Objective score from initial interview identifies patients with probable dissociative seizures

Grading objective diagnostic components in paroxysmal events: One-year follow-up at a tertiary epilepsy center

OBJECTIVE

This study was undertaken to develop a model and perform a preliminary internal validation study of the Scale for Objective Diagnostic Components of Paroxysmal Events (STAMP).

METHODS

We developed STAMP, which builds on the International League Against Epilepsy task force scale for functional seizures with additional categories for epileptic seizures and syncope. We included 200 consecutive referrals to a Dutch tertiary epilepsy center to evaluate seizurelike events. We recorded demographic and clinical data and collected the clinical evaluation at referral and after 3, 6, 9, and 12 months of follow-up. We ascertained the STAMP at each time point and evaluated factors predicting an improvement in STAMP grade during follow-up.

RESULTS

Of the 200 referrals at baseline, 131 were classified as having epileptic seizures, 17 as functional seizures, and three as syncope, and 49 were unclassifiable. STAMP grade at baseline was 4 (absent) in 56 individuals, 3 (circumstantial) in 78, 2 (clinically established) in six, and 1 (documented) in 11. Over time, 62 cases STAMP grades improved, and 23 remained unclassifiable. A refinement of STAMP grade during follow-up was due to successful event recordings in 34 people (30 video-electroencephalographic [EEG] recordings, four tilt table testing), home videos or clinician-witnessed events in 13, and identification of interictal EEG or magnetic resonance imaging abnormalities in seven. An improved STAMP grade after 12 months of follow-up was significantly more likely in those with higher event frequency, unclassifiable events, longer event duration, and a shorter time since the first event and less likely in those with a history suggestive of seizures.

SIGNIFICANCE

This epilepsy service evaluation underscores the crucial role of event recording in improving diagnostic certainty. STAMP may be used to monitor diagnostic performance over time but requires further validation.

The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials

Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard.

Medication use in patients with functional seizures from a public and a private hospital

PURPOSE

Currently, we have limited knowledge of any potential differences among patients with functional seizures (FS), otherwise known as psychogenic non-epileptic seizures (PNES), from different socioeconomic backgrounds. Investigating medication use among these patients may provide insight into the quality and intensity of medical care they receive. Thus, we aimed to assess and compare the frequency and quantity of antiseizure medications (ASMs), and psychiatric and other medications used among patients with FS from a private and public epilepsy monitoring units (EMUs) in Cape Town, South Africa.

METHODS

Only video-electroencephalographically (video-EEG) confirmed patients with FS with no comorbid epilepsy were eligible for the study. For this retrospective case-control study we collected data on patients' medication-taking histories using digital patient records, starting with the earliest available digital patient record for each hospital.

RESULTS

A total of 305 patients from a private hospital and 67 patients from a public hospital were included in the study (N = 372). Patients with FS attending the public hospital had lower odds of taking any ASMs at presentation (aOR=0.39, 95% CI [0.20, 0.75]) and ever taking psychiatric medications (aOR=0.41, 95% CI [0.22, 0.78]) compared to FS patients from the private hospital. They did, however, have higher odds of being discharged with an ASM (aOR=6.60, 95% CI [3.27, 13.35]) and ever taking cardiovascular medication (aOR=2.69, 95% CI [1.22, 5.90]) when compared to the private hospital patients. With every additional presenting ASM (aOR=0.63, 95% CI [0.45, 0.89]) and psychiatric medication (aOR=0.58, 95% CI [0.40, 0.84]) the odds of being from the public hospital decreased. However, they increased with every additional discharge ASM (aOR=3.63, 95% CI [2.30, 5.72]) and cardiovascular medication (aOR=1.26, 95% CI [1.02, 1.55]).

CONCLUSION

Standard approaches to pharmacological treatment for patients with FS differed between the public and private hospitals and may indicate a gap in quality of care.

Elevated Mortality Rate in Patients With Functional Seizures After Diagnosis and Referral

Background and Objectives

To evaluate the standardized mortality ratio (SMR) of patients in the United States referred to a multidisciplinary clinic for treatment of functional seizures.

