Abstract
Frailty assessment is increasingly recommended for risk stratification in chronic disease management, but systematic clinical screening for frailty remains difficult to implement because it is resource-intensive. We investigated whether routinely collected clinical measures could be repurposed to prioritize adults with chronic diseases for clinical frailty assessment.
We conducted a prediction study using UK Biobank data. A stacked ensemble model was developed using 12 routinely and low cost measured clinical variables to predict frailty. Gain metrics were used to quantify the proportion of frailty cases identified when screening was restricted to individuals with the highest predicted risk of frailty. We compared health profiles (mortality, polypharmacy, self-rated health, falls) of frail and non-frail individuals across different levels of predicted risk. The robustness of this screening strategy was evaluated across 3,284 modelling settings.
Among 281,729 participants (mean age 57.9; 54.7% women) with at least one chronic condition, 5.8% were frail. In the validation sample (N validation=93,910, N training=187,819), screening only the 20% of participants with the highest predicted risk of frailty identified 55.6% of all frailty cases, corresponding to a 2.78-fold improvement compared to random screening. Overall, the model recovered 53.4% of the maximum achievable improvement over random screening. Frailty remained always associated with poor outcomes. Screening performance remained consistent across alternative modelling settings.
Routinely collected clinical measures can improve the efficiency of frailty screening among adults living with chronic diseases by prioritizing individuals based on their level of risk. Since these data are already gathered during routine care, this risk-prioritization strategy could support scalable screening without increasing testing burden or clinical workload.