Design and implementation of electronic health record common data elements for pediatric epilepsy: Foundations for a learning health care system.

OBJECTIVE: Common data elements (CDEs) are standardized questions and answer choices that allow aggregation, analysis, and comparison of observations from multiple sources. Clinical CDEs are foundational for learning health care systems, a data-driven approach to health care focused on continuous improvement of outcomes. We aimed to create clinical CDEs for pediatric epilepsy. METHODS: A multiple stakeholder group (clinicians, researchers, parents, caregivers, advocates, and electronic health record [EHR] vendors) developed clinical CDEs for routine care of children with epilepsy. Initial drafts drew from clinical epilepsy note templates, CDEs created for clinical research, items in existing registries, consensus documents and guidelines, quality metrics, and outcomes needed for demonstration projects. The CDEs were refined through discussion and field testing. We describe the development process, rationale for CDE selection, findings from piloting, and the CDEs themselves. We also describe early implementation, including experience with EHR systems and compatibility with the International League Against Epilepsy classification of seizure types. RESULTS: Common data elements were drafted in August 2017 and finalized in January 2020. Prioritized outcomes included seizure control, seizure freedom, American Academy of Neurology quality measures, presence of common comorbidities, and quality of life. The CDEs were piloted at 224 visits at 10 centers. The final CDEs included 36 questions in nine sections (number of questions): diagnosis (1), seizure frequency (9), quality of life (2), epilepsy history (6), etiology (8), comorbidities (2), treatment (2), process measures (5), and longitudinal history notes (1). Seizures are categorized as generalized tonic-clonic (regardless of onset), motor, nonmotor, and epileptic spasms. Focality is collected as epilepsy type rather than seizure type. Seizure frequency is measured in nine levels (all used during piloting). The CDEs were implemented in three vendor systems. Early clinical adoption included 1294 encounters at one center. SIGNIFICANCE: We created, piloted, refined, finalized, and implemented a novel set of clinical CDEs for pediatric epilepsy.

Functional Connectivity as a Potential Mechanism for Language Plasticity.

BACKGROUND AND OBJECTIVES: Task fMRI is a clinical tool for language lateralization, but has limitations, and cannot provide information about network-level plasticity. Additional methods are needed to improve the precision of presurgical language mapping. We investigate language resting-state functional connectivity (RS fMRI; FC) in typically developing children (TD) and children with epilepsy. Our objectives were to (1) understand how FC components differ between TD children and those with epilepsy; (2) elucidate how the location of disease (frontal/temporal epilepsy foci) affects FC; and (3) investigate the relationship between age and FC. METHODS: Our sample included 55 TD children (mean age 12 years, range 7-18) and 31 patients with focal epilepsy (mean age 13 years, range 7-18). All participants underwent RS fMRI. Using a bilateral canonical language map as target, vertex-wise intrahemispheric FC map and interhemispheric FC map for each participant were computed and thresholded at top 10% to compute an FC laterality index (FCLI; [(L - R)/(L + R)]) of the frontal and temporal regions for both integration (intrahemispheric FC; FCLIi) and segregation (interhemispheric FC; FCLIs) maps. RESULTS: We found FC differences in the developing language network based on disease, seizure foci location, and age. Frontal and temporal FCLIi was different between groups (t[84] = 2.82, p < 0.01; t[84] = 4.68, p < 0.01, respectively). Frontal epilepsy foci had the largest differences from TD (Cohen d frontal FCLIi = 0.84, FCLIs = 0.51; temporal FCLIi = 1.29). Development and disease have opposing influences on the laterality of FC based on groups. In the frontal foci group, FCLIi decreased with age (r = -0.42), whereas in the temporal foci group, FCLIi increased with age (r = 0.40). Within the epilepsy group, increases in right frontal integration FCLI relates to increased right frontal task activation in our mostly left language dominant group (r = 0.52, p < 0.01). Language network connectivity is associated with higher verbal intelligence in children with epilepsy (r = 0.45, p < 0.05). DISCUSSION: These findings lend preliminary evidence that FC reflects network plasticity in the form of adaptation and compensation, or the ability to recruit support and reallocate resources within and outside of the traditional network to compensate for disease. FC expands on task-based fMRI and provides complementary and potentially useful information about the language network that is not captured using task-based fMRI alone.

Functional connectivity hemispheric contrast (FC-HC): A new metric for language mapping.

Development of a task-free method for presurgical mapping of language function is important for use in young or cognitively impaired patients. Resting state connectivity fMRI (RS-fMRI) is a task-free method that may be used to identify cognitive networks. We developed a voxelwise RS-fMRI metric, Functional Connectivity Hemispheric Contrast (FC-HC), to map the language network and determine language laterality through comparison of within-hemispheric language network connections (Integration) to cross-hemispheric connections (Segregation). For the first time, we demonstrated robustness and efficacy of a RS-fMRI metric to map language networks across five groups (total N = 243) that differed in MRI scanning parameters, fMRI scanning protocols, age, and development (typical vs pediatric epilepsy). The resting state FC-HC maps for the healthy pediatric and adult groups showed higher values in the left hemisphere, and had high agreement with standard task language fMRI; in contrast, the epilepsy patient group map was bilateral. FC-HC has strong but not perfect agreement with task fMRI and thus, may reflect related and complementary information about language plasticity and compensation.

Using EHRs to advance epilepsy care.

The improved use of Electronic Health Record (EHR) Systems provides an opportunity to improve the overall efficiency and quality of care of patients with epilepsy. Tools and strategies that may be incorporated into the use of EHRs include utilizing patient generated data, clinical decision support systems and natural language processing systems. Standardization of data from EHR systems may lead to improvement in clinical research through the creation of data collections and multi-center collaborations. Challenges to collaborative use of EHR Systems across centers include costs and the diversity of EHR systems.