Methods

We identified patients who had or had not died based on automated retrospective review of electronic health records from a registry of patients referred to a single-center multidisciplinary functional seizures treatment clinic. We calculated an SMR by comparing the number of observed deaths with the expected number of deaths in an age-matched, sex-matched, and race-matched population within the same state, and year records were available.

Results

A total of 700 patients with functional seizures (mean age 37 years, 78% female) were followed up for 1,329 patient-years for a median of 15 months per patient (interquartile range 6-37 months). We observed 11 deaths, corresponding to a mortality rate of 8.2 per 1,000 patient-years and an SMR of 2.4 (95% confidence interval: 1.17-4.22). Five of 9 patients with identified circumstances around their death were in hospice care when they passed. None of the identified causes of death were related to seizures directly.

Discussion

These data provide further evidence of elevated mortality in functional seizures soon after diagnosis and referral to treatment. These data from the decentralized health care system of the United States build on the findings from other countries with large-scale health registries.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.

Using Verbally-Reported and Video-Observed Semiology to Identify Functional Seizures

Diagnosis of functional seizures, also known as psychogenic nonepileptic seizures, starts with a clinical interview and description of the seizures. A targeted approach to this evaluation can provide valuable information to gauge the likelihood of functional seizures as compared with other similar conditions including but not limited to epileptic seizures. This review focuses on the use of patient and witness descriptions and seizure videos to identify patients with probable functional seizures. Particular emphasis is given to recognizing the limitations of the available data and the influence of health-care provider expertise on diagnostic accuracy.

Increasing challenges to trial recruitment and conduct over time

OBJECTIVE

This study was undertaken to evaluate how the challenges in the recruitment and retention of participants in clinical trials for focal onset epilepsy have changed over time.

METHODS

In this systematic analysis of randomized clinical trials of adjunct antiseizure medications for medication-resistant focal onset epilepsy, we evaluated how the numbers of participants, sites, and countries have changed since the first such trial in 1990. We also evaluated the proportion of participants who completed each trial phase and their reasons for early trial exit. We analyzed these trends using mixed effects generalized linear models accounting for the influence of the number of trial sites and trial-specific variability.

RESULTS

The number of participants per site has steadily decreased over decades, with recent trials recruiting fewer than five participants per site (reduction by .16 participants/site/year, p < .0001). Fewer participants also progressed from recruitment to randomization over time (odds ratio = .94/year, p = .014). Concurrently, there has been an increase in the placebo response over time (increase in median percent reduction of .4%/year, p = .02; odds ratio of increase in 50% responder rate of 1.03/year, p = .02), which was not directly associated with the number of sites per trial (p > .20).

SIGNIFICANCE

This historical analysis highlights the increasing challenges with participant recruitment and retention, as well as increasing placebo response. It serves as a call to action to change clinical trial design to address these challenges.

Functional Seizure Clinics: A Proposed Financially Viable Solution to the Neurologist Supply and Demand Mismatch

Background and Objectives

Projections from recent studies suggest that by 2025, there will not be enough neurologists to meet the demand in 41 states. In this study, we investigate the financial impact and improved access to care for persons with epilepsy that is possible by implementing a multidisciplinary treatment clinic for persons with functional seizures (FS), previously referred to as psychogenic nonepileptic seizures, thus separating those patients out of an epilepsy clinic.

Methods

This observational retrospective study used real-time data of 156 patients referred to an FS clinic integrated into a tertiary care epilepsy center to simulate its effect on epilepsy division access and finances. Access was measured using simulations of the number of return patient visits (RPVs) and new patient visits (NPVs) of patients with FS to a dedicated epilepsy clinic, based on survey results inquiring about the standard of care without the FS clinic. Finances were simulated using the resultant access multiplied by respective wRVU and reimbursement per CPT code.

Results

Treatment of 156 patients with FS in a multidisciplinary FS clinic resulted in 343 newly opened NPVs, reimbursement of $102,000, and 1,200 wRVUs in our dedicated epilepsy clinic. There were 686 RPVs, $103,000 in reimbursement, and 1,320 wRVUs. Relative to the total number of NPVs with epilepsy clinic epileptologists, 343 NPVs represent a biennial 15.5% increase in available new patient visit slots.

