Dlima Schenelle Dayna, Harris Danielle, Aminu Abodunrin Quadri, Hall Alex, Todd Chris, Vardy Emma Rlc
School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK; National Institute for Health and Care Research, Applied Research Collaboration - Greater Manchester, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK; National Institute for Health and Care Research Policy Research Unit in Healthy Ageing, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK; National Institute for Health and Care Research Policy Research Unit in Healthy Ageing, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
J Frailty Aging. 2025 Jun;14(3):100047. doi: 10.1016/j.tjfa.2025.100047. Epub 2025 May 3.
A frailty index (FI) is a frailty assessment tool calculated as the proportion of the number of health-related deficits an individual has to the total number of variables in the index. Routinely collected clinical and administrative data can be used as sources of deficits to automatically calculate FIs. This scoping review aimed to evaluate the current research landscape on routine data-based FIs. We searched seven databases to find literature published in 2013-2023. Main inclusion criteria were original research articles on FIs constructed from routine data, with deficits in at least two of the following categories: "symptoms/signs", "laboratory values", "diseases", "disabilities", and "others". From 7526 publications screened, 218 were included. Studies were primarily from North America (47.7 %), conducted in the community (35.3 %), and used routine data-based FIs for risk stratification (51.4 %). FIs were calculated using various routine data sources; however, most were initially developed and validated using hospital records. We noted geographical differences in study settings and routine data sources. We identified 611 unique deficits comprising these FIs. Most were either "diseases" (34.4 %) or "symptoms/signs" (32.1 %). Routine data-based FIs are feasible and valid risk stratification tools, but research is confined to high-income countries, their routine adoption is slow, and deficits comprising these FIs emphasise a reactive and overtly medical approach in addressing frailty. Future directions include exploring the feasibility and applicability of using routine databases for frailty assessment in lower- and middle-income countries, and leveraging non-clinical routine data through data linkages to proactively identify and manage frailty.
衰弱指数（FI）是一种衰弱评估工具，计算方法为个体健康相关缺陷的数量占指数中变量总数的比例。常规收集的临床和管理数据可作为缺陷来源，用于自动计算衰弱指数。本综述旨在评估基于常规数据的衰弱指数的当前研究状况。我们检索了七个数据库，以查找2013年至2023年发表的文献。主要纳入标准是关于基于常规数据构建的衰弱指数的原创研究文章，其缺陷至少属于以下类别中的两类：“症状/体征”、“实验室检查值”、“疾病”、“残疾”和“其他”。在筛选的7526篇出版物中，纳入了218篇。研究主要来自北美（47.7%），在社区开展（35.3%），并使用基于常规数据的衰弱指数进行风险分层（51.4%）。衰弱指数使用各种常规数据来源进行计算；然而，大多数最初是使用医院记录开发和验证的。我们注意到研究背景和常规数据来源存在地域差异。我们识别出构成这些衰弱指数的611个独特缺陷。大多数缺陷要么是“疾病”（34.4%），要么是“症状/体征”（32.1%）。基于常规数据的衰弱指数是可行且有效的风险分层工具，但研究局限于高收入国家，其常规应用进展缓慢，且构成这些衰弱指数的缺陷在应对衰弱方面强调一种被动和过度医疗的方法。未来的方向包括探索在低收入和中等收入国家使用常规数据库进行衰弱评估的可行性和适用性，以及通过数据链接利用非临床常规数据来主动识别和管理衰弱。
