A self-administered screening instrument for psychogenic nonepileptic seizures

Design and validation of a diagnostic suspicion checklist to differentiate epileptic from psychogenic nonepileptic seizures (PNES-DSC)

OBJECTIVE

Psychogenic non-epileptic seizures (PNES) are complex clinical manifestations and misdiagnosis as status epilepticus remains high, entailing deleterious consequences for patients. Video-electroencephalography (vEEG) remains the gold-standard method for diagnosing PNES. However, time and economic constraints limit access to vEEG, and clinicians lack fast and reliable screening tools to assist in the differential diagnosis with epileptic seizures (ES). This study aimed to design and validate the PNES-DSC, a clinically based PNES diagnostic suspicion checklist with adequate sensitivity (Se) and specificity (Sp) to discriminate PNES from ES.

METHODS

A cross-sectional study with 125 patients (n = 104 drug-resistant epilepsy; n = 21 PNES) admitted for a vEEG protocolised study of seizures. A preliminary PNES-DSC (16-item) was designed and used by expert raters blinded to the definitive diagnosis to evaluate the seizure video recordings for each patient. Cohen's kappa coefficient, leave-one-out cross-validation (LOOCV) and balance accuracy (BAC) comprised the main validation analysis.

RESULTS

The final PNES-DSC is a 6-item checklist that requires only two to be present to confirm the suspicion of PNES. The LOOCV showed 71.4% BAC (Se = 45.2%; Sp = 97.6%) when the expert rater watched one seizure video recording and 83.4% BAC (Se = 69.6%; Sp = 97.2%) when the expert rater watched two seizure video recordings.

CONCLUSION

The PNES-DSC is a straightforward checklist with adequate psychometric properties. With an integrative approach and appropriate patient history, the PNES-DSC can assist clinicians in expediting the final diagnosis of PNES when vEEG is limited. The PNES-DSC can also be used in the absence of patients, allowing clinicians to assess seizure recordings from smartphones.

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.

Multidimensional psychopathological profile differences between patients with psychogenic nonepileptic seizures and epileptic seizure disorders

OBJECTIVE

Early differential diagnosis of psychogenic nonepileptic seizures (PNES) and epileptic seizures (ES) remains difficult. Self-reported psychopathology is often elevated in patients with PNES, although relatively few studies have examined multiple measures of psychopathology simultaneously. This study aimed to identify differences in multidimensional psychopathology profiles between PNES and ES patient groups.

METHOD

This was a retrospective case-control study involving patients admitted for video-EEG monitoring (VEM) over a two-year period. Clinicodemographic variables and psychometric measures of depression, anxiety, dissociation, childhood trauma, maladaptive personality traits, and cognition were recorded. Diagnosis of PNES or ES was determined by multidisciplinary assessment and consensus opinion. General linear mixed models (GLMMs) were used to investigate profile differences between diagnostic groups across psychometric measures. A general psychopathology factor was then computed using principal components analysis (PCA) and differences between groups in this 'p' factor were investigated.

RESULTS

261 patients (77 % with ES and 23 % with PNES) were included in the study. The PNES group endorsed greater symptomatology with GLMM demonstrating a significant main effect of group (η²_p = 0.05) and group by measure interaction (η²_p = 0.03). Simple effects analysis indicated that the PNES group had particularly elevated scores for childhood trauma (β = 0.78), dissociation (β = 0.70), and depression (β = 0.60). There was a high correlation between psychopathology measures, with a single p factor generated to explain 60 % variance in the psychometric scores. The p factor was elevated in the PNES group (β = 0.61). ROC curve analysis indicated that these psychometric measures had limited usefulness when considered individually (AUC range = 0.63-0.69).

CONCLUSION

Multidimensional psychopathological profile differences exist between patients with PNES and ES. Patients with PNES report more psychopathology overall, with particular elevations in childhood trauma, dissociation, and depression. Although not suitable to be used as a standalone screening tool to differentiate PNES and ES, understanding of these profiles at a construct level might help triage patients and guide further psychiatric examination and enquiry.

Diagnostic accuracy of clinical signs and symptoms for psychogenic nonepileptic attacks versus epileptic seizures: A systematic review and meta-analysis

BACKGROUND

Psychogenic nonepileptic attacks (PNEA) are events of altered behavior that resemble epileptic seizures (ES) but are not caused by abnormal electrical cortical activity. Understanding which clinical signs and symptoms are associated with PNEA may allow better triaging for video-electroencephalogram monitoring (VEM) and for a more accurate prediction when such testing is unavailable.

METHODS

We performed a systematic review searching Medline, Embase, and Cochrane Central from inception to March 29, 2019. We included original research that reported at least one clinical sign or symptom, included distinct groups of adult ES and PNEA with no overlap, and used VEM for the reference standard. Two authors independently assessed quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies tool. Pooled estimates of sensitivity and specificity of studies were evaluated using a bivariate random effects model.

