A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy.

Epilepsy affects ~2-3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy.

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research.

OBJECTIVE: The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies. MATERIALS AND METHODS: We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy. RESULTS: Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology. DISCUSSION: Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards. CONCLUSION: The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.

Incidence and prevalence of treated epilepsy among poor health and low-income Americans.

OBJECTIVES: To determine the incidence and prevalence of treated epilepsy in an adult Medicaid population. METHODS: We performed a retrospective, dynamic cohort analysis using Ohio Medicaid claims data between 1992 and 2006. Individuals aged 18-64 years were identified as prevalent cases if they had ≥2 claims of epilepsy (ICD-9-CM: 345.xx) or ≥3 claims of convulsion (ICD-9-CM: 780.3 or 780.39) and ≥2 claims of antiepileptic drugs. Incident cases were required to have no epilepsy or convulsion claims for ≥5 years before epilepsy diagnosis. Subjects were determined as having preexisting disability and/or comorbid conditions, including brain tumor, depression, developmental disorders, migraine, schizophrenia, stroke, and traumatic brain injury, when at least one of these conditions occurred before epilepsy onset. RESULTS: There were 9,056 prevalent cases of treated epilepsy in 1992-2006 and 1,608 incident cases in 1997-2006. The prevalence was 13.2/1,000 (95% confidence interval, 13.0-13.5/1,000). The incidence was 362/100,000 person-years (95% confidence interval, 344-379/100,000 person-years). The incidence and prevalence were significantly higher in men, in older people, in blacks, and in people with preexisting disability and/or comorbid conditions. The most common preexisting conditions in epilepsy subjects were depression, developmental disorders, and stroke, whereas people with brain tumor, traumatic brain injury, and stroke had the higher risk of developing epilepsy. CONCLUSIONS: The Medicaid population has a high incidence and prevalence of epilepsy, in an order of magnitude greater than that reported in the US general population. This indigent population carries a disproportionate amount of the epilepsy burden and deserves more attention for its health care needs and support services.