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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 publ...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428292/ https://www.ncbi.nlm.nih.gov/pubmed/36059922 http://dx.doi.org/10.3389/fdata.2022.965715 |
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author | Li, Xiaojin Tao, Shiqiang Lhatoo, Samden D. Cui, Licong Huang, Yan Hampson, Johnson P. Zhang, Guo-Qiang |
author_facet | Li, Xiaojin Tao, Shiqiang Lhatoo, Samden D. Cui, Licong Huang, Yan Hampson, Johnson P. Zhang, Guo-Qiang |
author_sort | Li, Xiaojin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9428292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94282922022-09-01 A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy Li, Xiaojin Tao, Shiqiang Lhatoo, Samden D. Cui, Licong Huang, Yan Hampson, Johnson P. Zhang, Guo-Qiang Front Big Data Big Data 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. Frontiers Media S.A. 2022-08-17 /pmc/articles/PMC9428292/ /pubmed/36059922 http://dx.doi.org/10.3389/fdata.2022.965715 Text en Copyright © 2022 Li, Tao, Lhatoo, Cui, Huang, Hampson and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Li, Xiaojin Tao, Shiqiang Lhatoo, Samden D. Cui, Licong Huang, Yan Hampson, Johnson P. Zhang, Guo-Qiang A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy |
title | A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy |
title_full | A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy |
title_fullStr | A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy |
title_full_unstemmed | A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy |
title_short | A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy |
title_sort | multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428292/ https://www.ncbi.nlm.nih.gov/pubmed/36059922 http://dx.doi.org/10.3389/fdata.2022.965715 |
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