Misdiagnosis in epilepsy: a review and recognition of diagnostic uncertainty

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review

Background

Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.

Methods

We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.

Results

We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.

Conclusion

The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.

Significance

We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

Prediction begins with diagnosis: Estimating seizure recurrence risk in the First Seizure Clinic

OBJECTIVES

To assess the feasibility of using a seizure recurrence prediction tool in a First Seizure Clinic, considering (1) the accuracy of initial clinical diagnoses and (2) performance of automated computational models in predicting seizure recurrence after first unprovoked seizure (FUS).

METHODS

To assess diagnostic accuracy, we analysed all sustained and revised diagnoses in patients seen at a First Seizure Clinic over 5 years with 6+ months follow-up ('accuracy cohort', n = 487). To estimate prediction of 12-month seizure recurrence after FUS, we used a logistic regression of clinical factors on a multicentre FUS cohort ('prediction cohort', n = 181), and compared performance to a recently published seizure recurrence model.

RESULTS

Initial diagnosis was sustained over 6+ months follow-up in 69% of patients in the 'accuracy cohort'. Misdiagnosis occurred in 5%, and determination of unclassified diagnosis in 9%. Progression to epilepsy occurred in 17%, either following FUS or initial acute symptomatic seizure. Within the 'prediction cohort' with FUS, 12-month seizure recurrence rate was 41% (95% CI [33.8%, 48.5%]). Nocturnal seizure, focal seizure semiology and developmental disability were predictive factors. Our model yielded an Area under the Receiver Operating Characteristic curve (AUC) of 0.60 (95% CI [0.59, 0.64]).

CONCLUSIONS

High clinical accuracy can be achieved at the initial visit to a First Seizure Clinic. This shows that diagnosis will not limit the application of seizure recurrence prediction tools in this context. However, based on the modest performance of currently available seizure recurrence prediction tools using clinical factors, we conclude that data beyond clinical factors alone will be needed to improve predictive performance.

Functional seizures and their mimics: a retrospective service review of cases from a tertiary video telemetry database

ABSTRACT

Objective

Identify the proportion of patients referred with putative functional seizures (FS) that were subsequently re-diagnosed as epileptic seizures (ES), or an alternative diagnosis, following video telemetry EEG (VTEEG). In addition, describe the characteristics of those seizures.

Methods

The VTEEG reports from patients admitted to the Chalfont Centre for Epilepsy between 2019 and 2022 were reviewed. Pre-VTEEG and post-VTEEG diagnoses were compared to identify whether a diagnostic revision was made from suspected FS to ES or another diagnosis. Diagnostic revision cases were then grouped into cohorts with associated features and reviewed to characterise and describe FS mimics.

Results

444 VTEEG reports where patients had habitual events were identified. 4.7% of patients were referred with FS and were subsequently diagnosed with ES or another diagnosis. In this group, several cohorts could be identified including frontal lobe epileptic seizures, ES with functional overlay, insular or temporal lobe epileptic seizures associated with autonomic or marked experiential peri-ictal symptoms, and individuals who had both ES and FS but whose ES were revealed on medication withdrawal.

Conclusion

In patients referred to a tertiary epilepsy unit, a small minority of cases had seizures diagnosed as functional and reclassified as epileptic or an alternative diagnosis. It is clinically important to be aware of these FS mimics.

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.

Approach to Patients with Seizures and Epilepsy: A Guide for Primary Care Physicians

Seizures and epilepsy are common neurologic conditions that are frequently encountered in the outpatient primary care setting. An accurate diagnosis relies on a thorough clinical history and evaluation. Understanding seizure semiology and classification is crucial in conducting the initial assessment. Knowledge of common seizure triggers and provoking factors can further guide diagnostic testing and initial management. The pharmacodynamic characteristics and side effect profiles of anti-seizure medications are important considerations when deciding treatment and counseling patients, particularly those with comorbidities and in special populations such as patient of childbearing potential.

Challenges and Prospects in Epilepsy Monitoring Units: A Comprehensive Review of Logistic Barriers

Epilepsy is one of the most common neurological diseases with a prevalence ranging from 0.5% to 2% in different sittings. The World Health Organization (WHO) estimated that nearly 80% of this burden is borne by resource-poor countries where even conventional electroencephalogram (EEG) coverage is dramatically short.  Video EEG monitoring applied for days as conducted in epilepsy monitoring units (EMUs) is aimed at seizure localization, anti-seizure medication (ASM) adjustment, or epilepsy surgery evaluation and planning. However, the EEG approach in EMUs has its obstacles. The present article is aimed to concentrate on the logistic challenges of EMUs, discussing existing data and limitations and offering suggestions for future planning to enhance the utilization of existing technology. Shortages of adult and pediatric epileptologists, qualified nurses, as well as EEG technologists have been reported in different countries. Moreover, injuries and falls, psychosis, status epilepticus, and unexpected death have been stated to be the most frequent safety issues in EMUs. Enhancements to mitigate logistical and healthcare system-related barriers in EMUs include the implementation of large cohort studies and the utilization of artificial intelligence (AI) for the identification and categorization of specific risks among EMU admissions. The establishment of EMUs and their associated challenges and barriers are best acknowledged through discussions and dialogue with various stakeholders.

Diagnostic utility of prolonged ambulatory video-electroencephalography monitoring

OBJECTIVES

Ambulatory video-electroencephalography (video-EEG) represents a low-cost, convenient and accessible alternative to inpatient video-EEG monitoring, however few studies have examined their diagnostic yield. In this large-scale retrospective study conducted in Australia, we evaluated the efficacy of prolonged ambulatory video-EEG recordings in capturing diagnostic events and resolving the referring question.

METHODS

Sequential adult and paediatric ambulatory video-EEG reports from April 2020 to June 2021 were reviewed retrospectively. Data collection included patient demographics, clinical information, and details of events and EEG abnormalities. Clinical utility was assessed by examining i) time to first diagnostic event, and ii) ability to resolve the referring questions - seizure localisation, quantification, classification, and differentiation (differentiating seizures from non-epileptic events).

RESULTS

Of the 600 reports analysed, 49 % captured at least one event, and 45 % captured interictal abnormalities (epileptiform or non-epileptiform). Seizures, probable psychogenic events (mostly non-convulsive), and other non-epileptic events occurred in 13 %, 23 % and 21 % of recordings respectively, with overlap. Unreported events were captured in 53 (9 %) recordings, and unreported seizures represented more than half of all seizures captured (51 %, 392/773). Nine percent of events were missing clinical, video or electrographic data. A diagnostic event occurred in 244 (41 %) recordings, of which 14 % were captured between the fifth and eighth day of recording. Reported event frequency ≥ 1/week was the only significant predictor of diagnostic event capture. In recordings with both seizures and psychogenic events, unrecognized seizures were frequent, and seizures may be missed if recording is terminated early. The referring question was resolved in 85 % of reports with at least one event, and 53 % of all reports. Specifically, this represented 46 % of reports (235/512) for differentiation of events, and 75 % of reports (27/36) for classification of seizures.

