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

Reuber, Markus; Chen, Min; Jamnadas-Khoda, Jennifer; Broadhurst, Mark; Wall, Melanie M.; Grünewald, Richard A.; Howell, Stephen; Koepp, Matthias J.; Parry, Steve W.; Sisodiya, Sanjay M.; Walker, Matthew C.; Hesdorffer, Dale C.

doi:10.1212/wnl.0000000000002948

Cited by 64 publications

(44 citation statements)

References 35 publications

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“…Instead, our analysis suggests that patient-and witness-reported behaviors only moderately predict the likelihood of psychogenic seizures. This performance was better than some analyses where witness reports were no better than chance [17] and was statistically similar to other algorithms that focused on patient, witness, or video-EEG observed peri-ictal behaviors [17,[19][20][21]30]. The prospective AUCs were comparable to the AUC of neurology trainees and internal medicine physicians who viewed videos of patients' seizures (76% and 73% versus 76% and 72%, respectively [23]).…”

Section: Individual-level Predictionssupporting

confidence: 61%

“…Therefore, our models may triage patients towards more definitive diagnostic testing. One theme observed in other diagnostic scores is that incorporating information about medical and psychiatric comorbidities as well as trauma history may increase the predictive performance [18,20,31,32]. Therefore, this evaluation should be considered in the appropriate clinical context.…”

Section: Individual-level Predictionsmentioning

confidence: 99%

“…Contrary to widely held belief, PNES commonly manifest with features that are not within the canonical feature list and each patients' peri-ictal behavior can be stereotyped [14][15][16]. Decision algorithms to identify PNES based on peri-ictal behavior and other factors have over 85% accuracy on retrospective populations of up to 227 patients [13,[17][18][19][20][21]. However, recent reports suggest that patient-and witness-reported ictal behavior may not be reliable and that the accuracy of ictal-behavior-based diagnosis is highly dependent on the level of clinical training in seizures [17,22,23].…”

mentioning

confidence: 92%

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

Kerr

Chau

Janio

et al. 2019

Seizure

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A B S T R A C TPurpose: 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 videoelect 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.

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Section: Individual-level Predictionssupporting

confidence: 61%

Section: Individual-level Predictionsmentioning

confidence: 99%

mentioning

confidence: 92%

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

Kerr

Chau

Janio

et al. 2019

Seizure

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“…Another study achieved similar levels of differential diagnostic accuracy between epilepsy and PNES (77% of cases correctly classified) with a more detailed questionnaire focusing on a wider range of self-reportable symptoms associated with transient loss of consciousness, although the questionnaire differentiated better between syncope and epilepsy (91%) and between syncope and PNES (94% [47]. Another study focusing on the relationship between different types of symptoms in the PNES group included in the comparative research described above found that a greater recall of ictal panic symptoms is associated with more common dissociative experiences [48].…”

Section: Subjective Experiencesmentioning

confidence: 82%

Understanding psychogenic nonepileptic seizures—Phenomenology, semiology and the Integrative Cognitive Model

Reuber

Brown

2017

Seizure

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“…Six cases were excluded because data were missing or incomplete, and a further 16 cases were excluded because the patients had received a diagnosis other than epilepsy or PNES and previous linguistic findings have only been demonstrated within these two diagnostic categories. Thirteen of those excluded with other diagnoses were found to have experienced syncope; a condition which has previously been shown to be readily distinguishable from seizures (either epileptic or nonepileptic) with a short series of yes/no questions [25][26][27].…”

Section: Datamentioning

confidence: 99%

Neurologists can identify diagnostic linguistic features during routine seizure clinic interactions: results of a one-day teaching intervention

Jenkins

Cosgrove

Chappell

et al. 2016

Epilepsy & Behavior

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The diagnostic distinction between epilepsy and psychogenic nonepileptic seizures (PNES) can be challenging. Previous studies have demonstrated that conversation analysts can identify linguistic and interactional features in transcripts and recordings of interviews with seizure patients that reliably distinguish between epilepsy and PNES. In this study, ten senior neurology trainees took part in a one-day intervention workshop about linguistic and interactional differences in the conversation behaviour of patients with epilepsy and those with PNES. Participants were familiarised with a 12-item questionnaire designed to capture their conversational observations immediately after talking to a patient with seizures. After the intervention, 55 initial outpatient visits of patients referred to seizure clinics were video and audio recorded. All medical diagnoses were confirmed two years after initial presentation on the basis of a chart review (including MRI and EEG findings) by a fully trained epilepsy expert. Post-visit questionnaires relating to patients confirmed to have epilepsy (n=20) or PNE" Dinteractional observations differed significantly between the epilepsy and PNES groups. Receiver operating curve analysis showed that a summation scale based on items demonstrating significant between-group differences correctly classified 81.8% of patients as having epilepsy or PNES. This study shows that a brief Conversation Analytic teaching intervention can enable neurologists to identify linguistic and interactional features supporting the differentiation of epilepsy and PNES as diagnostic accuracy.

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

Abstract: 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.

Cited by 64 publications

References 35 publications

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

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

Understanding psychogenic nonepileptic seizures—Phenomenology, semiology and the Integrative Cognitive Model

Neurologists can identify diagnostic linguistic features during routine seizure clinic interactions: results of a one-day teaching intervention

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