DDESVSFS: A simple, rapid and comprehensive screening tool for the Differential Diagnosis of Epileptic Seizures VS Functional Seizures

Testing the diagnostic accuracy of common questions for seizure diagnosis: Challenges and future directions

OBJECTIVE

The aim of this study was to evaluate the diagnostic accuracy of common interview questions used to distinguish a diagnosis of epilepsy from seizure mimics including non-epileptic seizures (NES), migraine, and syncope.

METHODS

200 outpatients were recruited with an established diagnosis of focal epilepsy (n = 50), NES (n = 50), migraine (n = 50), and syncope (n = 50). Patients completed an eight-item, yes-or-no online questionnaire about symptoms related to their events. Sensitivity and specificity were calculated. Using a weighted scoring for the questions alone with baseline characteristics, the overall questionnaire was tested for diagnostic accuracy.

RESULTS

Of individual questions, the most sensitive one asked if events are sudden in onset (98 % sensitive for epilepsy (95 % CI: 89 %, 100 %)). The least sensitive question asked if events are stereotyped (46 % sensitive for epilepsy (95 % CI: 32 %, 60 %)). Overall, three of the eight questions showed an association with epilepsy as opposed to mimics. These included questions about "sudden onset" (OR 10.76, 95 % CI: (1.66, 449.21) p = 0.0047), "duration < 5 min" (OR 3.34, 95 % CI: (1.62, 6.89), p = 0.0008), and "duration not > 30 min" (OR 4.44, 95 % CI: (1.94, 11.05), p = <0.0001). When individual seizure mimics were compared to epilepsy, differences in responses were most notable between the epilepsy and migraine patients. Syncope and NES were most similar in responses to epilepsy. The overall weighted questionnaire incorporating patient age and sex produced an area under the ROC curve of 0.80 (95 % CI: 0.74, 0.87)).

CONCLUSION

In this study, we examined the ability of common interview questions used by physicians to distinguish between epilepsy and prevalent epilepsy mimics, specifically NES, migraines, and syncope. Using a weighted scoring system for questions, and including age and sex, produced a sensitive and specific predictive model for the diagnosis of epilepsy. In contrast to many prior studies which evaluated either a large number of questions or used methods with difficult practical application, our study is unique in that we tested a small number of easy-to-understand "yes" or "no" questions that can be implemented in most clinical settings by non-specialists.

An update on psychogenic nonepileptic seizures

PURPOSE OF REVIEW

The understanding of psychogenic nonepileptic seizures (PNES) has advanced steadily over recent decades. This update summarizes new insights from the last three years.

RECENT FINDINGS

The process of diagnosing PNES has shifted from the exclusion of epilepsy to one based on the recognition of typical clinical features. While the diagnosis cannot rely on any single feature in isolation, a range of semiological features characterising PNES are now recognised and a number of studies hint at the potential for machine learning and AI to improve the diagnostic process. Advances in data processing and analysis may also help to make sense of the heterogeneity of PNES populations demonstrated by recent studies focussing on aetiology and patient subgroups. It is now clear that PNES are associated with high rates of mental and physical comorbidities and premature death, highlighting that they are only one manifestation of a complex disorder extending beyond the nervous system and the seizures themselves.

SUMMARY

PNES are now understood as a manifestation of dysfunction in interacting brain networks. This understanding provides an explanation for the psychopathological and semiological heterogeneity of PNES patient populations. New insights into medical comorbidities and increased rates of premature death call for more research into associated pathological processes outside the nervous system.

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.

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.

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.