CONCLUSION

Ambulatory video-EEG recordings are of high diagnostic value in capturing clinically relevant events and resolving the referring clinical questions.

Yield of Sleep Deprivation EEG in Suspected Epilepsy. A Retrospective Study

Background. Sleep is an activation procedure and is considered the most potent and best-documented modulator of seizures and interictal epileptiform discharges (IEDs) on electroencephalogram (EEG). The precise role of sleep deprivation in the diagnostic process of epilepsy has not been fully clarified after more than 50 years of use. Sleep deprivation is a procedure that is accompanied by discomfort for patients and their families. Therefore, an accurate indication according to each patient-specific characteristic is needed. This study aims to assess the effectiveness of sleep deprivation EEG in the diagnostic process of patients with suspected epilepsy in our center. Methods. We included patients with a first unprovoked seizure and patients with paroxysmal events suspecting seizures who underwent a sleep deprivation EEG (sdEEG) or routine EEG (rEEG). All patients were subsequently classified with confirmed epilepsy or not. Results. We included 460 patients. The group with sdEEG consisted of 115 patients, while the group with rEEG comprised 345 patients. In the sdEEG group, 19 patients (17%) were confirmed with epilepsy, of which 17 presented interictal epileptiform discharges (IEDs). For the rEEG group, 66 patients (19%) were confirmed with epilepsy, of which 63 presented IEDs. The difference was not statistically significant. Conclusion. Our study failed to find a difference in the yield of sleep deprivation versus routine EEG in patients with epilepsy, but there are many significant confounders/sample biases that limit the generalizability of the findings, particularly to the majority of adult practices.

A Case of Focal Seizures Presented With Recurrent Sweating and Chills

Focal seizures, characterized by excessive electrical excitation in a brain region, present diagnostic challenges due to diverse manifestations, particularly with non-motor symptoms. Here, we present a 69-year-old Japanese woman experiencing unexplained recurrent episodes of sweating, chills, and shivering. Despite exhaustive investigations that identified no abnormalities, her symptoms remained unalleviated by symptomatic treatments. The episodic nature of her presentations subsequently prompted a clinical suspicion of seizures, leading to further neurological evaluations. Magnetic resonance imaging (MRI) of the brain and electroencephalography (EEG) revealed chronic ischemic changes in the cerebral white matter and intermittent sharp and slow wave bursts in the frontal regions. These findings led to a diagnosis of focal seizures manifesting as autonomic symptoms. The patient's symptoms were successfully treated with carbamazepine. This case illustrates the importance of considering non-motor focal seizures in patients with episodic symptoms, even when routine tests show no abnormalities.

Masquerading bundle branch block: an often missed electrophysiological event

Masquerading bundle branch block is an easily overlooked pattern on the ECG that indicates severe disease of the atrioventricular nodal conduction pathway. It is often caused by coronary artery disease, infiltrative diseases of the heart and idiopathic degeneration of the atrioventricular nodal conduction pathways. The diagnosis is easily missed as it needs a detailed interpretation of the ECG in addition to the clinical presentation of the patient. The presence of this specific bundle branch block pattern on the ECG indicates severe degeneration of the conduction system requiring intervention. Given its rarity, this clinical entity risks misdiagnosis and inappropriate management. This case highlights two diagnostic challenges for clinicians: the rarely described masquerading bundle branch block and the art of clinically differentiating between epilepsy and convulsive syncope.

Pitfalls in scalp EEG: Current obstacles and future directions

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders

Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

Impact of ambulatory EEG in the management of patients with epilepsy in resource-limited Latin American populations

Objective

Ambulatory electroencephalography (AEEG) monitoring allows for prolonged recordings in normal environments, such as patients' homes, and is recognized as a cost-effective alternative to inpatient long-term video-EEG primarily in resource-limited countries. We aim to describe the impact of AEEG on the assessment of patients with suspected or confirmed epilepsy in two independent Latin-American populations with limited resources.

Methods

We included 63 patients who had undergone an AEEG due to confirmed/suspected epilepsy. Clinical (demographic, current antiseizure medication and indication) and electroencephalographic (duration of the study, result, and impact on clinical decision-making) were reviewed and compared.

Results

The main indication for an AEEG was the differentiation of seizures from non-epileptic events with 57% of patients. It was categorized as positive in 36 patients and did have an impact on the clinical decision-making process in 57% of patients. AEEG captured clinical events in 35 patients (20 epileptic and 15 non-epileptic).

Conclusions

AEEG proves to be a valuable tool in resource-limited settings for assessing suspected or confirmed epilepsy cases, with a significant impact on clinical decisions.

Significance

Our study provides valuable insights into the use of AEEG in under-resourced regions, shedding light on the challenges and potential benefits of this tool in clinical practice.

Teaching distinguishing semiological features improves diagnostic accuracy of seizure-like events by emergency physicians

Background

Misdiagnosis of seizure-like events (SLE) in emergency situations is common. Here, we evaluate whether a single, video-based lesson highlighting distinguishing semiological features can improve the diagnostic accuracy of emergency physicians for epileptic seizures (ES), psychogenic non-epileptic seizures (PNES) and syncopes (SY).

Methods

40 emergency physicians (24 anesthetists, nine surgeons and seven internal medicine specialists by primary specialty) participated in a prospective trial on the diagnostic accuracy of SLE. They assessed video-displayed SLE at two time points: before and after a lecture on distinguishing semiological features. In the lecture, semiological features were demonstrated using patient videos, some were acted by the instructor in addition. The increase in correct diagnoses and recognition of distinguishing semiological features were analyzed.

Results

Before the lesson, 45% of 200 SLE-ratings were correct: 15% of SY (n = 40), 30% of PNES (n = 40), 59% of ES (n = 120, focal to bilateral tonic–clonic seizures (FBTCS) 87.5% (n = 40), focal impaired aware seizures (FIAS) 45% (n = 80)). Semiology teaching increased both the rate of correct diagnoses of SLE to overall 79% (p < 0.001) (ES 91% (p < 0.001), FBCTS 98% (n.s.), FIAS 88% (p < 0.001), PNES 88% (p < 0.001), SY 35% (p < 0.001)), and the number of recognized distinguishing semiological features. We identified several semiological features with high entity specific positive predictive values (> 0.8).

Conclusions

A single 45-min video-based lesson highlighting distinguishing semiological features improves the diagnostic accuracy of ES, PNES and SY by emergency physicians. We expect that including this aspect into the curriculum of emergency physicians will lead to better individual patient treatment in pre-hospital medicine and more appropriate subsequent use of clinical resources.

Psycho-socio-clinical profiles and quality of life in seizure disorders: A cross-sectional registry study

OBJECTIVE

This prospective study aimed at comparing quality of life (QoL) and psycho-socio-clinical profiles between patients with epilepsy, psychogenic nonepileptic seizures (PNES), and syncope. We also intended to identify predictors of QoL in these three seizure disorders.