Discussion

Our findings describe the financial viability of integrating a treatment clinic for persons with FS by directing them to FS-specialized treatment and thereby increasing access for patients with probable epilepsy to the dedicated epilepsy clinic. This study provides a potential solution to the national mismatch in the supply and demand of neurologists and an initial framework to use for those who wish to establish or integrate FS services in their institution.

Amygdala subfield and prefrontal cortex abnormalities in patients with functional seizures

BACKGROUND

Functional seizures (FS) are paroxysmal episodes, resembling epileptic seizures, but without underlying epileptic abnormality. The aetiology and neuroanatomic associations are incompletely understood. Recent brain imaging data indicate cerebral changes, however, without clarifying possible pathophysiology. In the present study, we specifically investigated the neuroanatomic changes in subregions of the amygdala and hippocampus in FS.

METHODS

T1 MRI scans of 37 female patients with FS and 37 age-matched female seizure naïve controls (SNC) were analyzed retrospectively in FreeSurfer version 7.1. Seizure naïve controls included patients with depression and anxiety disorders. The analysis included whole-brain cortical thickness, subcortical volumes, and subfields of the amygdala and hippocampus. Group comparisons were carried out using multivariable linear models.

RESULTS

The FS and SNC groups did not differ in the whole hippocampus and amygdala volumes. However, patients had a significant reduction of the right lateral amygdala volume (p = 0.00041), an increase of the right central amygdala, (p = 0.037), and thinning of the left superior frontal gyrus (p = 0.024). Additional findings in patients were increased volumes of the right medial amygdala (p = 0.031), left anterior amygdala (p = 0.017), and left dentate gyrus of the hippocampus (p = 0.035).

CONCLUSIONS

The observations from the amygdala and hippocampus segmentation affirm that there are neuroanatomic associations of FS. The pattern of these changes aligned with some of the cerebral changes described in chronic stress conditions and depression. The pattern of detected changes further study, and may, after validation, provide biomarkers for diagnosis and treatment.

Epilepsy diagnosis using a clinical decision tool and artificially intelligent electroencephalography

OBJECTIVE

To construct a tool for non-experts to calculate the probability of epilepsy based on easily obtained clinical information combined with an artificial intelligence readout of the electroencephalogram (AI-EEG).

MATERIALS AND METHODS

We performed a chart review of 205 consecutive patients aged 18 years or older who underwent routine EEG. We created a point system to calculate the pre-EEG probability of epilepsy in a pilot study cohort. We also computed a post-test probability based on AI-EEG results.

RESULTS

One hundred and four (50.7%) patients were female, the mean age was 46 years, and 110 (53.7%) were diagnosed with epilepsy. Findings favoring epilepsy included developmental delay (12.6% vs 1.1%), prior neurological injury (51.4% vs 30.9%), childhood febrile seizures (4.6% vs 0.0%), postictal confusion (43.6% vs 20.0%), and witnessed convulsions (63.6% vs 21.1%); findings favoring alternative diagnoses were lightheadedness (3.6% vs 15.8%) or onset after prolonged sitting or standing (0.9% vs 7.4%). The final point system included 6 predictors: Presyncope (-3 points), cardiac history (-1), convulsion or forced head turn (+3), neurological disease history (+2), multiple prior spells (+1), postictal confusion (+2). Total scores of ≤1 point predicted <5% probability of epilepsy, while cumulative scores ≥7 predicted >95%. The model showed excellent discrimination (AUROC: 0.86). A positive AI-EEG substantially increases the probability of epilepsy. The impact is greatest when the pre-EEG probability is near 30%.

SIGNIFICANCE

A decision tool using a small number of historical clinical features accurately predicts the probability of epilepsy. In indeterminate cases, AI-assisted EEG helps resolve uncertainty. This tool holds promise for use by healthcare workers without specialty epilepsy training if validated in an independent cohort.

Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls

PURPOSE

Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations.

METHODS

Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners.

RESULTS

After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm³, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS.

CONCLUSIONS

These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.

Factors associated with comorbid epilepsy in patients with psychogenic nonepileptic seizures: A large cohort study

OBJECTIVE

Comorbid epilepsy and psychogenic nonepileptic seizures (PNES) occur in 12-22% of cases and the diagnosis of both simultaneous disorders is challenging. We aimed to identify baseline characteristics that may help distinguish patients with PNES-only from those with comorbid epilepsy.

