Differentiating PNES from epileptic seizures using conversational analysis on French patients: A prospective blinded study

Drawing the abdominal pain: A powerful tool to distinguish between organic and functional abdominal pain

OBJECTIVES

Abdominal pain (AP) in children imposes a large economic burden on the healthcare system. Currently, there are no reliable diagnostic tools to differentiate between organic and functional disorders. We hypothesized from previous research that the analysis of patients' graphic expression of subjective symptoms as well as their interactional behavior adds new ways to differentiate between functional and organic AP.

METHODS

Conversation analyses of physician-patient-encounters and graphical expression of AP-based pain were performed.

RESULTS

Twenty-two interactions were recorded and analyzed. Fifteen children were diagnosed with organic AP and seven with functional AP. We found marked differences between children with organic and functional AP. For example, all 15 children with organic AP saw the task of drawing a picture of the pain during the interview as a duty, whereas the seven children with functional AP took this as an opportunity to provide detailed descriptions about the nature of the pain, the circumstances, and how the AP impaired their quality of life.

CONCLUSION

Analysis of patients' interaction strategies in response to the painting task provides relevant clues as to whether AP is functional or requires further workup for organic causes.

Predicting the cause of seizures using features extracted from interactions with a virtual agent

OBJECTIVE

A clinical decision tool for Transient Loss of Consciousness (TLOC) could reduce currently high misdiagnosis rates and waiting times for specialist assessments. Most clinical decision tools based on patient-reported symptom inventories only distinguish between two of the three most common causes of TLOC (epilepsy, functional /dissociative seizures, and syncope) or struggle with the particularly challenging differentiation between epilepsy and FDS. Based on previous research describing differences in spoken accounts of epileptic seizures and FDS seizures, this study explored the feasibility of predicting the cause of TLOC by combining the automated analysis of patient-reported symptoms and spoken TLOC descriptions.

METHOD

Participants completed an online web application that consisted of a 34-item medical history and symptom questionnaire (iPEP) and spoken interaction with a virtual agent (VA) that asked eight questions about the most recent experience of TLOC. Support Vector Machines (SVM) were trained using different combinations of features and nested leave-one-out cross validation. The iPEP provided a baseline performance. Inspired by previous qualitative research three spoken language based feature sets were designed to assess: (1) formulation effort, (2) the proportion of words from different semantic categories, and (3) verb, adverb, and adjective usage.

RESULTS

76 participants completed the application (Epilepsy = 24, FDS = 36, syncope = 16). Only 61 participants also completed the VA interaction (Epilepsy = 20, FDS = 29, syncope = 12). The iPEP model accurately predicted 65.8 % of all diagnoses, but the inclusion of the language features increased the accuracy to 85.5 % by improving the differential diagnosis between epilepsy and FDS.

CONCLUSION

These findings suggest that an automated analysis of TLOC descriptions collected using an online web application and VA could improve the accuracy of current clinical decisions tools for TLOC and facilitate clinical stratification processes (such as ensuring appropriate referral to cardiological versus neurological investigation and management pathways).

Metaphoric language in the differential diagnosis of epilepsy and psychogenic non-epileptic seizures: Time to move forward

Conversation analysis (CA) to identify metaphoric language (ML) has been proposed as a tool for the differential diagnosis of epileptic (ES) and psychogenic nonepileptic seizures (PNES). However, the clinical relevance of metaphoric conceptualizations is not clearly defined. The current study aims to investigate the ML utilized by individuals with ES and PNES in a pulled multi-country sample. Two blinded researchers examined the transcripts and videos of 54 interviews of individuals (n = 29, Italy; n = 11, USA; n = 14, Russia) with ES and PNES, identifying the patient-seizure relationship representative of the patient's internal experience. The diagnoses were based on video-EEG. Metaphors were classified as "Space/place", "External force", "Voluntary action", and "Other". A total of 175 metaphors were identified. No differences between individuals with ES and PNES were found in metaphoric occurrence (χ2 (1, N = 54) = 0.07; p = 0.74). No differences were identified when comparing the types of metaphors utilized by participants with ES and those with PNES. Patients with PNES and ES did not demonstrate differences in terms of occurrence and categories in ML. Therefore, researchers and clinicians should carefully consider the use of metaphor conceptualizations for diagnostic purposes.