METHODS

A total of 245 inpatients (epilepsy n = 182, PNES n = 50, syncope n = 13) from a tertiary epilepsy clinic were included. Information on QoL as well as on psychological, sociodemographic, and clinical profiles was retrieved using questionnaires and medical records. Group comparisons on QoL and psycho-socio-clinical profiles were performed via analyses of variance, chi-square tests, and related post hoc tests. Predictors of QoL in epilepsy and PNES were determined using general linear modeling, which was not possible for syncope due to a small sample size.

RESULTS

Patients with epilepsy, PNES, and syncope reported levels of QoL impairment that did not differ significantly between groups (p = 0.266). However, there were significant group differences regarding sex distribution (p < 0.001), seizure disorder duration (p = 0.004), seizure frequency (p = 0.019), current treatment with antiseizure medications (ASM) (p < 0.001), number of current ASM (p < 0.001), and adverse ASM events (p = 0.019). More depressive symptoms (p = 0.001), more adverse ASM events (p = 0.036), and unemployment (p = 0.046) (in this order) independently predicted a diminished QoL in epilepsy. For PNES, more depressive symptoms were the only independent predictor of lower QoL (p = 0.029).

CONCLUSIONS

Patients with epilepsy, PNES, and syncope experience similarly diminished QoL and show a general psycho-socio-clinical burden with a specific pattern for each seizure disorder diagnosis. Although clinical aspects play an undisputed role for QoL in epilepsy, the psychosocial aspects and consequences are equally, or for PNES probably even more, meaningful. A comprehensive approach to research and treatment of seizure disorders seems mandatory to increase QoL for these patients. More research on QoL in syncope is needed.

A retrospective study of the correlation between duration of monitoring in the epilepsy monitoring unit and diagnostic yield

OBJECTIVE

Long-term video-electroencephalographic (LTVEM) monitoring is a valuable tool in the evaluation of paroxysmal clinical events. However, vEEG itself is costly. Hence, we aimed to establish if longer duration of monitoring (DOM) is associated with higher diagnostic yield.

METHOD

A retrospective review of patients admitted into the epilepsy monitoring unit (EMU) for the diagnostic evaluation of paroxysmal events was performed. Patients' demographic, clinical characteristics, and vEEG data were analyzed. In the cohort of patients with DOM > 7 days, the reasons for prolonged DOM were identified and the differences in clinical characteristics and vEEG data between conclusive and inconclusive studies were analyzed.

RESULT

A total of 501 patients were included. Four hundred and thirty-six (87 %) patients had conclusive studies. Of these patients, 67.9 % patients with conclusive studies received diagnosis within the first 7 days of monitoring with the highest on day 7. The likelihood of conclusive studies decreased beyond 7 days. A total of 175 had DOM > 7 days, of which 140 (80 %) had conclusive studies. In the cohort with DOM > 7 days, patients with previous abnormal routine EEG, previous vEEG monitoring, first event recorded before day 5 of admission and ≥1 events recorded during vEEG monitoring were more likely to have conclusive studies. The most common reason for prolonging DOM beyond 7 days was to adequately record multiple semiologically distinctive events (76 %).

CONCLUSION

Our study supports that longer DOM is associated with an increase in diagnostic yield. More than one-third of our cohort were monitored beyond 7 days with majority (80 %) being conclusive. Our findings may guide clinicians in planning the DOM and predicting the likelihood of conclusive vEEG studies in patients with prolonged DOM based on the clinical characteristics and vEEG data.

A world away and here at home: a prioritisation framework for US international patient programmes

Programmes serving international patients are increasingly common throughout the USA. These programmes aim to expand access to resources and clinical expertise not readily available in the requesting patients' home country. However, they exist within the US healthcare system where domestic healthcare needs are unmet for many children. Focusing our analysis on US children's hospitals that have a societal mandate to provide medical care to a defined geographic population while simultaneously offering highly specialised healthcare services for the general population, we assume that, given their mandate, priority will be given to patients within their catchment area over other patients. We argue that beyond prioritising patients within their region and addressing inequities within US healthcare, US institutions should also provide care to children from countries where access to vital medical services is unavailable or deficient. In the paper, we raise and attempt to answer the following: (1) Do paediatric healthcare institutions have a duty to care for all children in need irrespective of their place of residence, including international patients? (2) If there is such a duty, how should this general duty be balanced against the special duty to serve children within a defined geographical area to which an institution is committed, when resources are strained? (3) Finally, how are institutional obligations manifest in paradigm cases involving international patients? We start with cases, evaluating clinical and contextual features as they inform the strength of ethical claim and priority for access. We then proceed to develop a general prioritisation framework based on them.

Increased suicide attempt risk in people with epilepsy in the presence of concurrent psychogenic nonepileptic seizures

OBJECTIVES

To test the hypothesis that people with concurrent diagnosis of epilepsy and psychogenic nonepileptic seizures (PNES) are at increased risk of attempting suicide as compared to people with epilepsy or PNES alone. To report on suicide rates.

METHODS

Retrospective cohort study from the UK largest tertiary mental health care provider, with linked nationwide admission and mortality data from the Hospital Episode Statistics and Office for National Statistics. Participants were 2460 people with a primary or secondary diagnosis of epilepsy, PNES or concurrent epilepsy and PNES attending between 1 January 2007 and 18 June 2021. The primary outcome was a first hospital admission for suicide attempt (International Classification of Diseases, version 10 X60-X84).

RESULTS

9% of participants had at least one suicide attempt-related hospital admission. For people with concurrent diagnosis of epilepsy and PNES, the odds for suicide attempt-related admissions were 2.52 times the odds of people with epilepsy alone (OR 0.40; 95% CI 0.21 to 0.79; p=0.01). Odds were comparable between people with concurrent diagnosis and people with PNES alone (OR 0.75; 95% CI 0.41 to 1.48; p=0.40). Post hoc analyses revealed that the odds of people with PNES alone were 1.93 times the odds of people with epilepsy alone (OR 0.52; 95% CI 0.38 to 0.70; p<0.001).

CONCLUSIONS

People with concurrent diagnosis of epilepsy and PNES or PNES alone have significantly increased odds of hospitalisation due to suicide attempt as compared to people with epilepsy alone (152% and 93% increase, respectively). These findings have direct implications for the clinical management of suicide risk in people with epilepsy.

Developing and validating of the Clinical Uncertainty Measurement Questionnaire (CUMQ) among practicing physicians and clinical residents in Iran

BACKGROUND

Despite the fact that clinicians face uncertainty in their decisions, there is no comprehensive framework to measure it in medical practices which is the knowledge gap especially for Iran. Therefore, this study aimed to evaluate the reliability and validity of a Persian questionnaire which is designed to measure different determining aspects of uncertainty from clinical physicians' perspectives in Iran.

