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

Diagnostic features of functional/ dissociative seizures in the first presentation of transient loss of consciousness

OBJECTIVES

Previous studies have identified features in patient's history and seizure descriptions supporting a clinical diagnosis of functional / dissociative seizures (FDS). However, most studies involved patients with chronic seizure disorders. This study explores the value of reported features for a clinical diagnosis of FDS in an adult population with a first presentation of transient loss of consciousness (TLoC).

METHODS

We prospectively recruited patients newly presenting with TLoC to an Emergency Department (ED), Acute Medical Unit (AMU; admitting ward for general medical patients), first seizure or syncope clinic. We invited participants to complete an online questionnaire, either at home or at time of initial assessment. Two expert raters determined cause of participants' TLOC after 6-month follow-up. We also reviewed clinical records at this timepoint to extract relevant information for assessment of putative diagnostic features (13 categorical variables and 6 interval or continuous variables), and validation of two previously-developed diagnostic classifiers.

RESULTS

We included 178 patients in final analysis (134 syncope, 32 epilepsy, 12 FDS). 3 categorical variables were significantly more common in FDS: fluctuating course or waxing/waning movements (p = 0.0037), asynchronous limb movements (p = 0.0024), and preserved ictal awareness or responsiveness (p = 0.0013). Three interval/continuous variables supported diagnosis of FDS: younger age at onset (area under receiver-operating characteristic curve [AUC] = 0.865 (0.771-0.960)); total non-ictal symptoms reported on structured review of systems (AUC = 0.834 (0.730-0.928)); and total peri-ictal symptoms self-reported on structured questionnaire (AUC = 0.864 (0.781-0.948)).

CONCLUSIONS

Our study does not find support for some clinical features previously reported as diagnostic of FDS in adult patients with a first presentation of TLoC. Features suggestive of preserved ictal responsiveness (reported by witnesses) and awareness (in the form of total number of self-reported peri-ictal symptoms) support FDS diagnoses.

Development of a diagnostic checklist to identify functional cognitive disorder versus other neurocognitive disorders

Background

Functional cognitive disorder (FCD) poses a diagnostic challenge due to its resemblance to other neurocognitive disorders and limited biomarker accuracy. We aimed to develop a new diagnostic checklist to identify FCD versus other neurocognitive disorders.

Methods

The clinical checklist was developed through mixed methods: (1) a literature review, (2) a three-round Delphi study with 45 clinicians from 12 countries and (3) a pilot discriminative accuracy study in consecutive patients attending seven memory services across the UK. Items gathering consensus were incorporated into a pilot checklist. Item redundancy was evaluated with phi coefficients. A briefer checklist was produced by removing items with >10% missing data. Internal validity was tested using Cronbach's alpha. Optimal cut-off scores were determined using receiver operating characteristic curve analysis.

Results

A full 11-item checklist and a 7-item briefer checklist were produced. Overall, 239 patients (143 FCD, 96 non-FCD diagnoses) were included. The checklist scores were significantly different across subgroups (FCD and other neurocognitive disorders) (F(2, 236)=313.3, p<0.001). The area under the curve was excellent for both the full checklist (0.97, 95% CI 0.95 to 0.99) and its brief version (0.96, 95% CI 0.93 to 0.98). Optimal cut-off scores corresponded to a specificity of 97% and positive predictive value of 91% for identifying FCD. Both versions showed good internal validity (>0.80).

Conclusions

This pilot study shows that a brief clinical checklist may serve as a quick complementary tool to differentiate patients with neurodegeneration from those with FCD. Prospective blind large-scale validation in diverse populations is warranted.Cite Now.

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.

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.

Influence of comorbidity on mortality in patients with epilepsy and psychogenic nonepileptic seizures

OBJECTIVE

This study aims to determine the contribution of comorbidities to excess psychogenic nonepileptic seizures (PNES) mortality.

