Which patients with epilepsy are at risk for psychogenic nonepileptic seizures (PNES)? A multicenter case-control study.

OBJECTIVE: We sought to examine the clinical and electrographic differences between patients with combined epileptic (ES) and psychogenic nonepileptic seizures (PNES) and age- and gender-matched patients with ES-only and PNES-only. METHODS: Data from 138 patients (105 women [77%]), including 46 with PNES/ES (39±12years), 46 with PNES-only (39±11years), and 46 with ES-only (39±11years), were compared using logistic regression analysis after adjusting for clustering effect. RESULTS: In the cohort with PNES/ES, ES antedated PNES in 28 patients (70%) and occurred simultaneously in 11 (27.5%), while PNES were the initial presentation in only 1 case (2.5%); disease duration was undetermined in 6. Compared with those with ES-only, patients with PNES/ES had higher depression and anxiety scores, shorter-duration electrographic seizures, less ES absence/staring semiology (all p≤0.01), and more ES arising in the right hemisphere, both in isolation and in combination with contralateral brain regions (61% vs. 41%; p=0.024, adjusted for anxiety and depression) and tended to have less ES arising in the left temporal lobe (13% vs. 28%; p=0.054). Compared with those with PNES-only, patients with PNES/ES tended to show fewer right-hemibody PNES events (7% vs. 23%; p=0.054) and more myoclonic semiology (10% vs. 2%; p=0.073). CONCLUSIONS: Right-hemispheric electrographic seizures may be more common among patients with ES who develop comorbid PNES, in agreement with prior neurobiological studies on functional neurological disorders.

Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study.

BACKGROUND AND OBJECTIVES: Epilepsy surgery is often delayed. We previously developed machine learning (ML) models to identify candidates for resective epilepsy surgery earlier in the disease course. In this study, we report the prospective validation. METHODS: In this multicenter, prospective, longitudinal cohort study, random forest models were validated at a pediatric epilepsy center consisting of 2 hospitals and 14 outpatient neurology clinic sites and an adult epilepsy center with 2 hospitals and 27 outpatient neurology clinic sites. The models used neurology visit notes, EEG and MRI reports, visit patterns, hospitalizations, and medication, laboratory, and procedure orders to identify candidates for surgery. The models were trained on historical data up to May 10, 2019. Patients with an ICD-10 diagnosis of epilepsy who visited from May 11, 2019, to May 10, 2020, were screened by the algorithm and assigned surgical candidacy scores. The primary outcome was area under the curve (AUC), which was calculated by comparing scores from patients who underwent epilepsy surgery before November 10, 2020, against scores from nonsurgical patients. Nonsurgical patients' charts were reviewed to determine whether patients with high scores were more likely to be missed surgical candidates. Delay to surgery was defined as the time between the first visit that a surgical candidate was identified by the algorithm and the date of the surgery. RESULTS: A total of 5,285 pediatric and 5,782 adult patients were included to train the ML algorithms. During the study period, 41 children and 23 adults underwent resective epilepsy surgery. In the pediatric cohort, AUC was 0.91 (95% CI 0.87-0.94), positive predictive value (PPV) was 0.08 (0.05-0.10), and negative predictive value (NPV) was 1.00 (0.99-1.00). In the adult cohort, AUC was 0.91 (0.86-0.97), PPV was 0.07 (0.04-0.11), and NPV was 1.00 (0.99-1.00). The models first identified patients at a median of 2.1 years (interquartile range [IQR]: 1.2-4.9 years, maximum: 11.1 years) before their surgery and 1.3 years (IQR: 0.3-4.0 years, maximum: 10.1 years) before their presurgical evaluations. DISCUSSION: ML algorithms can identify surgical candidates earlier in the disease course. Even at specialized epilepsy centers, there is room to shorten the time to surgery. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a machine learning algorithm can accurately distinguish patients with epilepsy who require resective surgery from those who do not.