METHODS

Clinical Uncertainty Measurement Questionnaire (CUMQ) has been derived from a mixed method study since March 2019 to January 2021. To exclude raw items of the questionnaire, the literature was reviewed and in-depthinterviews were implemented with 24 residents,specialists and sub-specialists in all major clinical fields which resulted in the first theoretical uncertainty in clinical decision making framework. CUMQ content validity has been evaluated using content validity index (CVI) and content validity ratio (CVR). The structural validity of the questionnaire was assessed using confirmatory factor analysis and factor loading and t-value for each indicator of uncertainty is reported. Moreover, to analyze the research model we used the Partial Least Squares (PLS) technique using the SmartPLS software. Convergent (using Average Variance Extracted (AVEs) for each latent variable) and discriminant validity (using the criteria of Fornell and Larckerand cross loading) of the model was also evaluated. After that, the quality of the model was evaluated adjustment through predictive validity (Q²) and effect size (f²). In addition, the reliability was also assessed using Cronbach's alpha and composite reliability.

RESULTS

The CVR and CVI ranged from 0. 80 to 1. 00 which illustrates high content validity. Out of 30 items, 24 items had acceptable factor loading and remained in the questionnaire which have been categorized as five main clinical uncertainty dimensions; general determinants, individual determinants of the physician, individual determinants of patient, dynamics of medical sciences, diagnostic and instrumental limitations. The value of composite reliability and Cronbach's alpha for all dimensions were above the threshold value of 0. 7 and the reliability has been confirmed. As AVE values were greater than 0. 5, convergent validity is confirmed. The result of Fornell-Larcker and cross-loadings also indicated that discriminant validity is well established.

CONCLUSION

This CUMQ is as avalid and reliable instrument and a suitable tool to measure clinical uncertainty in the Iranian Medical community. However, the reliability of this questionnaire can be studied in other languages and in other countries.

Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review

In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long-term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state-of-the-art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty-three full-text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic-detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure-detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.

Evaluation of First Seizure and Newly Diagnosed Epilepsy

PURPOSE OF REVIEW

This article focuses on the evaluation of children and adults who present with new-onset seizures, with an emphasis on differential diagnosis, classification, evaluation, and management.

RECENT FINDINGS

New-onset seizures are a common presentation in neurologic practice, affecting approximately 8% to 10% of the population. Accurate diagnosis relies on a careful history to exclude nonepileptic paroxysmal events. A new classification system was accepted in 2017 by the International League Against Epilepsy, which evaluates seizure type(s), epilepsy type, epilepsy syndrome, etiology, and comorbidities. Accurate classification informs the choice of investigations, treatment, and prognosis. Guidelines for neuroimaging and laboratory and genetic testing are summarized.

SUMMARY

Accurate diagnosis and classification of first seizures and new-onset epilepsy are key to choosing optimal therapy to maximize seizure control and minimize comorbidities.

Prehospital capillary lactate in children differentiates epileptic seizure from febrile seizure, syncope, and psychogenic nonepileptic seizure

PURPOSE

The aim of the study was to examine prehospital capillary lactate in children as a diagnostic biomarker to differentiate epileptic seizures from febrile seizures, syncope, and psychogenic nonepileptic seizures (PNES).

METHODS

Capillary lactate concentrations taken in a pediatric prehospital setting within 2 h of the paroxysmal event were compared retrospectively between patients with epileptic seizure, febrile seizure, syncope, and PNES, based on the final diagnosis from the hospitalization report.

RESULTS

One hundred and two patients were included, 53 (52%) with epileptic seizures, 41 (40%) with febrile seizures, and 8 (8%) with syncope or PNES. Capillary lactate in patients with a final diagnosis of epileptic seizure was significantly increased in comparison to the concentrations in patients with febrile seizure (p < 0.0007) and in comparison to the concentrations in patients with syncope or PNES (p < 0.0204). The area under the ROC-curve was 0.71 (95% CI 0.61-0.80). For a cutoff concentration of prehospital capillary lactate >3.9 mmol/l (Youden index), the sensitivity was 49% and the specificity 92%.

CONCLUSION

Prehospital capillary lactate concentrations are a useful tool for differentiating the nature of a paroxysmal event in children.

Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility.

Minimum standards for inpatient long-term video-electroencephalographic monitoring: A clinical practice guideline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and to establish its clinical utility.

A web-based algorithm to rapidly classify seizures for the purpose of drug selection

OBJECTIVE

To develop and validate a pragmatic algorithm that classifies seizure types, to facilitate therapeutic decision-making.

METHODS

Using a modified Delphi method, five experts developed a pragmatic classification of nine types of epileptic seizures or combinations of seizures that influence choice of medication, and constructed a simple algorithm, freely available on the internet. The algorithm consists of seven questions applicable to patients with seizure onset at the age of 10 years or older. Questions to screen for nonepileptic attacks were added. Junior physicians, nurses, and physician assistants applied the algorithm to consecutive patients in a multicenter prospective validation study (ClinicalTrials.gov identifier: NCT03796520). The reference standard was the seizure classification by expert epileptologists, based on all available data, including electroencephalogram (EEG), video-EEG monitoring, and neuroimaging. In addition, physicians working in underserved areas assessed the feasibility of using the web-based algorithm in their clinical setting.

RESULTS

A total of 262 patients were assessed, of whom 157 had focal, 51 had generalized, and 10 had unknown onset epileptic seizures, and 44 had nonepileptic paroxysmal events. Agreement between the algorithm and the expert classification was 83.2% (95% confidence interval = 78.6%-87.8%), with an agreement coefficient (AC1) of .82 (95% confidence interval = .77-.87), indicating almost perfect agreement. Thirty-two health care professionals from 14 countries evaluated the feasibility of the web-based algorithm in their clinical setting, and found it applicable and useful for their practice (median = 6.5 on 7-point Likert scale).

SIGNIFICANCE

The web-based algorithm provides an accurate classification of seizure types, which can be used for selecting antiseizure medications in adolescents and adults.

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.

Psychogenic non-epileptic seizures in children

Psychogenic Non-Epileptic Seizures (PNES) are a relatively common condition in children. While their clinical presentation resembles epileptic seizures, the underlying cause for PNES involves a multitude of bio-psychosocial factors. Patients may be misdiagnosed with epilepsy and subjected to unnecessary treatments, often delaying the diagnosis for years. A strong understanding of its symptomatology is essential for diagnosis of PNES. Successful management depends on effective teamwork that involves the neurologist as well as mental health professionals. This paper reviews the various aspects of PNES in children with emphasis on the clinical presentation, diagnosis as well as the underlying psychological basis and treatment.

