Keywords: Frailty, Prediction model, Clinical Frailty Scale, Primary Care, Quality of life

Background:

The increased life expectancy around the world imposes significant demands on health care systems, as it is accompanied by a rise in age-related morbidity, frailty and healthcare needs within the ageing population. The true prevalence of frailty is uncertain. Since manually frailty assessments are time-consuming and have limited accuracy, both electronic frailty scores and prediction models to identify older adults with a higher risk for unplanned hospitalisations have been developed during the last decade.

Research questions:

What is the prevalence of frailty in a Swedish primary care population at high risk of hospitalisation?

How strong is the association between physical frailty and self‑reported loneliness, depression, memory problems, fear of falling, previous falls, and quality of life?

Method:

Community-dwelling people aged 75 years or older, within the 15% highest risk of hospitalisation according to the prediction model based on routine administrative healthcare data were included. A questionnaire was used to identify self-reported physical frailty. The questionnaire also included questions about perceived loneliness, depression, memory problems, fear of falling, previous falls and quality of life. In a subgroup CFS rating was performed.

Results:

Among 1169 participants, 497 (42.5%) were characterised as frail, 464 (39.7%) as pre-frail and 208 (17.8%) robust according to the questions selected to identify self-reported physical frailty. 488 individuals were rated with CFS, with 152 (31.1%) rated CFS 5-7, 153 (31.4%) CFS 4 and 183 (37,5%) CFS 1-3. There was a strong correlation between frailty and the prediction model risk index. Furthermore, there were strong correlations between self-reported physical frailty or pre-frailty and quality of life, perceived memory problems, perceived loneliness, self-reported fall episodes, and fear of falling.

Conclusions:

A prediction model that identifies older adults at high risk of hospitalisation can be used for an initial selection to initiate further frailty evaluation and subsequent proactive care.

Points for discussion:

What is your experience using frailty scales, such as the Clinical Frailty Scale, to identify vulnerable older adults in clinical practice?”

What barriers exist to implementing frailty tools in routine primary care?

What added value does an electronic prediction model provide compared with traditional frailty scales?