Differentiating between epileptic and functional/dissociative seizures using semantic content analysis of transcripts of routine clinic consultations

The common causes of Transient Loss of Consciousness (TLOC) are syncope, epilepsy, and functional/dissociative seizures (FDS). Simple, questionnaire-based decision-making tools for non-specialists who may have to deal with TLOC (such as clinicians working in primary or emergency care) reliably differentiate between patients who have experienced syncope and those who have had one or more seizures but are more limited in their ability to differentiate between epileptic seizures and FDS. Previous conversation analysis research has demonstrated that qualitative expert analysis of how people talk to clinicians about their seizures can help distinguish between these two TLOC causes. This paper investigates whether automated language analysis - using semantic categories measured by the Linguistic Inquiry and Word Count (LIWC) toolkit - can contribute to the distinction between epilepsy and FDS. Using patient-only talk manually transcribed from recordings of 58 routine doctor-patient clinic interactions, we compared the word frequencies for 21 semantic categories and explored the predictive performance of these categories using 5 different machine learning algorithms. Machine learning algorithms trained using the chosen semantic categories and leave-one-out cross-validation were able to predict the diagnosis with an accuracy of up to 81%. The results of this proof of principle study suggest that the analysis of semantic variables in seizure descriptions could improve clinical decision tools for patients presenting with TLOC.

The clinical management of functional neurological disorder: A scoping review of the literature

OBJECTIVE

To date, there have been no reviews bringing together evidence on the clinical management of functional neurological disorder (FND) and patients', caregivers', and healthcare workers' experiences. This review provides an overview of the literature focused on the clinical management of FND.

METHODS

Four databases were searched, and a consultation exercise was conducted to retrieve relevant records dated from September 2010 to September 2020. Articles documenting diagnostic methods, treatments or interventions, or the experiences and perspectives of patients and healthcare workers in the clinical management of FND were included.

RESULTS

In total, 2756 records were retrieved, with 162 included in this review. The diagnostic methods reported predominantly included positive clinical signs, v-EEG and EEG. Psychological treatments and medication were the most reported treatments. Mixed findings of the effectiveness of CBT were found. Haloperidol, physiotherapy and scripted diagnosis were found to be effective in reducing FND symptoms. Several facilitators and barriers for patients accessing treatment for FND were reported.

CONCLUSION

The literature describing the clinical management for FND has increased considerably in recent times. A wide variety of diagnostic tools and treatments and interventions were found, with more focus being placed on tests that confirm a diagnosis than 'rule-out' tests. The main treatment type found in this review was medication. This review revealed that there is a lack of high-quality evidence and reflects the need for official clinical guidelines for FND, providing healthcare workers and patients the support needed to navigate the process to diagnose and manage FND.

Differential diagnosis between epileptic and psychogenic nonepileptic seizures through conversational analysis: A blinded prospective study in the Russian language

The current study examined the validity of conversational analysis (CA) in Russian patients with seizures, using a scoring table for the Simplified Linguistic Evaluation (SLE). The study sample was composed of 12 adult participants suffering either from epilepsy (ES) or psychogenic nonepileptic seizures (PNES) recruited in the Moscow Research and Clinical Center for Neuropsychiatry. Definitive diagnosis was established only after a habitual event was captured onvEEG. All participants with PNES or ES and at least one mental disorder underwent a 20-minute-long interview recorded on video. The interview then was evaluated by the external blinded physician already experienced in CA. Finally, that physician filled the SLE, consisting of 5 items analyzing the main characteristics of patient narrations. A score of ≥12 suggested a diagnosis of ES, while a score of <12 suggested a diagnosis of PNES. The blinded evaluator correctly identified 11 out of 12 cases. The concordance between the vEEG diagnosis and the CA diagnostic hypothesis was 91.67%. The sensitivity of the scoring table was 100%, while the specificity was 80%. The positive and the negative predictive values were, respectively, 87.5% and 100%. Our results suggested that the differences in seizure descriptions between patients with PNES and patients with ES are similar across Indo-European language family and are independent of psychiatric comorbidity.

Dissoziative Anfälle frühzeitig erkennen

Wenn dissoziative Anfälle, also psychogene, nichtepileptische Anfälle als epileptische Anfälle fehlgedeutet werden, führt dies zu frustraner medikamentöser Therapie und verzögert die Einleitung der erforderlichen psychotherapeutischen Maßnahmen. Folgende Anfallssymptome sollten an dissoziative Anfälle denken lassen: unrhythmisches, wildes Hin- und Herbewegen des Kopfes oder der Extremitäten, geschlossene Augen, lange Dauer und undulierender Verlauf. Ein unauffälliges Elektroenzephalogramm (EEG) spricht für dissoziative Anfälle, aber erst ein negativer EEG-Befund während eines Anfalls ist beweisend. Im Arztgespräch ist es entscheidend, dass die Betroffenen die Möglichkeit bekommen, frei zu schildern. Betroffene mit dissoziativen Anfällen zeigen dann Besonderheiten, die als Diagnosekriterien genutzt werden sollten: Sie fokussieren auf Begleitumstände und lassen in ihren Schilderungen den Moment des Bewusstseinsverlusts aus. Sie machen eher allgemeine Angaben und unterscheiden einzelne Anfälle kaum.