METHODS

A retrospective cohort study was conducted of tertiary epilepsy outpatients from St. Vincent's Hospital Melbourne, Australia with an 8:1 comparison cohort, matched by age, sex, and socioeconomic status (SES) to national administrative databases between 2007 and 2017. Privacy-preserving data linkage was undertaken with the national prescription, National Death Index, and National Coronial Information System. Forty-five comorbid disease classes were derived by applying the Australian validated RxRisk-V to all dispensed prescriptions. We fitted Cox proportional hazard models controlling for age, sex, SES, comorbidity, disease duration, and number of concomitant antiseizure medications, as a marker of disease severity. We also performed a parallel forward-selection change in estimate strategy to explore which specific comorbidities contributed to the largest changes in the hazard ratio.

RESULTS

A total of 13 488 participants were followed for a median 3.2 years (interquartile range = 2.4-4.0 years), including 1628 tertiary epilepsy outpatients, 1384 patients with epilepsy, 176 with PNES, and 59 with both. Eighty-two percent of epileptic seizures and 92% of typical PNES events were captured in an epilepsy monitoring unit. The age-/sex-/SES-adjusted hazard ratio was elevated for epilepsy (4.74, 95% confidence interval [CI] = 3.36-6.68) and PNES (3.46, 95% CI = 1.38-8.68) and remained elevated for epilepsy (3.21, 95% CI = 2.22-4.63) but not PNES (2.15, 95% CI = .77-6.04) after comorbidity adjustment. PNES had more pre-existing comorbidities (p = .0007), with a three times greater median weighted Rx-RiskV score. Psychotic illness, opioid analgesia, malignancies, and nonopioid analgesia had the greatest influence on PNES comorbid risk.

SIGNIFICANCE

Higher comorbidity appears to explain the excess PNES mortality and may represent either a wider underrecognized somatoform disorder or a psychological response to physical illness. Better understanding and management of the bidirectional relationship of these wider somatic treatments in PNES could potentially reduce the risk of death.

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.

Diagnosis and management of functional neurological disorder

Functional neurological disorder (FND), previously regarded as a diagnosis of exclusion, is now a rule-in diagnosis with available treatments. This represents a major step toward destigmatizing the disorder, which was often doubted and deemed untreatable. FND is prevalent, generally affecting young and middle aged adults, and can cause severe disability in some individuals. An early diagnosis, with subsequent access to evidence based rehabilitative and/or psychological treatments, can promote recovery-albeit not all patients respond to currently available treatments. This review presents the latest advances in the use of validated rule-in examination signs to guide diagnosis, and the range of therapeutic approaches available to care for patients with FND. The article focuses on the two most frequently identified subtypes of FND: motor (weakness and/or movement disorders) and seizure type symptoms. Twenty two studies on motor and 27 studies on seizure type symptoms report high specificities of clinical signs (64-100%), and individual signs are reviewed. Rehabilitative interventions (physical and occupational therapy) are treatments of choice for functional motor symptoms, while psychotherapy is an emerging evidence based treatment across FND subtypes. The literature to date highlights heterogeneity in responses to treatment, underscoring that more research is needed to individualize treatments and develop novel interventions.

Assessing the hidden burden of psychiatric disease in patients with nonepileptic seizures

Nonepileptic seizures are commonly associated with psychiatric comorbidities, and specifically PTSD. Despite increased prevalence of psychiatric disease noted on referral of patients to our dedicated clinic for nonepileptic seizures, we found even higher rates of comorbid psychiatric disease or significant symptomatology after our initial clinic intakes, whereby patients are formally evaluated by a behavioral health provider, in addition to an epileptologist. After intake, an additional 21% of patients were identified as having PTSD or significant trauma-related symptoms, an additional 7% of patients were identified with significant anxiety or panic-related symptoms, and an additional 11% of patients were identified with significant depressive symptoms. While highly effective treatment of nonepileptic seizures remains elusive, well-developed treatment paradigms with proven efficacy exist for depression, anxiety, and PTSD. Eliciting these psychiatric comorbidities and pursuing targeted treatments, especially for those patients that do not have easy access to providers with dedicated expertise in the management of nonepileptic seizures, may be a more easily scalable and implementable treatment modality for these patients.