人工智能驱动的卒中研究及管理临床核心通用数据元标准专家共识Expert Consensus on Clinical Core Common Data Element Standards for Artificial Intelligence-Driven Stroke Research and Management

临床研究中存在的数据元定义、标准不一致和碎片化等问题限制了医学研究的效率，也难以满足目前大数据和人工智能技术高速发展环境下，医学研究对高效数据采集、整合与标准化日益增长的需求。国家神经系统疾病医疗质量控制中心、神经系统疾病国家临床医学研究中心、北京市脑血管疾病防治办公室和中国卒中学会医疗质量管理与促进分会联合成立专家组，对2020版《卒中临床诊疗和疾病管理核心数据元及定义专家共识》进行了全面更新，形成了《人工智能驱动的卒中研究及管理临床核心通用数据元标准专家共识》。本次共识的修订和更新进一步拓展了卒中研究与管理数据元的广度与深度，系统界定了人口学信息、入院与转运信息、既往史与危险因素、既往用药史、临床症状和体征、辅助检查、病因分型、再灌注治疗、急性期治疗和二级预防药物、住院期间并发症、出院情况、康复治疗及随访信息等13个数据元模块的定义和标准。本共识的修订基于现实需求和未来研究发展的态势，旨在为卒中的临床诊疗、疾病管理、真实世界研究提供统一、规范的数据标准，以及为卒中诊疗临床决策、疾病风险预测及预后评估等场景中人工智能模型的构建与应用提供支持。Problems such as inconsistent definitions, non-uniform standards, and fragmentation of data elements in clinical research reduce research efficiency, and also fail to meet the ever-increasing demands for efficient data collection, integration, and standardization in medical research against the backdrop of the rapid development of big data and artificial intelligence technologies. The National Center for Healthcare Quality Management in Neurological Diseases, the China National Clinical Research Center for Neurological Diseases, the Beijing Office for Cerebrovascular Disease Prevention and Control, and the Medical Quality Management and Promotion Branch of the Chinese Stroke Association jointly established an expert working group. The group completed a comprehensive update of the 2020 Expert Consensus on Core Data Elements and Definitions for Diagnosis, Treatment, and Management of Stroke and formulated the Expert Consensus on Clinical Core Common Data Element Standards for Artificial Intelligence-Driven Stroke Research and Management. This revision and update further expanded the breadth and depth of the common data elements for stroke research and management, and systematically defined the standards and specifications for 13 data element modules, including demographic information, admission and transfer information, past medical history and risk factors, prior medication history, clinical symptoms and signs, auxiliary examinations, etiological classification, reperfusion therapy, acute-phase treatment and secondary prevention medications, in-hospital complications, discharge status, rehabilitation therapy, and follow-up information. Based on current practical needs and future research directions, this revised expert consensus provides unified and standardized data specifications for stroke clinical practice, disease management, and real-world research. It also supports the development and application of artificial intelligence models in clinical decision-making, disease risk prediction, and prognostic assessment for stroke diagnosis and treatment.