Torres Moral Teresa, Sanchez-Niubo Albert, Monistrol-Mula Anna, Gerardi Chiara, Banzi Rita, Garcia Paula, Demotes-Mainard Jacques, Haro Josep Maria
Research and Development Unit, Parc Sanitari Sant Joan de Déu, 08830 Barcelona, Spain.
Melanoma Unit, Dermatology Department, August Pi i Sunyer Biomedical Research Institute (IDIBAPS) and Hospital Clínic, 08036 Barcelona, Spain.
J Pers Med. 2022 Apr 26;12(5):688. doi: 10.3390/jpm12050688.
Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer's disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.
个性化医疗需要大规模队列来进行患者分层和验证患者聚类。然而,用于个性化医疗队列设计和管理的方法和工具的标准及统一做法仍有待确定。本研究旨在描述该领域的当前技术水平。我们进行了一项范围综述,在PubMed、EMBASE、Web of Science、Psycinfo和Cochrane图书馆中搜索有关个性化医疗中使用的队列相关工具和方法的综述。搜索聚焦于癌症、中风和阿尔茨海默病,且仅限于2005年至2020年4月以英文、法文、德文、意大利文和西班牙文发表的报告。通过PRISMA流程图报告了筛选过程。纳入了50篇综述,其中大部分包含有关数据如何生成的信息(25/50)以及用于数据管理和分析的工具的信息(24/50)。未找到有关数据质量和监测相关临床数据要求的直接信息。在样本量计算等特定领域发现信息和标准匮乏。有了这些信息,未来可以制定全面的指南,以提高个性化医疗研究中队列设计和管理的可重复性和稳健性。