METHODS

We performed a longitudinal cohort study on those patients diagnosed with PNES in our epilepsy monitoring unit (EMU) between May 2001 and February 2011, prospectively followed up until September 2016. Patients were classified into PNES-only, PNES + possible or probable epilepsy, and PNES + definite epilepsy based on the clinical, vEEG, and neuroimaging data. Demographic and basal clinical data were obtained from chart review. Multiple regression models were performed to identify significant predictors of PNES + definite epilepsy, excluding patients with only possible or probable epilepsy for this specific analysis.

RESULTS

One-hundred and ninety four patients with PNES-only, 30 with PNES + possible or probable epilepsy and 47 with PNES + definite epilepsy were included. 73.8% were female and the mean age at EMU admission was 37.4 ± standard deviation 13.5 years. Patients with PNES + definite epilepsy most likely had never worked, had history of febrile seizures, structural brain lesions, developmental disabilities, and maximum reported seizure duration between 0.5 and 2 min. Patients with PNES-only were on fewer anti-seizure medications (ASM), reported more frequently an initial minor head trauma, seizures longer than 10 min, and a higher number of neurological and medical illnesses - being migraine (18.1%), other types of headaches (18.5%), and asthma (15.5%) the most prevalent ones. All p < 0.05. On the hierarchical regression analysis, history of febrile seizures, developmental disabilities, brain lesions, longest reported seizure duration between 0.5 and 2 min, and lack of neurological comorbidity, remained as significant predictors of PNES + epilepsy. The model's performance of a 5-fold cross-validation analysis showed an overall accuracy of 84.7% to classify patients correctly.

CONCLUSIONS

Some demographic and clinical characteristics may support the presence of comorbid epilepsy in patients with PNES, being unemployment, the presence of brain lesions, developmental disabilities, history of febrile seizures, seizure duration and lack of comorbid headaches the most relevant ones.

Pitfalls using smartphones videos in diagnosing functional seizures

Expert review of seizure semiology looking at video recordings independent of EEG has been found to be useful for diagnosing functional seizures. Videos recorded outside the hospital containing "spells" have similar sensitivity to EEG when quality recordings are evaluated. Recently, smartphone videos were shown to serve as an adjunct to standard history and physical examination with similar diagnostic yields when compared to diagnostic video-EEG monitoring and reviewed by experts. However, caution must be exercised when interpreting videos of paroxysmal neurological events recorded by caregivers to ensure proper video quality is maintained and recorded event is representative. In this report, we present a case of initial identification of and event falsely suggesting functional seizures in a patient with epilepsy. The smartphone video of a "seizure" was recorded by his wife using her smartphone. Despite a quality recording and a history consistent with epilepsy, the smartphone video reviewed during evaluation in the clinic suggested a functional behavior in contrast to the history that suggested epilepsy manifest as convulsions. Instead of bilateral tonic-clonic motor movements, bizarre, intermittent non-clonic wild flinging movements and vocalization were identified on the smartphone video. The discordance between the clnical history and ideo prompted inpatient video-EEG monitoring. The same nonepileptic semiology was subsequently clarified to represent a physiological nonepileptic event. The event on the smartphone was typical of his agitated post-ictal state following an electroclinical tonic-clonic seizure. With treatment the seizures became controlled with antiseizure medication in long-term follow-up. We highlight the pitfalls using patient-recorded smartphone videos in patients diagnosed with epilepsy. Understanding the utility of smartphones as an adjunct to the clinical history will help in differentiating epileptic from functional seizures.

A minority of patients with functional seizures have abnormalities on neuroimaging

OBJECTIVE

Functional seizures often are managed incorrectly as a diagnosis of exclusion. However, a significant minority of patients with functional seizures may have abnormalities on neuroimaging that typically are associated with epilepsy, leading to diagnostic confusion. We evaluated the rate of epilepsy-associated findings on MRI, FDG-PET, and CT in patients with functional seizures.