RESULTS

We identified 4028 articles, of which 33 were included. There was a female sex predominance in the PNEA population (n = 22). From our meta-analysis, pooled sensitivities (0.27-0.72) and specificities (0.51-0.89) for PNEA were modest for individual signs. History of sexual abuse had the highest pooled specificity (89%), while the most sensitive feature was female sex (72%). Individual studies (n = 4) reported high levels of accuracy for ictal eye closure (sensitivity 64-73.7% and specificity 76.9-100%) and post-traumatic stress disorder (no reported sensitivity or specificity). Assuming the pre-test probability for PNEA in a tertiary care epilepsy center is 14%, even the strongest meta-analyzed features only exert modest diagnostic value, increasing post-test probabilities to a maximum of 33%.

CONCLUSIONS

This review reflects the limited certainty afforded by individual clinical features to distinguish between PNEA and ES. Specific demographic and comorbid features, even despite moderately high specificities, impart minimal impact on diagnostic decision making. This emphasizes the need for the development of multisource predictive tools to optimize diagnostic likelihood ratios.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Reliability of additional reported seizure manifestations to identify dissociative seizures

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

OBJECTIVE

Functional seizures (FS) are often misclassified as epileptic seizures (ES). This study aimed to create an easy to use but comprehensive screening tool to guide further evaluation of patients presenting with this diagnostic dilemma.

MATERIALS AND METHODS

Demographic, clinical and diagnostic data were collected on patients admitted for video-EEG monitoring for clarification of their diagnosis. Upon discharge, patients were classified as having ES vs FS. Using the collected characteristics and video-EEG diagnosis, we created a multivariable logistic regression model to identify predictors of ES. Then, we trained an integer-coefficient model with the most frequently selected predictors, creating a pointing system coined DDESVSFS, with scores ranging from -17 to +8 points.

RESULTS

43 patients with FS and 165 patients with ES were recruited. In the final integer-coefficient model, 8 predictors were identified as significant in differentiating ES from FS: normal electroencephalogram (-3 points), predisposing factors for FS (-3 points), increased number of comorbidities (-3 points), semiology suggestive of FS (-4 points), increased seizure frequency (-4 points), longer disease duration (+3 points), antiepileptic polypharmacy (+2 points) and compliance with antiepileptic drugs (+3 points). Cumulative scores of ≤ -9 points carried <5% predictive value for ES, while cumulative scores of ≥ -1 points carried >95% predictive value. The model performed well (AUC: 0.923, sensitivity: 0.945, specificity: 0.698).

CONCLUSIONS

We propose DDESVSFS as a simple, rapid and comprehensive prediction score for the Differential Diagnosis of Epileptic Seizures VS Functional Seizures. Large prospective studies are needed to evaluate its utility in clinical practice.

Objective score from initial interview identifies patients with probable dissociative seizures

OBJECTIVE

To develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with "probable" dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment.

METHODS

Based on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews.

RESULTS

The DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74-80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists' impression (84%, 95% CI: 80-88%) and the kappa between neurologists' and the DSLS was 21% (95% CI: 1-41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0-11%).

SIGNIFICANCE

The evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center.

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.

Machine learning as a diagnostic decision aid for patients with transient loss of consciousness

Background

Transient loss of consciousness (TLOC) is a common reason for presentation to primary/emergency care; over 90% are because of epilepsy, syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnoses are common, and there are currently no validated decision rules to aid diagnosis and management. We seek to explore the utility of machine-learning techniques to develop a short diagnostic instrument by extracting features with optimal discriminatory values from responses to detailed questionnaires about TLOC manifestations and comorbidities (86 questions to patients, 31 to TLOC witnesses).

Methods

Multi-center retrospective self- and witness-report questionnaire study in secondary care settings. Feature selection was performed by an iterative algorithm based on random forest analysis. Data were randomly divided in a 2:1 ratio into training and validation sets (163:86 for all data; 208:92 for analysis excluding witness reports).

Results

Three hundred patients with proven diagnoses (100 each: epilepsy, syncope and PNES) were recruited from epilepsy and syncope services. Two hundred forty-nine completed patient and witness questionnaires: 86 epilepsy (64 female), 84 PNES (61 female), and 79 syncope (59 female). Responses to 36 questions optimally predicted diagnoses. A classifier trained on these features classified 74/86 (86.0% [95% confidence interval 76.9%-92.6%]) of patients correctly in validation (100 [86.7%-100%] syncope, 85.7 [67.3%-96.0%] epilepsy, 75.0 [56.6%-88.5%] PNES). Excluding witness reports, 34 features provided optimal prediction (classifier accuracy of 72/92 [78.3 (68.4%-86.2%)] in validation, 83.8 [68.0%-93.8%] syncope, 81.5 [61.9%-93.7%] epilepsy, 67.9 [47.7%-84.1%] PNES).