Missed, mistaken, stalled: Identifying components of delay to diagnosis in epilepsy

A substantial proportion of individuals with newly diagnosed epilepsy report prior seizures, suggesting a missed opportunity for early epilepsy care and management. Consideration of the causes and outcomes of diagnostic delay is needed to address this issue. We aimed to review the literature pertaining to delay to diagnosis of epilepsy, describing the components, characteristics, and risk factors for delay. We undertook a systematic search of the literature for full-length original research papers with a focus on diagnostic delay or seizures before diagnosis, published 1998-2020. Findings were collated, and a narrative review was undertaken. Seventeen papers met the inclusion criteria. Studies utilized two measures of diagnostic delay: seizures before diagnosis and/or a study-defined time between first seizure and presentation/diagnosis. The proportion of patients with diagnostic delay ranged from 16% to 77%; 75% of studies reported 38% or more to be affected. Delays of 1 year or more were reported in 13%-16% of patients. Seizures prior to diagnosis were predominantly nonconvulsive, and usually more than one seizure was reported. Prior seizures were often missed or mistaken for symptoms of other conditions. Key delays in the progression to specialist review and diagnosis were (1) "decision delay" (the patient's decision to seek/not seek medical review), (2) "referral delay" (delay by primary care/emergency physician referring to specialist), and (3) "attendance delay" (delay in attending specialist review). There were few data available relevant to risk factors and virtually none relevant to outcomes of diagnostic delay. This review found that diagnostic delay consists of several components, and progression to diagnosis can stall at several points. There is limited information relating to most aspects of delay apart from prevalence and seizure types. Risk factors and outcomes may differ according to delay characteristics and for each of the key delays, and recommendations for future research include examining each before consideration of interventions is made.

Relationship between Tumor Necrosis Factor-Alpha and Neuropeptide Y Expression and Neurological Function Score in Epileptic Children

Background

To observe the relationship between Tumor Necrosis Factor-alpha (TNF-α) and Neuropeptide Y (NPY) expression and neurological function score in epileptic children.

Methods

Fifty-four epileptic children diagnosed and treated in Xuzhou Children's Hospital, China from Feb 2017 to Mar 2018 were collected and included in a research group (RG), while 30 healthy children who underwent physical examination at the same time were included in the control group (CG). ELISA was used to detect the expression of TNF-α and NPY in the serum of children in the two groups, and those before treatment were compared. The National Institute of Health stroke scale (NIHSS) and Hamilton Anxiety (HAMA) scores before and after treatment were observed, and Pearson correlation was used to analyze the relationship between the expression levels of TNF-α and NPY in the serum as well as NIHSS and HAMA scores.

Results

The expression levels of TNF-α and NPY in the serum of children in the RG were significantly higher than those in the CG (P<0.001). The expression level of TNF-α was positively correlated with the NIHSS and HAMA scores (r=0.748, P<0.001) (r=0.772, P<0.001). The expression level of NPY was positively correlated with the NIHSS and HAMA scores (r=0.768, P<0.001) (r=0.643, P<0.001).

Conclusion

TNF-α and NPY are highly expressed in epileptic children and are positively correlated with neurological function score.

When should we obtain a routine EEG while managing people with epilepsy?

More than eight decades after its discovery, routine electroencephalogram (EEG) remains a safe, noninvasive, inexpensive, bedside test of neurological function. Knowing when a routine EEG should be obtained while managing people with epilepsy is a critical aspect of optimal care. Despite advances in neuroimaging techniques that aid diagnosis of structural lesions in the central nervous system, EEG continues to provide critical diagnostic evidence with implications on treatment. A routine EEG performed after a first unprovoked seizure can support a clinical diagnosis of epilepsy and differentiate those without epilepsy, classify an epilepsy syndrome to impart prognosis, and characterize seizures for antiseizure management. Despite a current viral pandemic, EEG services continue, and the value of routine EEG is unchanged.

MiR-194-5p serves as a potential biomarker and regulates the proliferation and apoptosis of hippocampus neuron in children with temporal lobe epilepsy

BACKGROUND

The aim of the present study is to explore the expression level and the clinical significance of miR-194-5p to the children with temporal lobe epilepsy, and investigate its functions in regulating cell behaviors of hippocampal neurons.

METHODS

The expression level of miR-194-5p was detected in the serum of 59 temporal lobe epilepsy (TLE) children and 63 healthy children. To further study the role of miR-194-5p in the development of TLE in children, the epileptiform discharge model was established in rat hippocampal neurons to mimic TLE conditions in children. Receiver operator characteristic (ROC) curves and area under the ROC curve were established to evaluate the diagnostic value of serum microRNAs to the differentiation of the TLE group and healthy group. The influence of miR-194-5p on the proliferation and apoptosis of hippocampus neurons was examined by using MTT and flow cytometric apoptosis assay. Luciferase reporter assay was performed to confirm the target gene of miR-194-5p.

RESULTS

The result demonstrated that miR-194-5p was significantly dysregulated in plasma of TLE patients. Analysis of ROCs showed that the miR-194-5p had high specificity and sensitivity in the diagnosis of the TLE in children. The expression of miR-194-5p was found to increase in the hippocampal cells cultured in the magnesium-free medium through quantitative real-time polymerase chain reaction. Hyper-expressed of miR-194-5p reversed TLE-induced reduction for the cell viability, and inhibited the cell apoptosis induced by TLE. Insulin-like growth factor 1 receptor (IGF1R) was proved to be a direct target gene of miR-194-5p.

CONCLUSION

MiR-194-5p is a likely potential biomarker and treatment target of TLE in children. IGF1R might be involved in the regulatory role of miR-194-5p in hippocampus neuron apoptosis.

Experimental Therapeutic Strategies in Epilepsies Using Anti-Seizure Medications

Epilepsies are among the most common neurological problems. The disease burden in patients with epilepsy is significantly high, and epilepsy has a huge negative impact on patients' quality of life with epilepsy and their families. Anti-seizure medications are the mainstay treatment in patients with epilepsy, and around 70% of patients will ultimately control with a combination of at least two appropriately selected anti-seizure medications. However, in one-third of patients, seizures are resistant to drugs, and other measures will be needed. The primary goal in using experimental therapeutic medication strategies in patients with epilepsy is to prevent recurrent seizures and reduce the rate of traumatic events that may occur during seizures. So far, various treatments using medications have been offered for patients with epilepsies, which have been classified according to the type of epilepsy, the effectiveness of the medications, and the adverse effects. Medications such as Levetiracetam, valproic acid, and lamotrigine are at the forefront of these patients' treatment. Epilepsy surgery, neuro-stimulation, and the ketogenic diet are the main measures in patients with medication-resistant epilepsies. In this paper, we will review the therapeutic approach using anti-seizure medications in patients with epilepsy. However, it should be noted that some of these patients still do not respond to existing treatments; therefore, the limited ability of current therapies has fueled research efforts for the development of novel treatment strategies. Thus, it seems that in addition to surgical measures, we should look for more specific agents that have less adverse events and have a greater effect in stopping seizures.

Discovering themes in medical records of patients with psychogenic non-epileptic seizures

Introduction

Epileptic and psychogenic non-epileptic seizures (PNES) are common diagnostic problems encountered in hospital practice. This study explores the use of unsupervised machine learning in discovering themes in medical records of patients presenting with PNES. We hypothesised that themes generated by machine learning are comparable with the classification by human experts.

Methods

This is a retrospective analysis of the medical records in the emergency department of patients (age >18 years) with PNES who underwent inpatient video-electroencephalography monitoring from May 2009 to June 2014 and received a final diagnosis of PNES. Prior to machine learning of written text, we applied a standardised approach in natural language processing to create a document-term matrix (removal of numbers, stop-words and punctuations, transforming fonts to lower case). The words were separated into tokens and treated as if existing within a bag-of-words. A probability of each word existing within a topic (theme) was modelled on multivariate Dirichlet distribution (R Foundation, V.3.5.0). Next, we asked four experts to independently provide a clinical interpretation of the generated topics. When the majority of (≥3) experts agreed, it was regarded as highly congruent. Interactive data are available on the web at (https://gntem2.github.io/PNES/%23topic=1&lambda=0.6&term=).

