The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
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DOI：
10.1093/jamia/ocaa196
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发表时间：
2021-03-01
影响因子：
6.4
通讯作者：
Gersing, Ken R.
中科院分区：
文献类型：
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作者：
Haendel, Melissa A.;Chute, Christopher G.;Gersing, Ken R.
目的：2019冠状病毒病（COVID-19）带来了社会挑战，需要快速的数据和知识共享。尽管组织临床数据丰富，但这些数据在很大程度上无法被外部研究人员访问。统计、机器学习和因果分析在大规模数据的情况下是最成功的，这些数据超出了任何给定组织的可用数据。在这里，我们介绍了国家COVID队列协作（N3 C），一个开放的科学社区，专注于分析来自许多中心的患者水平的数据。材料和方法：临床和转化科学奖计划和科学界创建了N3 C，以克服技术，法规，政策和治理障碍，共享和协调个人水平的临床数据。我们开发了解决方案来提取、聚合和协调跨组织和数据模型的数据，并创建了一个安全的数据飞地，以实现高效、透明和可重复的协作分析。成果：我们在包容性工作流中组织起来，为组织和研究人员创建了法律的协议和治理;数据提取脚本，以识别和摄取阳性、阴性和可能的COVID-19病例;建立数据质量保证和协调管道，以创建单一的协调数据集;使用数据、机器学习和统计分析工具填充安全数据飞地;传播机制;以及合成数据试点，以实现数据访问的民主化。N3 C已经证明，多站点协作学习健康网络可以克服障碍，快速构建可扩展的基础设施，整合多组织COVID临床数据。19分析我们希望这项工作能够通过临床医生、研究人员和数据科学家之间的快速合作来拯救生命，以确定治疗和专业护理，从而减少COVID-19的直接和长期影响。
Objective: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and Methods: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.Results: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.Conclusions: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.