Conclusions

A tool based on patient symptoms/comorbidities and witness reports separates well between syncope and other common causes of TLOC. It can help to differentiate epilepsy and PNES. Validated decision rules may improve diagnostic processes and reduce misdiagnosis rates.

Classification of evidence

This study provides Class III evidence that for patients with TLOC, patient and witness questionnaires discriminate between syncope, epilepsy and PNES.

Personality traits, illness behaviors, and psychiatric comorbidity in individuals with psychogenic nonepileptic seizures (PNES), epilepsy, and other nonepileptic seizures (oNES): Differentiating between the conditions

OBJECTIVES

The study aimed to investigate if South African individuals with psychogenic nonepileptic seizures (PNES) differ from individuals with epileptic seizures (ES) and other nonepileptic seizures (oNES) in terms of demographic and seizure characteristics, personality traits, illness behaviors, and depression, anxiety, and posttraumatic stress disorder (PTSD) comorbidity in statistically significant ways; and if so, to test if these differences can be utilized in raising suspicion of PNES as the differential diagnosis to epilepsy and oNES in practice.

METHODS

Data were analyzed from 29 adults with seizure complaints recruited using convenience sampling from a private and a government hospital with video-electroencephalography (vEEG) technology. A quantitative double-blind convenient sampling comparative design was used. A demographic and seizure questionnaire, the NEO Five Factor Inventory-3 (NEO-FFI-3), an abbreviated version of Illness Behavior Questionnaire (IBQ), and the Beck Anxiety Inventory - Primary Care (BAI-PC) were administered. Cronbach's alphas, analysis of variance (ANOVA), cross-tabulation, Fisher exact test, and receiver operating characteristic (ROC) analyses were performed on the dataset.

RESULTS

The total sample consisted of 29 participants, of which 5 had PNES (17%), 21 ES (73%), and 3 oNES (10%). The final sample was comprised of 24 participants from the private hospital and 5 from the government hospital. The group with PNES was found to be significantly more male, to experience significantly more monthly seizures, and chronic pain when comparing the PNES with the ES group, and the PNES with the combined ES and oNES group in both private only sample, as well as the private and government hospital combined sample. Patients with PNES also had a higher level of education compared with the group with ES in the combined private and government hospital sample, something that was not evident in the private hospital only sample. No significant differences between groups were found in either sample in terms of age, population group, language, age at first seizure, and the NEO-FFI-3 subscales. All three groups scored above the cutoff point of 5 exhibiting depression, anxiety, and PTSD symptoms on the BAI-PC in both samples. However, the group with PNES tended to score significantly higher than the group with ES and the combined ES and oNES group in the private hospital sample. A cutoff point of 12 on the BAI-PC was found to predict PNES in this seizure population with 80% sensitivity and 89% specificity. However, once the analysis was repeated on the combined private and government hospital sample, significance in BAI-PC scores between groups was lost. All scales showed good reliability in our study, with the exception of the "Openness to Experience" subscale of the NEO-FFI-3 once reliability analysis was carried out on the combined private and government hospital group.

CONCLUSIONS

This study provides an important stepping stone in the understanding of demographic and seizure factors, personality domains, abnormal illness behaviors, and psychiatric comorbidity in the South African population with PNES. The study also reported on a cutoff score of 12 on the BAI-PC predicting PNES with 80% sensitivity and 89% specificity in a private hospital sample.

The Fainting Assessment Inventory: A 10-Item Instrument Developed to Screen for Psychogenic Nonsyncopal Collapse Among Youth Referred for Syncope

The conversion disorder that appears like syncope is common but poorly recognized. The study aimed to develop and validate a brief, clinician-administered screening tool to discriminate psychogenic nonsyncopal collapse (PNSC) among young patients referred for fainting. Consecutive patients with PNSC and with syncope (15.4 ± 2.2 years) completed a 92-item inventory highlighting features typical of PNSC and neurally mediated syncope (n = 35, each cohort). Fourteen items were retained and revised and then administered to new cohorts ultimately diagnosed with PNSC or syncope (n = 40, each cohort). Further revision led to a 10-item Fainting Assessment Inventory (FAI-10). Scoring the syncope ratings positively and the PNSC ratings negatively, median scores differed between cohorts with PNSC and with syncope (-6 vs. 7; p < 0.001). Diagnostic sensitivity (0.95), specificity (0.875), positive predictive value (0.889), negative predictive value (0.93), and area under the curve (0.973) were calculated. The FAI-10 furthers clinicians' ability to distinguish various forms of transient loss of consciousness.