Results

There were 39 patients (74.4% women, median age 35 years with range 20-82). A total of 121 documents were converted to text files for text mining. There were 15 generated topics with 12/15 topics rated as highly congruent. The main themes were about descriptors of seizures and medication use.

Conclusions

The findings from machine learning on PNES-related documentation provides evidence for the feasibility of applying machine-learning methodology to analyse large volumes of medical records. The topics generated by machine learning were congruent with interpretations by clinicians indicating this method can be used for screening of medical conditions among large volumes of medical records.

Development and validation of a screening questionnaire for psychogenic nonepileptic seizures

OBJECTIVE

Epilepsy and psychogenic nonepileptic seizures (PNES) are serious conditions, associated with substantial morbidity and mortality. Although prompt diagnosis is essential, these conditions are frequently misdiagnosed, delaying appropriate treatment. We developed and validated the Anxiety, Abuse, and Somatization Questionnaire (AASQ), a quick and clinically practical tool to differentiate PNES from epilepsy.

METHOD

We retrospectively identified psychological variables that differentiated epilepsy from PNES in a discovery cohort of patients admitted to a video-electroencephalography monitoring (VEM) unit from 2002 to 2017. From these findings, we developed the AASQ and prospectively validated it in an independent cohort of patients with gold-standard VEM diagnosis.

RESULTS

One thousand two hundred ninety-one patients were included in the retrospective study; mean age was 39.5 years (range: 18-99), 58% were female, 67% had epilepsy, and 33% had PNES. Psychometric data for 192 instrument items were reviewed, receiver operating characteristic curves were computed, and a 20-item AASQ was created. Prospective validation in 74 patients showed that a one-point increase in the AASQ score was associated with 11 times increase in the odds of having PNES compared with epilepsy. Low scores on the AASQ were associated with a low probability of PNES with a negative predictive value of 95%.

SIGNIFICANCE

The AASQ is quick, inexpensive, and clinically useful for workup of seizure disorders. The AASQ excludes PNES with a high degree of confidence and can predict PNES with significance when combined with basic clinicodemographic variables. Future research will investigate diagnostic performance of the AASQ in relevant clinical subgroups, such as patients with comorbid epilepsy and PNES.

Can seizure-related heart rate differentiate epileptic from psychogenic nonepileptic seizures?

OBJECTIVES

We aimed to (1) determine if seizure-related heart rate (HR) differentiates epileptic seizures (ES) from psychogenic nonepileptic seizures (PNES); (2) define the most useful point of the following HR measurements: preictal, ictal-onset, maximal-ictal, or postictal; and (3) delineate the optimal HR cutoff points (absolute HR and relative HR increase) to differentiate ES from PNES.

METHODS

All video-electroencephalography (VEEG) recorded at an Australian tertiary hospital from May 2009 to November 2015 were retrospectively reviewed. Baseline (during rest and wakefulness), 1-min preictal, ictal-onset, maximal-ictal, and 1-min postictal HR were measured for each ES and PNES event. Events lasting <10 s or with uninterpretable electrocardiogram (ECG) due to artifacts were excluded. Receiver operating characteristic curve analysis was performed to assess the diagnostic accuracy of HR reflected by the area under the curve (AUC).

RESULTS

Video-electroencephalography of 341 ES and 265 PNES from 130 patients were analyzed. The AUC for preictal, ictal-onset, maximal-ictal, and postictal HR were found to have poor differentiation between all types of ES and PNES. However, comparing bilateral tonic-clonic ES and PNES, AUC for absolute maximal-ictal HR was 0.84 (95% confidence interval [CI]: 0.73-0.95) and for absolute postictal HR was 0.90 (95% CI: 0.81-1.00) indicating good diagnostic discrimination. Using Youden's index to diagnose tonic-clonic ES, the optimal cutoff point for absolute maximal-ictal HR was 114 bpm (sensitivity: 84%, specificity: 82%) and for absolute postictal HR was 90 bpm (sensitivity: 91%, specificity: 82%).

CONCLUSION

These findings suggest that seizure-related HR is useful in differentiating bilateral tonic-clonic ES from PNES. Based on the AUC, the best diagnostic measurements are maximal-ictal and postictal HR.

Diverse Applications of Artificial Intelligence in Neuroradiology

Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."

Subspecialty-Level Deep Gray Matter Differential Diagnoses with Deep Learning and Bayesian Networks on Clinical Brain MRI: A Pilot Study

Purpose

To develop and validate a system that could perform automated diagnosis of common and rare neurologic diseases involving deep gray matter on clinical brain MRI studies.

Materials and Methods

In this retrospective study, multimodal brain MRI scans from 212 patients (mean age, 55 years ± 17 [standard deviation]; 113 women) with 35 neurologic diseases and normal brain MRI scans obtained between January 2008 and January 2018 were included (110 patients in the training set, 102 patients in the test set). MRI scans from 178 patients (mean age, 48 years ± 17; 106 women) were used to supplement training of the neural networks. Three-dimensional convolutional neural networks and atlas-based image processing were used for extraction of 11 imaging features. Expert-derived Bayesian networks incorporating domain knowledge were used for differential diagnosis generation. The performance of the artificial intelligence (AI) system was assessed by comparing diagnostic accuracy with that of radiologists of varying levels of specialization by using the generalized estimating equation with robust variance estimator for the top three differential diagnoses (T3DDx) and the correct top diagnosis (TDx), as well as with receiver operating characteristic analyses.

Results

In the held-out test set, the imaging pipeline detected 11 key features on brain MRI scans with 89% accuracy (sensitivity, 81%; specificity, 95%) relative to academic neuroradiologists. The Bayesian network, integrating imaging features with clinical information, had an accuracy of 85% for T3DDx and 64% for TDx, which was better than that of radiology residents (n = 4; 56% for T3DDx, 36% for TDx; P < .001 for both) and general radiologists (n = 2; 53% for T3DDx, 31% for TDx; P < .001 for both). The accuracy of the Bayesian network was better than that of neuroradiology fellows (n = 2) for T3DDx (72%; P = .003) but not for TDx (59%; P = .19) and was not different from that of academic neuroradiologists (n = 2; 84% T3DDx, 65% TDx; P > .09 for both).

Conclusion

A hybrid AI system was developed that simultaneously provides a quantitative assessment of disease burden, explainable intermediate imaging features, and a probabilistic differential diagnosis that performed at the level of academic neuroradiologists. This type of approach has the potential to improve clinical decision making for common and rare diseases.Supplemental material is available for this article.© RSNA, 2020.

