Abstract :
[en] Frailty is a geriatric medical condition that is highly associated with age and age-related diseases. The multidimensional
consequences of frailty are heavily impacting the quality of life, and will inevitably increase the burden
on healthcare systems in the future. Most importantly, the lack of a universal standard to describe, diagnose, or let
alone treat frailty, is further complicating the situation in the long-term. Nowadays, more and more frailty assessment
tools are being developed on a regional and institutional basis, which is continuing to drive the heterogeneity in the
characterization of frailty further apart. Gaining better insights into the underlying causes and pathophysiology
of frailty, and how it is developing in patients is, therefore, required to establish strong and accurately tailored
response schemes for frail patients, where currently only symptoms are treated. Thus, in this study, we deployed
machine learning-based classification and optimization techniques to predict frailty in the Berlin Aging Study II
(BASE-II, N=1512, frail=484) and revealed some of the most informative biomedical information to characterize
frailty, including new potential biomarkers. Frailty in BASE-II was measured by the Fried et al. 5-item frailty index,
composed of the clinical variables grip strength, weight loss, exhaustion, physical activity, and gait. The level of
frailty in BASE-II was adapted for binary classification purposes by merging the pre-frail and frail levels as frail. A
configurable in-house pipeline was developed for pre-processing the clinical data, predicting the target disease, and
determining the most informative subgroup of clinical measurements with regards to frailty. The best prediction
power was yielded with resampling and dimensionality reduction techniques using the F-beta-2 score, and was further
increased by adding one item of the Fried et al. frailty index. We suggest that a combination of the easy-to-obtain
biomedical information on frailty risk factors together with one Fried et al. phenotype information provided by
i.e. smart wearable devices (gait, grip strength, . . . ) could significantly improve the frailty prediction power.