Reliability of reported peri-ictal behavior to identify psychogenic nonepileptic seizures

PURPOSE

Differentiating psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES) can be difficult, even when expert clinicians have video recordings of seizures. Moreover, witnesses who are not trained observers may provide descriptions that differ from the expert clinicians', which often raises concern about whether the patient has both ES and PNES. As such, quantitative, evidence-based tools to help differentiate ES from PNES based on patients' and witnesses' descriptions of seizures may assist in the early, accurate diagnosis of patients.

METHODS

Based on patient- and observer-reported data from 1372 patients with diagnoses documented by video-elect roencephalography (vEEG), we used logistic regression (LR) to compare specific peri-ictal behaviors and seizure triggers in five mutually exclusive groups: ES, PNES, physiologic non-epileptic seizure-like events, mixed PNES plus ES, and inconclusive monitoring. To differentiate PNES-only from ES-only, we retrospectively trained multivariate LR and a forest of decision trees (DF) to predict the documented diagnoses of 246 prospective patients.

RESULTS

The areas under the receiver operating characteristic curve (AUCs) of the DF and LR were 75% and 74%, respectively (empiric 95% CI of chance 37-62%). The overall accuracy was not significantly higher than the naïve assumption that all patients have ES (accuracy DF 71%, LR 70%, naïve 68%, p > 0.05).

CONCLUSIONS

Quantitative analysis of patient- and observer-reported peri-ictal behaviors objectively changed the likelihood that a patient's seizures were psychogenic, but these reports were not reliable enough to be diagnostic in isolation. Instead, our scores may identify patients with "probable" PNES that, in the right clinical context, may warrant further diagnostic assessment.

Value of witness observations in the differential diagnosis of transient loss of consciousness

OBJECTIVE

This retrospective study explores to what extent additional information from event witnesses provided using the novel 31-item Paroxysmal Event Observer (PEO) Questionnaire improves the differentiation among epilepsy, syncope, and psychogenic nonepileptic seizures (PNES) achievable with information provided by patients alone.

METHODS

Patients with transient loss of consciousness caused by proven epilepsy (n = 86), syncope (n = 79), or PNES (n = 84) attending specialist neurology/syncope services in the United Kingdom and event observers provided Paroxysmal Event Profile (PEP), PEO, and personal information (PI) (e.g., sex, age, medical history) data. PEO data were subjected to exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA). PEO, PEP, and PI data were used separately and in combination to differentiate diagnoses by pairwise and multinomial logistic regressions. Predicted diagnoses were compared with gold standard medical diagnoses.

RESULTS

EFA/CFA identified a 4-factor structure of the PEO based on 26/31 questionnaire items with loadings ≥0.4. Observer-reported factors alone differentiated better between syncope and epilepsy than patient-reported factors (accuracy: 96% vs 85%, p = 0.0004). Observer-reported data improved accuracy over differentiation based on patient-reported data alone from 90% to 100% between syncope and epilepsy (p = 0.005), 76% to 83% between epilepsy and PNES (p = 0.006), and 93% to 95% between syncope and PNES (p = 0.098).

CONCLUSIONS

Information from observers can make an important contribution to the differentiation of epilepsy from syncope or PNES but adds less to that of syncope from PNES.

Self-reporting versus clinical scrutiny: the value of adding questionnaires to the routine evaluation of seizure disorders. An exploratory study on the differential diagnosis between epilepsy and psychogenic nonepileptic seizures

Questionnaires or symptom lists have proved effective for differentiating epileptic seizures (ES) from psychogenic nonepileptic seizures (PNES). However, monitoring the events, corroborated by medical history gathered by experts, remains the gold standard. We directly compared symptoms and characteristic of the events self-reported by patients/eyewitnesses (Questionnaire A/B) with the information contained in the clinical charts of 50 patients with undefined diagnosis undergoing long-term monitoring. Data extracted from medical records were reformatted to fit the questionnaires' templates (A1/B1) for comparison. Quantitatively, self-reported information was considerably greater and more complete. Calculating sensitivity (SE) and specificity (SP) of all variables in the group with confirmed diagnosis, we identified those above the preset thresholds with the potential to discriminate between ES and PNES. Eight predictive variables were common to both methods: head injury, physical/emotional abuse, chronic fatigue (A); talked out of seizures, eyes closed, apnea, and collapsing (B). Eleven predictive variables were specific to direct questioning: preictal headache, bright light, feeling overwhelmed, heart racing, tingling and numbness, postictal trouble speaking, physical pain, history of gastro-esophageal reflux disease (GERD), self-inflicted injuries (A); on/off shaking, and side-to-side head movements (B). Thirteen predictive variables were generated by chart review: sleep deprivation, strong emotions/anxiety, preictal headache (warning), nausea/vomiting, history of PNES, cholecystectomy, depression, medications for behavioral problems (A1), sudden start/sudden stop of shaking, both sides shaking, falling during the seizure, feeling confused and disoriented postictally (B1). CONCLUSION: Self-reporting and clinical scrutiny are complementary. Structured questionnaires increase the range of predictive variables and should be utilized routinely to facilitate clinicians' quest for the correct diagnosis.

