more prone to make a false-positive diagnosis of PNEE in ES with some atypical features. At this point, efforts should be channeled to better training in the proper recognition of ES that mimic PNEE.

When should induction protocols be used in the diagnostic evaluation of patients with paroxysmal events?

To determine the timing of spontaneous psychogenic nonepileptic events (PNEE) during video-EEG telemetry (VEEG), and the need to use induction protocols (IP). We studied 100 consecutive patients (75 females, 25 males) admitted to our inpatient VEEG unit from July 1994 to June 1996 for differential diagnosis of paroxysmal events. We recorded the time to the first diagnostic spontaneous event, identified by the patient or a family member as typical. Episodes were classified as PNEE, physiologic nonepileptic events (PhysNEE), and epileptic seizures (ES). The mean duration of VEEG was 74+/-SD 54.1 h. In 82 patients, a diagnostic event occurred spontaneously. The first event was an ES in 22 patients, a PNEE in 53, and a PhysNEE in 7. The time to first diagnostic event was significantly shorter for PNEE than for ES [15.0+/-SD 16.3 h (range 5 min to 58 h) vs. 28.6+/-SD 34.0 h (range 1-110 h) F=15.621, p < 0.0001]. In the first 24 h, 77.4% of the patients with PNEE had an event. By 48 h, all but 2 (96.2%) had had diagnostic events. After the first 58 h of monitoring, all patients with PNEE experienced a spontaneous diagnostic event. Spontaneous events can be expected to occur within 48 h in most patients with PNEE. Therefore, if IP are to be used as a diagnostic tool, we suggest that they be withheld during the initial 48 h of VEEG monitoring.