The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository: rationale and blueprint.

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Neuroinformatics Pub Date : 2025-01-07 eCollection Date: 2024-01-01 DOI:10.3389/fninf.2024.1508161
Hakim Baazaoui, Stefan T Engelter, Henrik Gensicke, Lukas S Enz, Marios Psychogios, Matthias Mutke, Patrik Michel, Davide Strambo, Alexander Salerno, Henk A Marquering, Paul J Nederkoorn, Nabila Wali, Stephanie Tanadini-Lang, Björn Menze, Ezequiel de la Rosa, Kaiyuan Yang, Gian Marco De Marchis, Tolga D Dittrich, Francesco Valletta, Manon Germann, Carlo W Cereda, João Pedro Marto, Lisa Herzog, Patrick Hirschi, Zsolt Kulcsar, Susanne Wegener
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Abstract

Purpose: The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository is a collaboration established in 2024 by seven stroke centres in Europe. MAGIC consolidates clinical and radiological data from acute ischemic stroke (AIS) patients who underwent endovascular therapy, intravenous thrombolysis, a combination of both, or conservative management.

Participants: All centres ensure accuracy and completeness of the data. Only patients who did not refuse use of their routine data collected during or after their hospital stay are included in the repository. Approvals or waivers are obtained from the responsible ethics committees before data exchange. A formal data transfer agreement (DTA) is signed by all contributing centres. The centres then share their data, and files are stored centrally on a safe server at the University Hospital Zurich. There, patient identifiers are removed and images are algorithmically de-faced. De-identified structured clinical data are connected to the imaging data by a new identifier. Data are made available to participating centres which have entered into a DTA for stroke research projects.

Repository setup: Initially, MAGIC is set to comprise initial and first follow-up imaging of 2,500 AIS patients. Clinical data consist of a comprehensive set of patient characteristics and routine prehospital metrics, treatment and laboratory variables.

Outlook: Our repository will support research by leveraging the entire range of routinely collected imaging and clinical data. This dataset reflects the current state of practice in stroke patient evaluation and management and will enable researchers to retrospectively study clinically relevant questions outside the scope of randomized controlled clinical trials. New centres are invited to join MAGIC if they meet the requirements outlined here. We aim to reach approximately 10,000 cases by 2026.

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多中心急性缺血性卒中成像和临床数据(MAGIC)存储库:原理和蓝图。
目的:多中心急性缺血性卒中成像和临床数据(MAGIC)存储库是由欧洲7个卒中中心于2024年合作建立的。MAGIC整合了急性缺血性卒中(AIS)患者的临床和放射学数据,这些患者接受了血管内治疗、静脉溶栓、两者联合治疗或保守治疗。参加者:各中心确保资料的准确性及完整性。只有不拒绝使用住院期间或住院后收集的常规数据的患者才被纳入存储库。在数据交换之前,必须获得负责任的伦理委员会的批准或豁免。所有提供数据的中心签署了正式的数据转移协议。然后,这些中心共享他们的数据,文件集中存储在苏黎世大学医院的一台安全服务器上。在那里,患者标识符被删除,图像被算法删除。去识别的结构化临床数据通过一个新的标识符连接到成像数据。数据提供给已签订中风研究项目数据交换协议的参与中心。存储库设置:最初,MAGIC将包括2500名AIS患者的初始和首次随访成像。临床数据包括一套全面的患者特征和常规院前指标、治疗和实验室变量。展望:我们的知识库将通过利用常规收集的所有影像和临床数据来支持研究。该数据集反映了卒中患者评估和管理的现状,并将使研究人员能够回顾性地研究随机对照临床试验范围之外的临床相关问题。新中心如符合以下要求,可获邀请加入MAGIC。我们的目标是到2026年达到约1万例。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
期刊最新文献
Editorial: Protecting privacy in neuroimaging analysis: balancing data sharing and privacy preservation. The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository: rationale and blueprint. Leveraging deep learning for robust EEG analysis in mental health monitoring. Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks. Editorial: Emerging trends in large-scale data analysis for neuroscience research.
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