Dual diagnosis of epilepsy and psychogenic nonepileptic seizures: Systematic review and meta-analysis of frequency, correlates, and outcomes

Comorbid epilepsy and psychogenic nonepileptic seizures (PNES) represent a serious challenge for the clinicians. However, the frequency, associations, and outcomes of dual diagnosis of epilepsy and PNES are unclear. The aim of the review was to determine the frequency, correlates, and outcomes of a dual diagnosis. A systematic review of all published observational studies (from inception to Dec. 2016) was conducted to determine the frequency, correlates, and outcomes of dual diagnosis. We included studies of individuals of any age reporting a dual diagnosis of epilepsy and PNES. All observational study designs were included with the exception of case reports and case series with fewer than 10 participants. The mean frequency of epilepsy in patients with PNES across all studies was 22% (95% confidence intervals [CI] 20 to 25%, range: 0% to 90%) while the mean frequency of PNES in patients with epilepsy was 12% (95% CI 10 to 14%, range: 1% to 62%). High heterogeneity means that these pooled estimates should be viewed with caution. A number of correlates of dual diagnosis were reported. Some studies delineated differences in semiology of seizures in patients with dual diagnosis vs. PNES or epilepsy only. However, most of the correlates were inconclusive. Only a few studies examined outcome in patients with dual diagnosis. Dual diagnosis is common in clinical practice, especially among patients referred to specialized services, and requires careful diagnosis and management.

Diagnostic criteria to aid the differential diagnosis of patients presenting with transient loss of consciousness: A systematic review

BACKGROUND

Transient loss of consciousness (TLOC) is a common presentation in primary care. Over 90% of these are due to epileptic seizures (ES), syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnosis rates are as high as 30%.

METHODS

Systematic review of inter-ictal clinical criteria to aid differential diagnosis of TLOC. We searched Medline, EMBASE, CINAHL and PsycInfo databases, as well as relevant grey literature depositories and citations of relevant reviews and guidelines for studies giving sensitivity and specificity of inter-ictal clinical characteristics used to differentiate between causes of TLOC. Two independent reviewers selected studies for inclusion and performed critical appraisal of included articles. We performed a narrative synthesis of included studies.

RESULTS

Of 1023 results, 16 papers were included. Two compared syncope, ES, and PNES; all others compared ES and PNES. All were at significant risk of bias in at least one domain. 6 studied patient symptoms, 6 medical and social history, 3 witness reports and 1 examination findings. No individual criterion differentiated between diagnoses with high sensitivity and specificity.

CONCLUSIONS

There is a lack of validated diagnostic criteria to help clinicians assessing patients in primary or emergency care settings to discriminate between common causes of TLOC. Performance may be improved by combining sets of criteria in a clinical decision rule, but no such rule has been validated prospectively against gold-standard diagnostic criteria.

Patients' and caregivers' contributions for differentiating epileptic from psychogenic nonepileptic seizures. Value and limitations of self-reporting questionnaires: A pilot study

PURPOSE

Questionnaires investigating semiology and comorbidities of psychogenic non-epileptic seizures (PNES) have been used mainly to help physicians expedite referrals to epilepsy centres for confirmation of diagnosis rather than as alternative diagnostic tool when video-EEG monitoring (VEM), the current gold standard, is not available or is inconclusive.

METHODS

We developed one structured questionnaire for patients, exploring subjective experiences and vulnerabilities and one for eyewitnesses, focused on features observable during typical events to study prospectively 50 consecutive adult patients with PNES or epileptic seizures (ES) admitted for VEM. A list of variables representing specific signs, symptoms and risk factors was obtained from each question. Specificity (SP) and sensitivity (SE) of each variable were calculated analyzing patient's and witness' responses against the final diagnosis. Statistical significance was assessed using the Fisher's exact test.

RESULTS

Twenty-eight patients' questionnaires (17 PNES, 11 ES) were eligible for analysis. Seven variables with high SE and SP, of which 5 statistically significant, emerged as diagnostic predictors. They comprised three historical items: head injury, physical abuse and chronic fatigue; two warning signs: heart racing and tingling or numbness; one triggering sign: headache; one postictal symptom: physical pain. Sixteen witness questionnaires (6 PNES, 10 ES) were available. Side-to-side head movements and eyes closed were the statistically significant variables.

CONCLUSION

Pending further refinements, ad hoc questionnaires specifically designed for patients and eyewitnesses, may represent a practical tool for distinguishing ES from PNES in settings without sophisticated facilities or when VEM is inconclusive.

