Use of routine clinical data in adults with chronic diseases for identifying risk of frailty: results from the UK Biobank study
- 1Kiel University
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{middle dot}9; 54{middle dot}7% women) with at least one chronic condition, 5{middle dot}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{middle dot}6% of all frailty cases, corresponding to a 2{middle dot}78-fold improvement compared to random screening. Overall, the model recovered 53{middle dot}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.
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BibTeX
@misc{landre2026routine,
title = {{Use of routine clinical data in adults with chronic diseases for identifying risk of frailty: results from the UK Biobank study}},
author = {B. LANDRE and G. C. Silva and A. Fayosse and I. Meneghel Danilevicz and W. Lieb and S. Sabia and A. Singh Manoux and P. Sturmer},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.05.26364730},
url = {https://www.medrxiv.org/content/10.64898/2026.10.05.26364730v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Use of routine clinical data in adults with chronic diseases for identifying risk of frailty: results from the UK Biobank study
AU - B. LANDRE
AU - G. C. Silva
AU - A. Fayosse
AU - I. Meneghel Danilevicz
AU - W. Lieb
AU - S. Sabia
AU - A. Singh Manoux
AU - P. Sturmer
PY - 2026
DA - 2026/10/05
PB - medRxiv
DO - 10.64898/2026.10.05.26364730
UR - https://www.medrxiv.org/content/10.64898/2026.10.05.26364730v1
AB - 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{middle dot}9; 54{middle dot}7% women) with at least one chronic condition, 5{middle dot}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{middle dot}6% of all frailty cases, corresponding to a 2{middle dot}78-fold improvement compared to random screening. Overall, the model recovered 53{middle dot}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.
ER -