The first-line management of psychogenic non-epileptic seizures (PNES) in adults in the emergency: a practical approach

Distinguishing non-epileptic events, especially psychogenic non-epileptic seizures (PNES), from epileptic seizures (ES) constitutes a diagnostic challenge. Misdiagnoses are frequent, especially when video-EEG recording, the gold-standard for PNES confirmation, cannot be completed. The issue is further complicated in cases of combined PNES with ES. In emergency units, a misdiagnosis can lead to extreme antiepileptic drug escalade, unnecessary resuscitation measures (intubation, catheterization, etc.), as well as needless biologic and imaging investigations. Outside of the acute window, an incorrect diagnosis can lead to prolonged hospitalization or increase of unhelpful antiepileptic drug therapy. Early recognition is thus desirable to initiate adequate treatment and improve prognosis. Considering experience-based strategies and a thorough review of the literature, we aimed to present the main clinical clues for physicians facing PNES in non-specialized units, before management is transferred to epileptologists and neuropsychiatrists. In such conditions, patient recall or witness-report provide the first orientation for the diagnosis, recognizing that collected information may be inaccurate. Thorough analysis of an event (live or based on home-video) may lead to a clinical diagnosis of PNES with a high confidence level. Indeed, a fluctuating course, crying with gestures of frustration, pelvic thrusting, eye closure during the episode, and the absence of postictal confusion and/or amnesia are highly suggestive of PNES. Moreover, induction and/or inhibition tests of PNES have a good diagnostic value when positive. Prolactinemia may also be a useful biomarker to distinguish PNES from epileptic seizures, especially following bilateral tonic-clonic seizures. Finally, regardless the level of certainty in the diagnosis of the PNES, it is important to subsequently refer the patient for epileptological and neuropsychiatric follow-up.

Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised

Current explanatory concepts suggest seizures emerge from ongoing dynamics of brain networks. It is unclear how brain network properties determine focal or generalised seizure onset, or how network properties can be described in a clinically-useful manner. Understanding network properties would cast light on seizure-generating mechanisms and allow to quantify to which extent a seizure is focal or generalised. Functional brain networks were estimated in segments of scalp-EEG without interictal discharges (68 people with epilepsy, 38 controls). Simplified brain dynamics were simulated using a computer model. We introduce: Critical Coupling (C_c), the ability of a network to generate seizures; Onset Index (OI), the tendency of a region to generate seizures; and Participation Index (PI), the tendency of a region to become involved in seizures. C_c was lower in both patient groups compared with controls. OI and PI were more variable in focal-onset than generalised-onset cases. In focal cases, the regions with highest OI and PI corresponded to the side of seizure onset. Properties of interictal functional networks from scalp EEG can be estimated using a computer model and used to predict seizure likelihood and onset patterns. This may offer potential to enhance diagnosis through quantification of seizure type using inter-ictal recordings.

[Sudden unexpected death in epilepsy (SUDEP) : Epidemiology, cardiac and other risk factors]

Sudden unexpected death in epilepsy (SUDEP) is one of the most frequent epilepsy-related causes of death. The incidence of SUDEP is estimated to be approximately 1.2/1000 person-years (PY); however, it varies considerably depending on disease-specific and demographic factors. The estimated incidence of SUDEP in children seems to be significantly lower (0.22/1000 PY) than in adults but recent studies in children (>12 years) indicated a similar incidence to that of adults. Based on these estimations, approximately 700 SUDEP cases would be expected in Germany annually but no reliable data or epidemiological studies on SUDEP are available. Various risk factors and predictors for SUDEP have been investigated, e.g. age, seizure frequency, number of antiepileptic drugs, non-compliance and comorbidities, with sometimes contradictory results. This is understandable given that the exact mechanisms of SUDEP are unclear; however, it is very likely that the frequency of (nocturnal) generalized tonic-clonic seizures is the most important risk factor. Nocturnal monitoring of seizures (using devices) or the presence of another person at night may represent important factors to reduce the risk of SUDEP. Thus, seizure control and seizure monitoring are, according to current knowledge, the most important factors to avoid SUDEP. Some recent studies have contributed to a better understanding of possible pathomechanisms of SUDEP; however, further research is needed to identify predictive clinical factors and biomarkers and in particular to prevent SUDEP.

Electrocardiogram and cardiac testing among patients in the emergency department with seizure versus syncope

Objective

Cardiogenic syncope can present as a seizure. The distinction between seizure disorder and cardiogenic syncope can only be made if one considers the diagnosis. Our main objective was to identify whether patients presenting with a chief complaint (reason for visit) as seizure or syncope received an electrocardiogram in the emergency department across all age groups.

Methods

We conducted a secondary analysis of data collected in the 2010 to 2014 National Hospital Ambulatory Medical Care Survey comparing patients presenting with a chief complaint of syncope versus seizure to determine likelihood of getting an evaluation for possible life threatening cardiovascular disease. The primary endpoint was receiving an electrocardiogram in the emergency department; secondary endpoint was receiving cardiac biomarkers.

Results

There was a total of 144,094 patient encounters. Of these visits, 1,553 had syncope and 1,470 had seizure (60.3% vs. 44.2% female, 19.9% vs. 29.0% non-white). After adjusting for age, sex, mode of arrival and insurance, patients with syncope were more likely to receive an electrocardiogram compared to patients with seizure (odds ratio, 10.86; 95% confidence interval [CI], 8.52 to 13.84). This was true across all age groups (0 to 18 years, 56% vs. 7.5%; 18 to 44 years, 60% vs. 27%; 45 to 64 years, 82% vs. 41%; ≥65 years, 85% vs. 68%; P<0.01 for all). Car- diac biomarkers were also obtained more frequently in adult patients with syncope patients (18 to 44 years, 17.5% vs. 10.5%; 45 to 64 years, 33.8% vs. 21.4%; ≥65 years, 47.1% vs. 32.3%; P<0.01 for all).

Conclusion

Patients evaluated in the emergency department for syncope received an electrocar- diogram and cardiac biomarkers more frequently than those that had seizure.

Physicians' role in the development of inappropriate polypharmacy among older adults in Iran: a qualitative study

OBJECTIVES

The use of unnecessary or excessive medications (inappropriate polypharmacy) is a major health challenge among older adults which is driven by several factors. This study aims to provide in-depth descriptions of the physician's role in the development of inappropriate polypharmacy among older adults in Iran.

DESIGN

Qualitative content analysis of interviews, field notes and other relevant documents available (eg, medical records). Data collection and analyses were done concurrently to guide the sampling process.

SETTING

Three purposively selected referral hospitals in Tehran, Iran.

PARTICIPANTS

A total of 7 physicians, 10 older adults, 3 caregivers and 3 pharmacists with a median age of 54 (IQR 23) years were recruited through convenience sampling.

RESULTS

Emerged categories included misdiagnosis, inappropriate prescribing, insufficient patient education, poor communication, unprofessional behaviour and limited perspectives which highlight the role of physicians in the development of inappropriate polypharmacy among older adults in Iran under the main concept of poor medical practice.