Identifying psychogenic seizures through comorbidities and medication history

OBJECTIVE

Low-cost evidence-based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities may contribute to this high cost.

METHODS

Based on data from 1,365 adult patients with video-electroencephalography-confirmed diagnoses from a single center, we used logistic and Poisson regression to compare the total number of comorbidities, number of medications, and presence of specific comorbidities in five mutually exclusive groups of diagnoses: epileptic seizures (ES) only, PNES only, mixed PNES and ES, physiologic nonepileptic seizurelike events, and inconclusive monitoring. To determine the diagnostic utility of comorbid diagnoses and medication history to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and age, trained using a retrospective database and validated using a prospective database.

RESULTS

Our model differentiated PNES only from ES only with a prospective accuracy of 78% (95% confidence interval =72-84%) and area under the curve of 79%. With a few exceptions, the number of comorbidities and medications was more predictive than a specific comorbidity. Comorbidities associated with PNES were asthma, chronic pain, and migraines (p < 0.01). Comorbidities associated with ES were diabetes mellitus and nonmetastatic neoplasm (p < 0.01). The population-level analysis suggested that patients with mixed PNES and ES may be a population distinct from patients with either condition alone.

SIGNIFICANCE

An accurate patient-reported medical history and medication history can be useful when screening for possible PNES. Our prospectively validated and objective score may assist in the interpretation of the medication and medical history in the context of the seizure description and history.

What patients think about psychogenic nonepileptic seizures in Buenos Aires, Argentina: A qualitative approach

PURPOSE

To analyse the methods of reasoning with regard to patients' experiences of living with psychogenic nonepileptic seizures (PNES) in Buenos Aires, Argentina.

METHOD

A qualitative approach using semi-structured interviews was used to gain an in-depth and contextual understanding of the perspectives of five patients with PNES. Data collection and analysis were followed by an inductive and interpretive approach informed by the principles of thematic analysis.

RESULTS

Explanatory models and prototypes were identified from the patients' narratives. Four patients related their suffering regarding psychosocial causes -family conflicts, sexual harassment, and life changes, among others-. Hereditary and organic hypotheses appeared to be unspecific. Folk explanations were common to all participants (magic, witchcraft, energetic causes). Four patients used the term epilepsy as an illness prototype, focusing on seizures and the use of antiepileptic drugs. Three of them also compared their illness to other people's "attacks" (heart attacks, panic attacks, nervous breakdown). Only one of them referred to someone who was suspected of having epilepsy.

CONCLUSION

Patients' psychosocial explanatory models are different from the results of previous studies because these studies indicate that most patients support somatic explanations. Patients also use folk explanations related to traditional medicine, which highlights the interpersonal aspects of the disease. Doctor-patient communication is essential for a correct understanding of PNES, resulting in better outcomes. It could also help to reduce the cultural distance between professionals and patients, leading to narrowing inequalities present in multicultural healthcare services.

Value of patient-reported symptoms in the diagnosis of transient loss of consciousness

OBJECTIVE

Epileptic seizures, syncope, and psychogenic nonepileptic seizures (PNES) account for over 90% of presentations with transient loss of consciousness (TLOC). The patient's history is crucial for the diagnosis, but the diagnostic value of individual semiologic features is limited. This study explores the diagnostic potential of a comprehensive questionnaire focusing on TLOC-associated symptoms.

METHODS

A total of 386 patients with proven epilepsy, 308 patients with proven PNES, and 371 patients with proven syncope were approached by post to recruit 100 patients in each diagnostic group. Symptoms were self-reported on an 86-item questionnaire (the Paroxysmal Event Profile [PEP]) using a 5-point Likert scale (always to never). Data were subjected to exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA). Factors were used to differentiate between diagnoses by pairwise and multinomial regression.

RESULTS

Patients with PNES reported more and more frequent TLOC-associated symptoms than those with epilepsy or syncope (p < 0.001). EFA/CFA identified a 5-factor structure based on 74/86 questionnaire items with loadings ≥0.4. Pairwise logistic regression analysis correctly classified 91% of patients with epilepsy vs those with syncope, 94% of those with PNES vs those with syncope, and 77% of those with epilepsy vs those with PNES. Multinomial logistic regression analysis yielded a similar pattern.

CONCLUSIONS

Clusters of self-reported TLOC symptoms can be used to direct patients to appropriate investigation and treatment pathways for syncope on the one hand and seizures on the other, although additional information is required for a reliable distinction, especially between epilepsy and PNES.

