Predictive modeling of suicidal ideation in patients with epilepsy.

OBJECTIVE: The prevalence of suicide in the United States has seen an increasing trend and is responsible for 1.6% of all mortality nationwide. Although suicide has the potential to broadly impact the entire population, it has a substantially increased prevalence in persons with epilepsy (PWE), despite many of these individuals consistently seeing a health care provider. The goal of this work is to predict the development of suicidal ideation (SI) in PWE using machine learning methodology such that providers can be better prepared to address suicidality at visits where it is likely to be prominent. METHODS: The current study leverages data collected at an epilepsy clinic during patient visits to predict whether an individual will exhibit SI at their next visit. The data used for prediction consisted of patient responses to questions about the severity of their epilepsy, issues with memory/concentration, somatic problems, markers for mental health, and demographic information. A machine learning approach was then applied to predict whether an individual would display SI at their following visit using only data collected at the prior visit. RESULTS: The modeling approach allowed for the successful prediction of an individual's passive and active SI severity at the following visit (r = .42, r = .39) as well as the presence of SI regardless of severity (area under the curve [AUC] = .82, AUC = .8). This shows that the model was successfully able to synthesize the unique combination of an individual's responses to important questions during a clinical visit and utilize that information to indicate whether that individual will exhibit SI at their next visit. SIGNIFICANCE: The results of this modeling approach allow the health care team to be prepared, in advance of a clinical visit, for the potential reporting of SI. By allowing the necessary support to be prepared ahead of time, it can be better integrated at the point of care, where patients are most likely to follow up on potential referrals or treatment.

Effectiveness of a Self-Management Program to Improve Cognition and Quality of Life in Epilepsy: A Pragmatic, Randomized, Multicenter Trial.

BACKGROUND AND OBJECTIVES: We conducted a multisite, pragmatic replication trial at 4 New England epilepsy centers to determine the effectiveness of Home-Based Self-Management and Cognitive Training Changes Lives (HOBSCOTCH) in a real-world setting and to assess feasibility of a virtual intervention. METHODS: HOBSCOTCH is an 8-session intervention addressing cognitive impairment and quality of life (QoL) for people with epilepsy (PWE). Participants were recruited from epilepsy centers in 4 states and block-randomized into the following groups: in-person HOBSCOTCH (H-IP), virtual HOBSCOTCH (H-V), and waitlist control. Outcome measures were assessed for all groups at baseline, 3 months, and 6 months; intervention groups received long-term follow-up at 9 and 12 months. RESULTS: A total of 108 participants were recruited, of whom 85 were included in this analysis (age at baseline 47.5 ± 11.5 years; 68% female). Participants completing the in-person intervention (H-IP) had a 12.4-point improvement in QoL score compared with controls (p < 0.001). Pairwise comparisons found a 6.2-point treatment effect for subjective cognition in the H-IP group (p < 0.001). There were no meaningful group differences in objective cognition or health care utilization at any time points and the treatment effect for QoL diminished by 6 months. The virtual intervention demonstrated feasibility but did not significantly improve outcomes compared with controls. Within-group analysis found improvements in QoL for both H-V and H-IP. DISCUSSION: This study replicated the effectiveness of the HOBSCOTCH program in improving QoL for PWE. The study was conducted prior to the COVID-19 pandemic, but the distance-delivered intervention may be particularly well-suited for the current environment. Future research will explore modifications designed to improve the efficacy of H-V and the sustainability of HOBSCOTCH's treatment effect. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov (NCT02394509). CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in-person HOBSCOTCH delivery improved subjective measures of cognition in persons with epilepsy.

A pilot study assessing the feasibility and acceptability of Project UPLIFT adapted for patients with psychogenic nonepileptic seizures.

BACKGROUND: People with psychogenic nonepileptic seizures (PNES) are at elevated risk of multiple psychiatric comorbidities. Current treatment gaps highlight the need for time-limited, distance-delivered interventions that can be cost-effectively delivered to patients with PNES. Project UPLIFT is a self-management program addressing mood problems in epilepsy that has not previously been adapted for PNES. The purpose of this study was to assess the feasibility and acceptability of UPLIFT for patients with PNES. METHODS: Project UPLIFT was minimally adapted by a multidisciplinary research group at Dartmouth-Hitchcock Medical Center. Participants were recruited into a nonrandomized pilot study and were assessed at baseline, after completing the intervention, and at 1-month follow-up. RESULTS: The intervention was adapted and delivered without additional modification. A total of eight participants enrolled in the trial, and six participated in the first session. All six participants who started UPLIFT completed the intervention, with a 98% attendance record across the eight sessions. At follow-up, 100% reported that UPLIFT provided useful tools for daily life. All participants continued using UPLIFT after completing the program, and 83% felt it helped with seizure management. Preliminary results provide proof-of-concept for future efficacy trials. CONCLUSION: This study provides initial support for the feasibility and acceptability of Project UPLIFT, minimally adapted for patients with PNES. As a time-limited program that can be distance-delivered to groups of patients, UPLIFT may be well-suited for the healthcare environment brought on by the COVID-19 pandemic.