CONCLUSION

This study provides valuable insight on the role of physicians in the development of inappropriate polypharmacy among the elderly in the healthcare setting in Iran by exploring the viewpoints of physicians, patients, caregivers and pharmacists. Physicians can be an influential factor in tackling this challenge through proper diagnosis, prescription, patient education and follow-up. In Iran, physicians' practice styles are affected by potentially adverse factors such as the novelty of geriatric medicine, lack of a referral system, patient unfamiliarity with the system and lack of a monitoring system for multiple prescriptions. Furthermore, clinics tend to be overcrowded and visit fees can be low; in this setting, lack of physician assistants leads to limited time allocation to each patient and physician dissatisfaction with their income.

Dual-center, dual-platform microRNA profiling identifies potential plasma biomarkers of adult temporal lobe epilepsy

Background

There are no blood-based molecular biomarkers of temporal lobe epilepsy (TLE) to support clinical diagnosis. MicroRNAs are short noncoding RNAs with strong biomarker potential due to their cell-specific expression, mechanistic links to brain excitability, and stable detection in biofluids. Altered levels of circulating microRNAs have been reported in human epilepsy, but most studies collected samples from one clinical site, used a single profiling platform or conducted minimal validation.

Method

Using a case-control design, we collected plasma samples from video-electroencephalogram-monitored adult TLE patients at epilepsy specialist centers in two countries, performed genome-wide PCR-based and RNA sequencing during the discovery phase and validated findings in a large (>250) cohort of samples that included patients with psychogenic non-epileptic seizures (PNES).

Findings

After profiling and validation, we identified miR-27a-3p, miR-328-3p and miR-654-3p with biomarker potential. Plasma levels of these microRNAs were also changed in a mouse model of TLE but were not different to healthy controls in PNES patients. We determined copy number of the three microRNAs in plasma and demonstrate their rapid detection using an electrochemical RNA microfluidic disk as a prototype point-of-care device. Analysis of the microRNAs within the exosome-enriched fraction provided high diagnostic accuracy while Argonaute-bound miR-328-3p selectively increased in patient samples after seizures. In situ hybridization localized miR-27a-3p and miR-328-3p within neurons in human brain and bioinformatics predicted targets linked to growth factor signaling and apoptosis.

Interpretation

This study demonstrates the biomarker potential of circulating microRNAs for epilepsy diagnosis and mechanistic links to underlying pathomechanisms.

Discovery and validation of blood microRNAs as molecular biomarkers of epilepsy: Ways to close current knowledge gaps

There is a major unmet need for biomarkers of epilepsy. Biofluids such as blood offer a potential source of molecular biomarkers. MicroRNAs (miRNAs) fulfill several key requirements for a blood‐based molecular biomarker being enriched in the brain and dysregulated in epileptic brain tissue, and manipulation of miRNAs can have seizure‐suppressive and disease‐modifying effects in preclinical models. Biofluid miRNAs also possess qualities that are favorable for translation, including stability and easy and cheap assay techniques. Herein we review findings from both clinical and animal models. Studies have featured a mix of unbiased profiling and hypothesis‐driven efforts. Blood levels of several brain‐enriched miRNAs are altered in patients with epilepsy and in patients with drug‐resistant compared to drug‐responsive seizures, with encouraging receiver‐operating characteristic (ROC) curve analyses, both in terms of sensitivity and specificity. Both focal and generalized epilepsies are associated with altered blood miRNA profiles, and associations with clinical parameters including seizure burden have been reported. Results remain preliminary, however. There is a need for continued discovery and validation efforts that include multicenter studies and attention to study design, sample collection methodology, and quality control. Studies focused on epileptogenesis as well as associations with covariables such as sex, etiology, and timing of sampling remain limited. We identify 10 knowledge gaps and propose experiments to close these. If adequately addressed, biofluid miRNAs may be an important future source of diagnostic biomarkers that could also support forthcoming trials of antiepileptogenesis or disease‐modifying therapies.

How Long Should Routine EEG Be Recorded to Get Relevant Information?

OBJECTIVE

The optimal duration of routine EEG (rEEG) has not been determined on a clinical basis. This study aims to determine the time required to obtain relevant information during rEEG with respect to the clinical request.

METHOD

All rEEGs performed over 3 months in unselected patients older than 14 years in an academic hospital were analyzed retrospectively. The latency required to obtain relevant information was determined for each rEEG by 2 independent readers blinded to the clinical data. EEG final diagnoses and latencies were analyzed with respect to the main clinical requests: subacute cognitive impairment, spells, transient focal neurologic manifestation or patients referred by epileptologists.

RESULTS

From 430 rEEGs performed in the targeted period, 364 were analyzed: 92% of the pathological rEEGs were provided within the first 10 minutes of recording. Slowing background activity was diagnosed from the beginning, whereas interictal epileptiform discharges were recorded over time. Moreover, the time elapsed to demonstrate a pattern differed significantly in the clinical groups: in patients with subacute cognitive impairment, EEG abnormalities appeared within the first 10 minutes, whereas in the other groups, data could be provided over time.

CONCLUSION

Patients with subacute cognitive impairment differed from those in the other groups significantly in the elapsed time required to obtain relevant information during rEEG, suggesting that 10-minute EEG recordings could be sufficient, arguing in favor of individualized rEEG. However, this conclusion does not apply to intensive care unit patients.

Practical Management of Epileptic Seizures and Status Epilepticus in Adult Palliative Care Patients

In terminally ill patients, paroxysmal or episodic changes of consciousness, movements and behavior are frequent. Due to ambiguous appearance, the correct diagnosis of epileptic seizures (ES) and non-epileptic events (NEE) is often difficult. Treatment is frequently complicated by the underlying condition, and an approach indicated in healthier patients may not always be appropriate in the palliative care setting. This article provides recommendations for diagnosis of ES and NEE and treatment options for ES in adult palliative care patients, including aspects of alternative administration routes for antiepileptic drugs such as intranasal, subcutaneous, or rectal application.

Early postictal serum lactate concentrations are superior to serum creatine kinase concentrations in distinguishing generalized tonic-clonic seizures from syncopes

Concentrations of serum creatine kinase (CK) and serum lactate are frequently measured to help differentiate between generalized tonic-clonic seizures (GTCS) and syncope. The aim of this prospective cohort study was to systematically compare these two markers. The primary outcome is the measurement of serum lactate and CK in blood samples drawn within 2 h of the event in patients admitted with either a GTCS (n = 49) or a syncope (n = 36). Furthermore, the specificity and sensitivity of serum lactate and CK are determined as diagnostic markers in distinguishing between GTCS and syncope. GTCS patients have significantly higher serum lactate levels compared to syncope patients (p < 0.001). In contrast, CK does not differ between groups at admission. Regarding the first hour after the seizure, we identify a cut-off for serum lactate of 2.45 mmol/l for diagnosing GTCS as the cause of an impairment of consciousness with a sensitivity of 0.94 and a specificity of 0.93 (AUC: 0.97; 95% CI 0.94-1.0). In the second hour after the event, the ROC analysis yields similar results (AUC: 0.94; 95% CI 0.85-1.0). Serum lactate is a sensitive and specific diagnostic marker to discriminate GTCS from syncope and is superior to CK early after admission to the emergency department.