Literature Collection

BACKGROUND: Clinical trials of remote patient monitoring (RPM) technology are well-suited to remote studies, for which patients complete key procedures online. However, remote digital health studies often suffer from low enrollment and retention, threatening the successful achievement of study outcomes and wasting resources and time. Recruiting patients from a large integrated health system offers a greater potential pool of participants for enrollment, which can increase the likelihood of successful study completion. OBJECTIVE: This study describes enrollment and retention outcomes for a remote digital health study of an RPM device conducted in collaboration with researchers from the Veterans Health Administration (VA). The VA is the largest integrated health system in the United States, with 9 million enrollees who are, as a group, older and with more medical and mental health comorbidities than the civilian population. METHODS: We aimed to enroll 200 VA patients for a clinical study of a cellular-enabled, handheld, multisensor device that captures multiple health parameters and transmits data to a cloud-based dashboard for viewing by clinicians. Eligible patients were hospitalized with COVID-19 within 3-6 months before enrollment and had one of 6 pre-existing medical comorbidities. Potentially eligible patients were identified using the VA Corporate Data Warehouse. Every 3 weeks, up to 1000 potentially eligible patients were mailed a recruitment letter. All study tasks, including obtaining informed consent, device training and troubleshooting, and handling study-related questions, were completed online and by telephone. Device and survey data were combined with VA clinical and utilization data to develop a predictive algorithm for clinical decompensation. The geographic distribution of enrolled patients was mapped by county. Demographic and health characteristics of nonenrolled versus enrolled, and of completers versus noncompleters were compared using t tests and chi-square tests as appropriate. Reasons for noncompletion were summed. Multivariate logistic regression was used to evaluate variables associated with enrolling versus nonenrolling, and completing versus noncompleting. RESULTS: Of the 7714 who were mailed a study invitation, 560 were screened. Of the screened patients, 203 were enrolled (2.9% enrollment yield) and 166 completed the study (82% retention rate). Enrolled patients were broadly distributed across the United States. Among those enrolled, completers and noncompleters were similar except for a slightly higher proportion of patients with hypertension among completers. The most common reason for noncompletion of the study was that participants were unable to be contacted for study tasks. CONCLUSIONS: Remote digital health studies are increasingly common, but inadequate enrollment often results in failed studies. Recruiting patients through the VA enables access to a very large population of potentially eligible patients and can help ensure that clinical trials reach targets for enrollment and completion. TRIAL REGISTRATION: ClinicalTrials.gov NCT05713266; https://clinicaltrials.gov/study/NCT05713266.

Epilepsy affects ~50 million people worldwide and is associated with increased psychiatric comorbidities, including depression, anxiety, psychosis and suicidality. Despite this, current epilepsy management primarily focusses on seizure control, potentially overlooking mental health concerns. This article explores the challenges of integrating psychiatric care into epilepsy treatment and proposes solutions for a more holistic approach. Using a consensus development panel method, a multidisciplinary team of neurologists, psychiatrists and a lived-experience expert identified key challenges to optimising the mental health of people living with epilepsy, such as healthcare system fragmentation, underdiagnosis of mental health conditions and inadequate resources. Among the proposed solutions, the need for routine mental health screening, interdisciplinary support and collaboration, and increased research into the neuropsychiatric aspects of epilepsy were highlighted. A shift from a seizure-centric model to a patient-centred approach is advocated, emphasising biopsychosocial care and improved access to psychiatric services. We also discuss prospective practical strategies to tackle the issues identified, including collaborative care models, structured decision trees and AI-driven screening tools, to enhance diagnosis and treatment. Addressing these challenges through systemic change, research investment and service innovation should significantly improve the care and quality of life for individuals with an epilepsy and co-occurring mental health disorders.

In this concept paper, we introduce epilepsy-heart syndrome as a shared burden of illness between epilepsy and cardiac disorders. This pragmatic definition is agnostic of which condition came first (the epilepsy or the cardiac disorder), recognising that these conditions can each serve as a risk factor for the other owing to a bidirectional relationship that exists between the brain and the heart. To provide clinical context, we include ictal asystole as an example phenotype of epilepsy-heart syndrome. We highlight evidence of patients with ictal asystole coming to harm owing to the failure of integrated care between neurology and cardiology. This underscores epilepsy-heart syndrome as an unmet need for collaborative care between neurology and cardiology. To address this, we propose a framework for integrated care, drawing upon our own centre's recently established and successful multidisciplinary team meeting (MDT) between neurologists and cardiologists, our joint cardiology-neurology PhD programme, and our work developing a joint national guideline on ictal asystole management between the Association of British Neurologists (ABN) and the British Heart Rhythm Society (BHRS).

BACKGROUND: The National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommends the paper-based or computerized Alcohol Symptom Checklist to assess alcohol use disorder (AUD) symptoms in routine care when patients report high-risk drinking. However, it is unknown whether Alcohol Symptom Checklist response characteristics differ when it is administered online (eg, remotely via an online electronic health record [EHR] patient portal before an appointment) versus in clinic (eg, on paper after appointment check-in). OBJECTIVE: This study evaluated the psychometric performance of the Alcohol Symptom Checklist when completed online versus in clinic during routine clinical care. METHODS: This cross-sectional, psychometric study obtained EHR data from the Alcohol Symptom Checklist completed by adult patients from an integrated health system in Washington state. The sample included patients who had a primary care visit in 2021 at 1 of 32 primary care practices, were due for annual behavioral health screening, and reported high-risk drinking on the behavioral health screen (Alcohol Use Disorder Identification Test-Consumption score ≥7). After screening, patients with high-risk drinking were typically asked to complete the Alcohol Symptom Checklist-an 11-item questionnaire on which patients self-report whether they had experienced each of the 11 AUD criteria listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) over a past-year timeframe. Patients could complete the Alcohol Symptom Checklist online (eg, on a computer, smartphone, or tablet from any location) or in clinic (eg, on paper as part of the rooming process at clinical appointments). We examined sample and measurement characteristics and conducted differential item functioning analyses using item response theory to examine measurement consistency across these 2 assessment modalities. RESULTS: Among 3243 patients meeting eligibility criteria for this secondary analysis (2313/3243, 71% male; 2271/3243, 70% White; and 2014/3243, 62% non-Hispanic), 1640 (51%) completed the Alcohol Symptom Checklist online while 1603 (49%) completed it in clinic. Approximately 46% (752/1640) and 48% (764/1603) reported ≥2 AUD criteria (the threshold for AUD diagnosis) online and in clinic (P=.37), respectively. A small degree of differential item functioning was observed for 4 of 11 items. This differential item functioning produced only minimal impact on total scores used clinically to assess AUD severity, affecting total criteria count by a maximum of 0.13 criteria (on a scale ranging from 0 to 11). CONCLUSIONS: Completing the Alcohol Symptom Checklist online, typically prior to patient check-in, performed similarly to an in-clinic modality typically administered on paper by a medical assistant at the time of the appointment. Findings have implications for using online AUD symptom assessments to streamline workflows, reduce staff burden, reduce stigma, and potentially assess patients who do not receive in-person care. Whether modality of DSM-5 assessment of AUD differentially impacts treatment is unknown.