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

TL;DR

This paper introduces a novel composite indicator for measuring frailty in elderly populations using a super-classifier approach that integrates multiple adverse health outcomes and determinants from healthcare data.

Contribution

It presents a new multidimensional frailty indicator that leverages diverse outcome-specific determinants without structural constraints, enhancing assessment accuracy.

Findings

Robust performance across multiple health outcomes

Effective quantification of frailty over time

Supports prevention of adverse events in elderly

Abstract

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.