METHODS

We studied radiologists' reports from neuroimages at our comprehensive epilepsy center from a consecutive series of patients diagnosed with functional seizures without comorbid epilepsy from 2006 to 2019. We summarized the MRI, FDG-PET, and CT results as follows: within normal limits, incidental findings, unrelated findings, non-specific abnormalities, post-operative study, epilepsy risk factors (ERF), borderline epilepsy-associated findings (EAF), and definitive EAF.

RESULTS

Of the 256 MRIs, 23% demonstrated ERF (5%), borderline EAF (8%), or definitive EAF (10%). The most common EAF was hippocampal sclerosis, with the majority of borderline EAF comprising hippocampal atrophy without T2 hyperintensity or vice versa. Of the 87 FDG-PETs, 26% demonstrated borderline EAF (17%) or definitive EAF (8%). Epilepsy-associated findings primarily included focal hypometabolism, especially of the temporal lobes, with borderline findings including subtle or questionable hypometabolism. Of the 51 CTs, only 2% had definitive EAF.

SIGNIFICANCE

This large case series provides further evidence that, while uncommon, EAF are seen in patients with functional seizures. A significant portion of these abnormal findings are borderline. The moderately high rate of these abnormalities may represent framing bias from the indication of the study being "seizures," the relative subtlety of EAF, or effects of antiseizure medications.

A novel scale for suspicion of psychogenic nonepileptic seizures: development and accuracy

OBJECTIVE

The differential diagnosis between epileptic and psychogenic nonepileptic seizures (PNES) is challenging, yet suspicion of PNES is crucial to rethink treatment strategies and select patients for diagnostic confirmation through video EEG (VEEG). We developed a novel scale to prospectively suspect PNES.

METHODS

First, we developed a 51-item scale in two steps, based upon literature review and panel expert opinion. A pilot study verified the applicability of the instrument, followed by a prospective evaluation of 158 patients (66.5% women, mean age 33 years) who were diagnosed for prolonged VEEG. Only epileptic seizures were recorded in 103 patients, and the other 55 had either isolated PNES or both types of seizures. Statistical procedures identified 15 items scored between 0 and 3 that best discriminated patients with and without PNES, with a high degree of consistency.

RESULTS

Internal consistency reliability of the scale for suspicion of PNES was 0.77 with Cronbach's Alpha Coefficient and 0.95 with Rasch Item Reliability Index, and performance did not differ according to the patient's gender. For a cut-off score of 20 (of 45) points, area under the curve was 0.92 (95% IC: 0.87-0.96), with an accuracy of 87%, sensitivity of 89%, specificity of 85%, positive predictive value of 77%, and negative predictive value of 94% (95% IC) for a diagnosis of PNES.

CONCLUSIONS

The scale for suspicion of PNES (SS-PNES) has high accuracy to a reliable suspicion of PNES, helping with the interpretation of apparent seizure refractoriness, reframing treatment strategies, and streamlining referral for prolonged VEEG.

Epilepsy, dissociative seizures, and mixed: Associations with time to video-EEG

PURPOSE

Video-electroencephalographic monitoring (VEM) is a core component to the diagnosis and evaluation of epilepsy and dissociative seizures (DS)-also known as functional or psychogenic seizures-but VEM evaluation often occurs later than recommended. To understand why delays occur, we compared how patient-reported clinical factors were associated with time from first seizure to VEM (TVEM) in patients with epilepsy, DS or mixed.

METHODS

We acquired data from 1245 consecutive patients with epilepsy, VEM-documented DS or mixed epilepsy and DS. We used multivariate log-normal regression with recursive feature elimination (RFE) to evaluate which of 76 clinical factors interacting with patients' diagnoses were associated with TVEM.

RESULTS

The mean and median TVEM were 14.6 years and 10 years, respectively (IQR 3-23 years). In the multivariate RFE model, the factors associated with longer TVEM in all patients included unemployment and not student status, more antiseizure medications (current and past), concussion, and ictal behavior suggestive of temporal lobe epilepsy. Average TVEM was shorter for DS than epilepsy, particularly for patients with depression, anxiety, migraines, and eye closure. Average TVEM was longer specifically for patients with DS taking more medications, more seizure types, non-metastatic cancer, and with other psychiatric comorbidities.

