Identifying psychogenic seizures through comorbidities and medication history

Abstract

Summary 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 seizure-like 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% CI 72–84%) and AUC 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 non-metastatic neoplasm (p<0.01). The population-level analysis suggested that patients with mixed PNES plus ES may be a distinct population from patients with either condition alone. Significance An accurate patient-reported past 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.

Potential use of leukocytosis and anion gap elevation in differentiating psychogenic nonepileptic seizures from epileptic seizures

“…9 Ammonia and lactate required narrow time windows (<1 hour, and 2 hours, respectively) to observe the changes. 13,14 Further studies of investigating the combination of the preceding methods and the concomitant AG and WBC cell biomarker changes for increasing the accuracy of determination of PNES vs ES would be interesting to pursue. Recent studies have shown that surface electromyography might help distinguish between convulsive PNES and ES.…”

“…12 In addition, a scoring system of clinical history and comorbidities has been proposed to diagnosis PNES, which achieved promising results. 13,14 Further studies of investigating the combination of the preceding methods and the concomitant AG and WBC cell biomarker changes for increasing the accuracy of determination of PNES vs ES would be interesting to pursue.…”

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

“…In particular, seizure duration and frequency can provide valuable information in differentiating PNES from ES. Our mediocre accuracy and predictive values suggested that peri-ictal behaviors should be used in combination with other diagnostic information like comorbidities, medications, clinical history and neurodiagnostics [5,18,20,23,32,35,36]. For the diagnostic certainty to rise above "probable PNES," the patient's seizures need to be captured during EEG or directly observed directly by a clinician familiar with seizure disorders.…”

“…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.…”

“…Additionally, the peri-ictal behavior of both ES and PNES were heterogeneous within each diagnostic group, as evidenced by a mildly improved predictive performance of decision forests relative to logistic regression. Identification of these subpopulations may be improved further by considering patients' comorbidities, medications and clinical history [18,32,35,36].…”