Margherita Silan | Searcharxiv

arXiv2026

Measuring frailty in the elderly: an indicator based on a super-classifier

Identifying frail older adults in an ageing population is essential for improving healthcare services. This study proposes a composite indicator to assess individual frailty levels using administrative healthcare data. Given the complex and multidimensional nature of frailty, a multi-outcome approach is adopted. Following an extensive literature review, a set of adverse health events is selected as proxies for frailty. These events were modelled using logistic classifiers, with frailty determinants (associated to adverse health events, selected using a gradient tree boosting) serving as covariates. The sensitivity and specificity of each classifier is used to compose their combined likelihood. From this, we derive an indicator capable of quantifying frailty across the population. The indicator shows robust performance across multiple outcomes and over time. Its primary innovation lies in allowing the use of diverse and outcome-specific sets of frailty determinants without any structural constraint. Overall, we offer an effective tool for quantifying frailty among older adults, potentially supporting health authorities in the prevention of frailty-related adverse events.

stat.ME↗

arXiv2025

Profiling Frailty: A parsimonious Frailty Index from health administrative data based on POSET theory

Frailty assessment is crucial for stratifying populations and addressing healthcare challenges associated with ageing. This study proposes a Frailty Index based on administrative health data, with the aim of facilitating informed decision-making and resource allocation in population health management. The aim of this work is to develop a Frailty Index that 1) accurately predicts multiple adverse health outcomes, 2) comprises a parsimonious set of variables, 3) aggregates variables without predefined weights, 4) regenerates when applied to different populations, and 5) relies solely on routinely collected administrative data. Using administrative data from a local health authority in Italy, we identified two cohorts of individuals aged $\ge$65 years. A set of six adverse outcomes (death, emergency room access with highest priority, hospitalisation, disability onset, dementia onset, and femur fracture) was selected to define frailty. Variable selection was performed using logistic regression modelling and a forward approach based on partially ordered set (POSET) theory. The final Frailty Index comprised eight variables: age, disability, total number of hospitalisations, mental disorders, neurological diseases, heart failure, kidney failure, and cancer. The Frailty Index performs well or very well for all adverse outcomes (AUC range: 0.664-0.854) except hospitalisation (AUC: 0.664). The index also captured associations between frailty and chronic diseases, comorbidities, and socioeconomic deprivation. This study presents a validated, parsimonious Frailty Index based on routinely collected administrative data. The proposed approach offers a comprehensive toolkit for stratifying populations by frailty level, facilitating targeted interventions and resource allocation in population health management.

stat.AP↗