CONCLUSIONS

In all patients with seizures, trials of numerous antiseizure medications, unemployment and non-student status was associated with longer TVEM. These associations highlight a disconnect between International League Against Epilepsy practice parameters and observed referral patterns in epilepsy. In patients with dissociative seizures, some but not all factors classically associated with DS reduced TVEM.

Validation of a predictive calculator to distinguish between patients presenting with dissociative versus epileptic seizures

Dissociative seizures (also known as psychogenic nonepileptic seizures) are a common functional neurological disorder that can be difficult to distinguish from epileptic seizures. Patients with dissociative seizures provide diagnostic challenges, leading to delays in care, inappropriate care, and significant healthcare utilization and associated costs. The dissociative seizure likelihood score (DSLS) was developed by Kerr and colleagues at UCLA to distinguish between patients with epileptic seizures and dissociative seizures based on clinical and medication history as well as features of seizure semiology. We validated this calculator at the University of Colorado, which is a Level 4 National Association of Epilepsy Center. The DSLS accurately predicted the diagnosis in 81% of patients, despite local variability in the factors associated with epileptic versus dissociative seizures between the two populations. The DSLS can be a useful tool to assist with history taking and may have important utility for clinical decision making with these difficult to distinguish patient populations.

Factors associated with delay to video-EEG in dissociative seizures

PURPOSE

While certain clinical factors suggest a diagnosis of dissociative seizures (DS), otherwise known as functional or psychogenic nonepileptic seizures (PNES), ictal video-electroencephalography monitoring (VEM) is the gold standard for diagnosis. Diagnostic delays were associated with worse quality of life and more seizures, even after treatment. To understand why diagnoses were delayed, we evaluated which factors were associated with delay to VEM.

METHODS

Using data from 341 consecutive patients with VEM-documented dissociative seizures, we used multivariate log-normal regression with recursive feature elimination (RFE) and multiple imputation of some missing data to evaluate which of 76 clinical factors were associated with time from first dissociative seizure to VEM.

RESULTS

The mean delay to VEM was 8.4 years (median 3 years, IQR 1-10 years). In the RFE multivariate model, the factors associated with longer delay to VEM included more past antiseizure medications (0.19 log-years/medication, standard error (SE) 0.05), more medications for other medical conditions (0.06 log-years/medication, SE 0.03), history of physical abuse (0.75 log-years, SE 0.27), and more seizure types (0.36 log-years/type, SE 0.11). Factors associated with shorter delay included active employment or student status (-1.05 log-years, SE 0.21) and higher seizure frequency (0.14 log-years/log[seizure/month], SE 0.06).

CONCLUSIONS

Patients with greater medical and seizure complexity had longer delays. Delays in multiple domains of healthcare can be common for victims of physical abuse. Unemployed and non-student patients may have had more barriers to access VEM. These results support earlier referral of complex cases to a comprehensive epilepsy center.

Reliability of additional reported seizure manifestations to identify dissociative seizures

PURPOSE

Descriptions of seizure manifestations (SM), or semiology, can help localize the symptomatogenic zone and subsequently included brain regions involved in epileptic seizures, as well as identify patients with dissociative seizures (DS). Patients and witnesses are not trained observers, so these descriptions may vary from expert review of seizure video recordings of seizures. To better understand how reported factors can help identify patients with DS or epileptic seizures (ES), we evaluated the associations between more than 30 SMs and diagnosis using standardized interviews.

METHODS

Based on patient- and observer-reported data from 490 patients with diagnoses documented by video-electoencephalography, we compared the rate of each SM in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic seizure-like events (PSLE), mixed DS and ES, and inconclusive testing.

RESULTS

In addition to SMs that we described in a prior manuscript, the following were associated with DS: light triggers, emotional stress trigger, pre-ictal and post-ictal headache, post-ictal muscle soreness, and ictal sensory symptoms. The following were associated with ES: triggered by missing medication, aura of déjà vu, and leftward eye deviation. There were numerous manifestations separately associated with mixed ES and DS.

CONCLUSIONS

Reported SM can help identify patients with DS, but no manifestation is pathognomonic for either ES or DS. Patients with mixed ES and DS reported factors divergent from both ES-alone and DS-alone.