Psychogenic non-epileptic seizures (PNES) in the context of concurrent epilepsy – making the right diagnosis

Epilepsy is a risk factor for the development of psychogenic non-epileptic seizures (PNES) and comorbid epilepsy is recognized as a comorbidity in about 10–30% of patients with PNES. The combination of epileptic and nonepileptic seizures poses a particular diagnostic challenge. In patients with epilepsy, additional PNES may be suspected on the basis of their typical semiology. The possibility of additional PNES should also be considered if seizures fail to respond to antiepileptic drug treatment, in patients with frequent emergency admissions with seizures and in those who develop new types of seizures. The description of semiological details by patients and witnesses can suggest additional PNES. Home video recordings can support an initial diagnosis, however, especially in patients with mixed seizure disorders it is advisable to seek further diagnostic confirmation by capturing all habitual seizure types with video-EEG. The clinical features of PNES associated with epilepsy are similar to those in isolated PNES disorders and include longer duration, fluctuating course, asynchronous movements, pelvic thrusting, side-to-side head or body movement, persistently closed eyes and mouth, ictal crying, recall of ictal experiences and absence of postictal confusion. PNES can also present as syncope-like episodes with unresponsiveness and reduced muscle tone. There is no unique epileptological or brain pathology profile putting patients with epilepsy at risk of additional PNES. However, patients with epilepsy and PNES typically have lower educational achievements and higher levels of psychiatric comorbidities than patients with epilepsy alone. Psychological trauma, including sexual abuse, appears to be a less relevant aetiological factor in patients with mixed seizure disorders than those with isolated PNES, and the gender imbalance (i.e. the greater prevalence in women) is less marked in patients with PNES and additional epilepsy than those with PNES alone. PNES sometimes develop after epilepsy surgery. A diagnosis of ‘known epilepsy’ should never be accepted without (at least brief) critical review. This narrative review summarises clinical, electrophysiological and historical features that can help identify patients with epilepsy and additional PNES.

[Psychogenic non-epileptic seizures]

The review provides epidemiological data and discuss the associated burden of non-epileptic seizures (PNES). Data on the prevalence, socio-demographic and clinical risk factors for the development of PNES are presented. The hypotheses of the PNES origin, including the contribution of psychological trauma, are considered. We also describe contemporary methods for differential diagnosis of epileptic seizures and PNES, including biomarkers and the use of diagnostic questionnaires. Special attention is given to the issues of the psychiatric comorbidity of PNES.

Feasibility of using an automated analysis of formulation effort in patients' spoken seizure descriptions in the differential diagnosis of epileptic and nonepileptic seizures

OBJECTIVE

There are three common causes of Transient Loss of Consciousness (TLOC), syncope, epileptic and psychogenic nonepileptic seizures (PNES). Many individuals who have experienced TLOC initially receive an incorrect diagnosis and inappropriate treatment. Whereas syncope can be distinguished relatively easily with a small number of "yes"/"no" questions, the differentiation of the other two causes of TLOC is more challenging. Previous qualitative research based on the methodology of Conversation Analysis has demonstrated that the descriptions of epileptic seizures contain more formulation effort than accounts of PNES. This research investigates whether features likely to reflect the level of formulation effort can be automatically elicited from audio recordings and transcripts of speech and used to differentiate between epileptic and nonepileptic seizures.

METHOD

Verbatim transcripts of conversations between patients and neurologists were manually produced from video and audio recordings of 45 interactions (21 epilepsy and 24 PNES). The subsection of each transcript containing the person's account of their first seizure was manually extracted for the analysis. Seven automatically detectable features were designed as markers of formulation effort. These features were used to train a Random Forest machine learning classifier.

RESULT

There were significantly more hesitations and repetitions in descriptions of epileptic than nonepileptic seizures. Using a nested leave-one-out cross validation approach, 71% of seizures were correctly classified by the Random Forest classifier.

DISCUSSION

This pilot study provides proof of principle that linguistic features that have been automatically extracted from audio recordings and transcripts could be used to distinguish between epileptic seizures and PNES and thereby contribute to the differential diagnosis of TLOC. Future research should explore whether additional observations can be incorporated into a diagnostic stratification tool and compare the performance of these features when they are combined with additional information provided by patients and witnesses about seizure manifestations and medical history.