PDF(Pubmed)
Abstract：
BACKGROUND: Rare disease registries (RDRs) are valuable tools for improving clinical care and advancing research. However, they often vary qualitatively, structurally, and operationally in ways that can determine their potential utility as a source of evidence to support decision-making regarding the approval and funding of new treatments for rare diseases.
OBJECTIVE: The goal of this research project was to review the literature on rare disease registries and identify best practices to improve the quality of RDRs.
METHODS: In this scoping review, we searched MEDLINE and EMBASE as well as the websites of regulatory bodies and health technology assessment agencies from 2010 to April 2023 for literature offering guidance or recommendations to ensure, improve, or maintain quality RDRs.
RESULTS: The search yielded 1,175 unique references, of which 64 met the inclusion criteria. The characteristics of RDRs deemed to be relevant to their quality align with three main domains and several sub-domains considered to be best practices for quality RDRs: (1) governance (registry purpose and description; governance structure; stakeholder engagement; sustainability; ethics/legal/privacy; data governance; documentation; and training and support); (2) data (standardized disease classification; common data elements; data dictionary; data collection; data quality and assurance; and data analysis and reporting); and (3) information technology (IT) infrastructure (physical and virtual infrastructure; and software infrastructure guided by FAIR principles (Findability; Accessibility; Interoperability; and Reusability).
CONCLUSIONS: Although RDRs face numerous challenges due to their small and dispersed populations, RDRs can generate quality data to support healthcare decision-making through the use of standards and principles on strong governance, quality data practices, and IT infrastructure.
OBJECTIVE: The goal of this research project was to review the literature on rare disease registries and identify best practices to improve the quality of RDRs.
METHODS: In this scoping review, we searched MEDLINE and EMBASE as well as the websites of regulatory bodies and health technology assessment agencies from 2010 to April 2023 for literature offering guidance or recommendations to ensure, improve, or maintain quality RDRs.
RESULTS: The search yielded 1,175 unique references, of which 64 met the inclusion criteria. The characteristics of RDRs deemed to be relevant to their quality align with three main domains and several sub-domains considered to be best practices for quality RDRs: (1) governance (registry purpose and description; governance structure; stakeholder engagement; sustainability; ethics/legal/privacy; data governance; documentation; and training and support); (2) data (standardized disease classification; common data elements; data dictionary; data collection; data quality and assurance; and data analysis and reporting); and (3) information technology (IT) infrastructure (physical and virtual infrastructure; and software infrastructure guided by FAIR principles (Findability; Accessibility; Interoperability; and Reusability).
CONCLUSIONS: Although RDRs face numerous challenges due to their small and dispersed populations, RDRs can generate quality data to support healthcare decision-making through the use of standards and principles on strong governance, quality data practices, and IT infrastructure.
摘要：
背景：罕见疾病登记处（RDR）是改善临床护理和推进研究的有价值的工具。然而,它们通常在质量上有所不同，在结构上，并且在操作上可以确定其潜在效用，作为支持有关批准和资助罕见疾病新疗法的决策的证据来源。
目的：该研究项目的目的是回顾有关罕见疾病注册的文献，并确定提高RDR质量的最佳实践。
方法：在本范围审查中，从2010年至2023年4月，我们搜索了MEDLINE和EMBASE以及监管机构和卫生技术评估机构的网站，以获取提供指导或建议的文献，以确保，改进,或保持RDR的质量。
结果：搜索产生了1,175个独特的参考，其中64人符合纳入标准。被认为与其质量相关的RDR的特征与被认为是高质量RDR的最佳实践的三个主要领域和几个子领域一致：（1）治理（注册目的和描述；治理结构；利益相关者参与；可持续性；道德/法律/隐私；数据治理；文档；以及培训和支持）；（2）数据（标准化疾病分类；通用数据元素；数据字典；数据收集；数据质量和保证；基础设施（虚拟基础设施的可重用性）；
结论：尽管RDR由于其人口少且分散而面临许多挑战，RDR可以通过使用关于强有力治理的标准和原则来生成高质量的数据，以支持医疗保健决策。质量数据实践，和IT基础设施。
目的：该研究项目的目的是回顾有关罕见疾病注册的文献，并确定提高RDR质量的最佳实践。
方法：在本范围审查中，从2010年至2023年4月，我们搜索了MEDLINE和EMBASE以及监管机构和卫生技术评估机构的网站，以获取提供指导或建议的文献，以确保，改进,或保持RDR的质量。
结果：搜索产生了1,175个独特的参考，其中64人符合纳入标准。被认为与其质量相关的RDR的特征与被认为是高质量RDR的最佳实践的三个主要领域和几个子领域一致：（1）治理（注册目的和描述；治理结构；利益相关者参与；可持续性；道德/法律/隐私；数据治理；文档；以及培训和支持）；（2）数据（标准化疾病分类；通用数据元素；数据字典；数据收集；数据质量和保证；基础设施（虚拟基础设施的可重用性）；
结论：尽管RDR由于其人口少且分散而面临许多挑战，RDR可以通过使用关于强有力治理的标准和原则来生成高质量的数据，以支持医疗保健决策。质量数据实践，和IT基础设施。