Nonepileptic seizures - subjective phenomena

Psychogenic nonepileptic seizures (PNES) superficially resemble epileptic seizures or syncope and most patients with PNES are initially misdiagnosed as having one of the latter two types of transient loss of consciousness. However, evidence suggests that the subjective seizure experience of PNES and its main differential diagnoses are as different as the causes of these three disorders. In spite of this, and regardless of the fact that PNES are considered a mental disorder in the current nosologies, research has only given limited attention to the subjective symptomatology of PNES. Instead, most phenomenologic research has focused on the visible manifestations of PNES and on physiologic parameters, neglecting patients' symptoms and experiences. This chapter gives an overview of qualitative and quantitative studies providing insights into subjective symptoms associated with PNES, drawing on a wide range of methodologies (questionnaires, self-reports, physiologic measures, linguistic analyses, and neuropsychologic experiments). After discussing the scope and limitations of these approaches in the context of this dissociative phenomenon, we discuss ictal, peri-ictal and interictal symptoms described by patients with PNES. We particularly focus on impairment of consciousness. PNES emerges as a clinically heterogeneous condition. We conclude with a discussion of the clinical significance of particular subjective symptoms for the engagement of patients in treatment, the formulation of treatment, and prognosis.

Improving first responders' psychogenic nonepileptic seizures diagnosis accuracy: Development and validation of a 6-item bedside diagnostic tool

OBJECTIVE

Epileptic seizures (ES) are often seen as a medical emergency, and their immediate and accurate recognition are pivotal in providing acute care. However, a number of clinical situations may mimic ES, potentially leading to misdiagnosis at the emergency room and to inappropriate prescription of antiepileptic drugs (AED) in the acute and chronic settings. Psychogenic nonepileptic seizures (PNES) play a major role in this scenario and often delay the correct diagnosis and increase treatment morbidity and cost. First responders often conduct the initial assessment of these patients, and their impression may be decisive in the prehospital approach to seizures. We sought to investigate and improve the accuracy of PNES diagnosis among professionals involved in the initial assistance to patients with seizures.

METHODS

Fifty-three registered nurses, 34 emergency physicians, 33 senior year medical students, and 12 neurology residents took a short training program consisting of an initial video-based seizure assessment test (pretest), immediately followed by a 30-minute presentation of a 6-item bedside diagnostic tool and then a video-based reassessment (posttest). Baseline status and learning curves were determined.

RESULTS

The distinct professional categories showed no significant differences in their ability to diagnose PNES on both pretests and posttests. All groups improved diagnostic skills after the instructional program.

SIGNIFICANCE

The findings helped determine the best identifiable PNES clinical signs and to provide initial validation to a novel diagnostic instrument. In addition, our results showed that educational measures might help in the identification of PNES by first responders, which may decrease the treatment gap.

Biopsychosocial predictors of psychogenic non-epileptic seizures

BACKGROUND

Previous studies have identified numerous biological, psychological and social characteristics of persons with psychogenic non-epileptic seizures (PNES) however the strength of many of these factors have not been evaluated to determine which are predictive of the diagnosis compared to those that may only be stereotypes with limited clinical utility.

METHOD

A retrospective chart review of persons admitted to our epilepsy monitoring unit over a 6-year period was conducted to examine predictors of a video-EEG confirmed PNES diagnosis.

RESULTS

A total of 689 patients had events leading to a diagnosis, 47% (n=324) with PNES only, 12% (n=84) with PNES & Epilepsy and 41% (n=281) with Epilepsy only. Five biological predictors of a PNES only diagnosis were found; number of years with events (OR=1.10), history of head injury (OR=1.91), asthma (OR=2.94), gastro-esophageal reflux disease (OR=1.72) and pain (OR=2.25). One psychological predictor; anxiety (OR=1.72) and two social predictors; being married (OR=1.81) and history of physical/sexual abuse (OR=3.35). Two significant biological predictors of a PNES & Epilepsy diagnosis were found; migraine (OR=1.83) and gastro-esophageal reflux disease (OR=2.17).

CONCLUSIONS

Our findings support the importance of considering the biopsychosocial model for the diagnosis and treatment of PNES or PNES with concomitant epilepsy.

Anxiety and avoidance in psychogenic nonepileptic seizures: the role of implicit and explicit anxiety

This study examined implicit and explicit anxiety in individuals with epilepsy and psychogenic nonepileptic seizures (PNESs) and explored whether these constructs were related to experiential avoidance and seizure frequency. Based on recent psychological models of PNESs, it was hypothesized that nonepileptic seizures would be associated with implicit and explicit anxiety and experiential avoidance. Explicit anxiety was measured by the State-Trait Anxiety Inventory; implicit anxiety was measured by an Implicit Relational Assessment Procedure; and experiential avoidance was measured with the Multidimensional Experiential Avoidance Questionnaire. Although both groups with epilepsy and PNESs scored similarly on implicit measures of anxiety, significant implicit-explicit anxiety discrepancies were only identified in patients with PNESs (p<.001). In the group with PNESs (but not in the group with epilepsy), explicit anxiety correlated with experiential avoidance (r=.63, p<.01) and frequency of seizures (r=.67, p<.01); implicit anxiety correlated with frequency of seizures only (r=.56, p<.01). Our findings demonstrate the role of implicit anxiety in PNESs and provide additional support for the contribution of explicit anxiety and experiential avoidance to this disorder.

