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

Evidence-Based Practice for the Clinical Assessment of Psychogenic Nonepileptic Seizures: A Report From the American Neuropsychiatric Association Committee on Research

The American Neuropsychiatric Association's Committee on Research assigned the task of defining the most helpful clinical factors and tests in establishing the diagnosis of psychogenic nonepileptic seizures (PNES) during a neuropsychiatric assessment. A systematic review of the literature was conducted using three search engines and specified search terms for PNES and the predetermined clinical factors and diagnostic tests, followed by a selection process with specific criteria. Data extraction results from selected articles are presented for clinical factors (semiology, psychiatric comorbidities, medical comorbidities, psychological traits) and diagnostic tests (EEG, psychometric and neuropsychological measures, prolactin level, clinical neuroimaging, autonomic testing). Semiology with video EEG (vEEG) remains the most valuable tool to determine the diagnosis of PNES. With the exception of semiology, very few studies revealed the predictive value of a clinical factor for PNES, and such findings were isolated and not replicated in most cases. Induction techniques, especially when coupled with vEEG, can lead to a captured event, which then confirms the diagnosis. In the absence of a captured event, postevent prolactin level and personality assessment can support the diagnosis but need to be carefully contextualized with other clinical factors. A comprehensive clinical assessment in patients with suspected PNES can identify several clinical factors and may include a number of tests that can support the diagnosis of PNES. This is especially relevant when the gold standard of a captured event with typical semiology on vEEG cannot be obtained.

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.

Clinical identification of psychogenic nonepileptic events using combinations of psychological tests in a veteran sample

OBJECTIVE

Patients with psychogenic nonepileptic events (PNEE) exhibit heterogenous symptoms and are best diagnosed with long-term video-electroencephalogram (vEEG) data. While extensive univariate data suggest psychological tests may confirm the etiology of PNEE, the multivariate discriminant utility of psychological tests is less clear. The current study aggregated likelihood ratios of multiple psychological tests to evaluate incremental and discriminant utility for PNEE.

METHODS

Veterans with vEEG-diagnosed PNEE (n = 166) or epileptic seizures (n = 92) completed self-report measures and brief neuropsychological evaluations during the 4-day vEEG hospitalization. Receiver operating characteristic (ROC) curves identified discriminating psychological tests and corresponding cut-scores (0.85 minimum specificity). Likelihood ratios from the remaining cut-scores were sequentially linked using the sample base rate of PNEE (64%) and alternative base rates (10%, 20%, 30%, 40%) to estimate posttest probabilities (PTP) of test combinations.

RESULTS

The Health Attitudes Survey, Health History Checklist, and Minnesota Multiphasic Personality Inventory-2-Restructured Form scales FBS-r, RC1, MLS, and NUC were identified as discriminating indicators of PNEE. Average PTPs were ≥90% when three or more indicators out of six administered were present at the sample base rate. Regardless of PNEE base rate, PTP for PNEE was ≥98% when all discriminating indicators were present and 92-99% when five of six indicators administered were present. PTPs were largely consistent with observed positive predictive values, particularly as indicators present increased.

SIGNIFICANCE

Aggregating psychological tests identified PNEE with a high degree of accuracy, regardless of PNEE base rate. Combining psychological tests may be useful for confirming the etiology of PNEE.

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.

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.

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.

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.

Diagnostic accuracy of paroxysmal spells: Clinical history versus observation

Our goal was to evaluate how accurate neurologists are at differentiating between different paroxysmal events based on clinical history versus observation of the spell in question. Forty-seven neurologists reviewed 12 clinical histories and videos of recorded events of patients admitted in the Epilepsy Monitoring Unit (EMU). They were asked to diagnose events as epileptic seizures, non-epileptic behavioral spells (NEBS), or other physiologic events as well as rate their confidence in their diagnosis. The median diagnostic accuracy for all paroxysmal events was 67% for clinical history and 75% for observation (p=.001). This was largely due to the difference in accuracy within the subgroup of patients with NEBS (67% history vs. 83% observation, p<.001). There were trends for higher diagnostic accuracy and increased inter-rater agreement with higher levels of training. Physicians with higher levels of training were more confident with diagnosis based on observation. In summary, reviewing videos of paroxysmal spells may improve diagnostic accuracy and enhance the evaluation of patients. Neurologists at all levels of training should encourage the recording and review of videos of recurrent spells to aid in medical decision-making especially when there is high concern that the spells in question are NEBS.

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.