Athanase Benetos, Head of Department of Geriatrics University Hospital of Nancy, 54511 Vandoeuvre les Nancy France; phone number: +33.3.83.15.49.45; fax number +33.3.83.15.76.68; e-mail:
J Nutr Health Aging. 2020;24(7):730-738. doi: 10.1007/s12603-020-1420-6.
To propose a simple frailty screening tool able to identify frailty profiles.
Cross-sectional observational study.
Participants were recruited in 3 different clinical settings: a primary care outpatient clinic (RURAL population, N=591), a geriatric day clinic (DAY-CLINIC population, N=76) and healthy volunteers (URBAN population, N=147).
A total of 817 older adults (>70 years old) living at home were included.
A 9-item questionnaire (Lorraine Frailty Profiling Screening Scale, LoFProSS), constructed by an experts' working group, was administered to participants by health professionals.
A Multiple Correspondence Analysis (MCA) followed by a hierarchical clustering of the results of the MCA performed in each population was conducted to identify participant profiles based on their answers to LoFProSS. A response pattern algorithm was resultantly identified in the RURAL (main) population and subsequently applied to the URBAN and DAY-CLINIC populations and, in these populations, the two classification methods were compared. Finally, clinically-relevant profiles were generated and compared for their ability to similarly classify subjects.
The response pattern differed between the 3 sub-populations for all 9 items, revealing significant intergroup differences (1.2±1.4 positive responses for URBAN vs. 2.1±1.3 for RURAL vs. 3.1±2.1 for DAY-CLINIC, all p<0.05). Five clusters were highlighted in the main RURAL population: "non-frail", "hospitalizations", "physical problems", "social isolation" and "behavioral", with similar clusters highlighted in the remaining two populations. Identification of the response pattern algorithm in the RURAL population yielded a second classification approach, with 83% of tested participants classified in the same cluster using the 2 different approaches. Three clinically-relevant profiles ("non-frail" profile, "physical frailty and diseases" profile and "cognitive-psychological frailty" profile) were subsequently generated from the 5 clusters. A similar double classification approach as above was applied to these 3 profiles revealing a very high percentage (95.6%) of similar profile classifications using both methods.
The present results demonstrate the ability of LoFProSS to highlight 3 frailty-related profiles, in a consistent manner, among different older populations living at home. Such scale could represent an added value as a simple frailty screening tool for accelerated and better-targeted investigations and interventions.
提出一种简单的虚弱筛查工具,以确定虚弱特征。
横断面观察性研究。
参与者在 3 个不同的临床环境中招募:初级保健门诊(农村人口,N=591)、老年日诊所(日间诊所人口,N=76)和健康志愿者(城市人口,N=147)。
共纳入 817 名在家居住的老年人(>70 岁)。
由专家组构建的 9 项问卷(洛林虚弱特征筛查量表,LoFProSS)由卫生专业人员向参与者进行。
对每个群体的结果进行多重对应分析(MCA)和 MCA 分层聚类,根据他们对 LoFProSS 的回答确定参与者的特征。在农村(主要)人群中确定了一种响应模式算法,然后将其应用于城市和日间诊所人群,并在这些人群中比较两种分类方法。最后,生成了具有临床意义的特征,并比较了它们对分类对象的相似性。
对于所有 9 个项目,3 个亚人群的反应模式不同,显示出显著的组间差异(城市人群为 1.2±1.4 个阳性反应,农村人群为 2.1±1.3 个,日间诊所人群为 3.1±2.1 个,均为 P<0.05)。在主要的农村人群中,有 5 个聚类突出:“非虚弱”、“住院”、“身体问题”、“社会隔离”和“行为”,在其余两个人群中也有类似的聚类。在农村人群中确定响应模式算法产生了第二种分类方法,使用两种方法对 83%的测试参与者进行了相同聚类的分类。随后从 5 个聚类中生成了 3 个具有临床意义的特征(“非虚弱”特征、“身体虚弱和疾病”特征和“认知心理虚弱”特征)。上述两种分类方法应用于这 3 种特征,发现使用两种方法的相似特征分类百分比非常高(95.6%)。
本研究结果表明,LoFProSS 能够以一致的方式在不同居住在家的老年人群中突出 3 种与虚弱相关的特征。该量表可以作为一种简单的虚弱筛查工具,用于加速和更有针对性的调查和干预,具有附加价值。