Multidisciplinary Care May Help to Reduce Disparities Associated With Race in Youth With Psychogenic Nonepileptic Seizures.

BACKGROUND: Psychogenic nonepileptic seizures (PNES) are a common type of functional neurological disorder in which patients experience seizurelike episodes. Health disparities based on race and socioeconomics, documented in children with epilepsy and adults with PNES, have not been reported in children and adolescents with PNES. We hypothesize that disparities exist in this population, which impact overall care and therefore influence outcomes. METHODS: We retrospectively analyzed youth referred to our multidisciplinary clinic from 2018 to 2020. All patient charts were screened by social work before the visit to identify potential barriers to care, and a nurse conducted follow-up calls. Patients' race was identified from the electronic health record and compared with several variables. Outcomes were collected via phone follow-up. Descriptive statistics were produced, and comparisons between white patients and patients of other races were completed using Fisher exact tests and multivariable logistic regressions. RESULTS: During the study period, 237 patients were eligible for the analysis. Sixty-eight patients (29%) identified as a race other than white. Only 60%, 56%, and 40% of the cohort were reached for follow-up at one, three, and 12 months, respectively. In general, outcomes were similar between racial groups; however, we found that patients of nonwhite race were more likely to receive support from social work due to barriers identified in screening (P = 0.045). CONCLUSIONS: Health disparities based on race may exist in youth with PNES. A multidisciplinary clinic including social work may help mitigate barriers leading to more equitable care and similar outcomes for white and nonwhite youth with PNES.

Predicting Patient No-Shows in an Academic Pediatric Neurology Clinic.

Background: No-shows can negatively affect patient care. Efforts to predict high-risk patients are needed. Previously, our epilepsy clinic identified patients with 2 or more no-shows or late cancelations in the past 18 months as being at high risk for no-shows. Our objective was to develop a model to accurately predict the risk of no-shows among patients with epilepsy seen at our neurology clinic. Methods: Using electronic health record data, we developed a least absolute shrinkage and selection operator (LASSO)-regularized logistic regression model to predict no-shows and compared its performance with our neurology clinic's above-mentioned ad hoc rule. Results: The ad hoc rule identified 13% of patients seen at our neurology clinic as high-risk patients for no-shows and resulted in a positive predictive value of 38%. In comparison, our LASSO model resulted in a positive predictive value of 48%. Our LASSO model identified that lack of private insurance, inactive Epic MyChart, greater past no-show rates, fewer appointment changes before the appointment date, and follow-up appointments were more likely to result in no-shows. Conclusions: Our LASSO model outperformed the ad hoc rule used by our neurology clinic in predicting patients at high risk for no-shows. Social workers can use the no-show risk scores generated by our LASSO model to prioritize high-risk patients for targeted intervention to reduce no-shows at our neurology clinic.