Introduction The frailty index (FI) is a frailty assessment tool calculated as the proportion of the number of deficits, or ‘things that individuals have wrong with them’, to the total number of variables in the index. Routine health and administrative databases are valuable sources of deficits to automatically calculate FIs. There is large heterogeneity in the deficits used in FIs. This sub-analysis of a scoping review on routine data-based FIs aimed to describe and map the deficits used in multi-dimensional FIs. Methods Seven databases were searched to find literature published between 2013 and 2023. The main inclusion criterion was multi-dimensional FIs constructed from routinely collected data. Multi-dimensional FIs should have deficits in at least two of the following categories: ‘symptoms/signs’, ‘laboratory values’, ‘disease classification’, ‘disabilities’, and ‘other’. Results Of the 7526 publications screened, 57 distinct FIs were identified from 56 included studies. Most FIs were developed in the USA (n = 15) and in hospital settings (n = 19). The most dominant data source of deficits to calculate the FIs was hospital records (n = 21). Twenty-five FIs were developed for specific conditions and populations, for example, cancer, HIV, dementia, organ transplant recipients, and veterans. The median number of deficits used in the FIs was 36 (range = 5–72). Almost all the FIs (n = 56) had at least one deficit in the ‘symptoms/signs’ category, followed by ‘disease classification’ (n = 55) and ‘disabilities’ (n = 50). Approximately two-thirds of all the deficits were ‘symptoms/signs’ and ‘disease classifications’. Conclusion These findings highlight the reactive approach to frailty assessment, as most of these FIs were calculated from hospital data and used symptoms/signs and diseases as deficits. Given the heterogenous manifestations and long-term impacts of frailty, using a more proactive approach that leverages non-clinical routine data is warranted to prevent frailty development and progression.

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2319 基于常规数据的多维衰弱指数组成的缺陷：范围审查的子分析


简介 衰弱指数 （FI） 是一种衰弱评估工具，计算方法是缺陷数量或“个人出错的事情”与指数中变量总数的比例。常规健康和行政数据库是自动计算 FI 的宝贵赤字来源。FI 中使用的缺陷存在很大的异质性。本子分析对基于常规数据的 FI 的范围审查，旨在描述和绘制多维 FI 中使用的缺陷。方法 检索 7 个数据库以查找 2013 年至 2023 年间发表的文献。主要纳入标准是由常规收集的数据构建的多维 FI。多维 FI 应至少在以下类别中的两类存在缺陷：“症状/体征”、“实验室值”、“疾病分类”、“残疾”和“其他”。结果 在筛选的 7526 篇出版物中，从 57 项纳入研究中确定了 56 个不同的 FIs。大多数 FI 是在美国 （n = 15） 和医院 （n = 19） 开发的。计算 FI 的赤字最主要的数据源是医院记录 （n = 21）。针对特定条件和人群开发了 25 种 FI，例如癌症、HIV、痴呆、器官移植受者和退伍军人。FI 中使用的赤字数中位数为 36 （范围 = 5-72）。几乎所有的 FI （n = 56） 在“症状/体征”类别中都至少有一个缺陷，其次是“疾病分类”（n = 55）和“残疾”（n = 50）。大约三分之二的缺陷是“症状/体征”和“疾病分类”。 结论这些发现突出了虚弱评估的反应性方法，因为这些 FI 中的大多数是根据医院数据计算的，并使用症状/体征和疾病作为缺陷。鉴于衰弱的异质性表现和长期影响，有必要使用更主动的方法来利用非临床常规数据来防止衰弱的发展和进展。