Psychogenic nonepileptic seizures: a current overview

Psychogenic nonepileptic seizures (PNES) resemble epilepsy, but no pathophysiological explanation has been established. Although there have been recent advances in PNES research and various hypotheses as to the psychopathology, no theory has achieved general acceptance. In this overview of selected literature on PNES, we highlight the often contradictory findings that underline the challenges that confront both practitioner and researcher. We first provide a synopsis of the history, diagnosis, treatment, and outcomes, as well as patient characteristics of PNES and the relevance of communication in the clinical context. In the subsequent sections we discuss recent research that may advance the understanding and diagnosis of this disorder. These themes include the use of qualitative methods as a viable research option, the application of nonlinear methods to analyze heterogeneous observations during diagnosis, recent advances in neuroimaging of PNES, and the development of international databases.

Diagnostic utility of the Structured Inventory of Malingered Symptomatology for identifying psychogenic non-epileptic events

The Structured Interview of Malingered Symptomatology (SIMS) is a self-report instrument that asks patients whether they experience atypical or implausible symptoms. The instrument has not been evaluated in an epilepsy population, and the potential for it to accurately distinguish between patients with psychogenic non-epileptic events (PNEE) and epileptic event groups has not been established. The SIMS was administered to patients in long-term video-EEG monitoring of these patients, 91 with PNEE and 29 with epilepsy were included in this study. Structured Interview of Malingered Symptomatology total scores as well as neurological and affective subscales were found to be predictors of group membership. Sensitivity and specificity across several different base rates of PNEE as well as maximum level likelihood ratios are presented. The findings not only demonstrate the utility of marked score elevations in differentiating PNEE from epilepsy but also point to considerable caution in interpreting mild elevations. Implications for the utility of this instrument in epilepsy evaluations are discussed.

Recent progress in the understanding and treatment of nonepileptic seizures

PURPOSE OF REVIEW

There has been a steady increase in the number of publications about (psychogenic) nonepileptic seizures (NES) over the past two decades. This review focuses on work published in the past 3 years. It summarizes the most important developments in terms of diagnosis, cause, clinical manifestations and treatment of NES.

RECENT FINDINGS

Several recent studies demonstrate the scope and limitation of questionnaire-based and conversation analytic approaches to the differential diagnosis of epilepsy and NES. Experimental work has revealed that patients with NES have increased levels of physiological arousal at rest which are associated with abnormal mental processing. There is a growing understanding of the meaning and clinical significance of the heterogeneous manifestations of NES. Several studies document the therapeutic potential of an early and effective communication of the diagnosis of NES. A number of randomized controlled or uncontrolled long-term follow-up pilot studies suggest that different forms of psychotherapy are effective for NES.

SUMMARY

Recent research has improved our understanding of NES as a biopsychosocial disorder. Clear diagnostic and management pathways for patients with NES are likely to emerge in the near future.

Psychogenic nonepileptic seizures

This review by three established clinicians/researchers and two 'rising stars' in the field of psychogenic nonepileptic seizures (PNES) describes recent progress in this area and highlights priorities for future research. Empirically testable models of PNES are emerging but many questions about the aetiology of PNES remain unanswered at present. Video-EEG has made it possible for doctors to make secured diagnoses of PNES in more cases. However, unacceptable diagnostic delays and misdiagnoses are still common. Non-specific EEG changes are often misinterpreted as evidence of epilepsy. A better understanding of the symptomatology of PNES may allow earlier and more accurate diagnoses using self-report questionnaires. The communication of the diagnosis and the engagement of patient in psychological treatment can be difficult. A recent pilot RCT has demonstrated the effectiveness of a psychological treatment in reducing seizures in the short term, but longer-term effectiveness is yet to be demonstrated.

Using patient history to distinguish between patients with non-epileptic and patients with epileptic events

Information obtained during psychological evaluations of 93 patients with epileptic events (EEs) and 63 with nonepileptic events (NEEs) was used to test the relative contributions of multiple risk factors to prediction of NEEs during video/EEG monitoring. The best group of independent predictors of NEEs comprised: (1) age at first spell, (2) symptoms of a psychiatric diagnosis other than anxiety or depression, (3) marital instability, (4) symptoms of an anxiety disorder other than panic disorder, and (5) years of education. Report of childhood abuse or neglect and taking psychotropic medication correlated with most of the other risk factors for NEEs. It may not be necessary to gather data on all of the variables shown to be associated with NEEs. Although there is a high prevalence of risk factors for psychopathology in patients with EEs, it is lower compared with that of patients with NEEs, and patients with EEs are less likely to report multiple risk factors.