Feasibility, acceptability and diagnostic test accuracy of frailty screening instruments in community-dwelling older people within the Australian general practice setting: a study protocol for a cross-sectional study

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/Introduction Frailty is one of the most challenging aspects of population ageing due to its association with increased risk of poor health outcomes and quality of life. General practice provides an ideal setting for the prevention and management of frailty via the implementation of preventive measures such as early identification through screening. Methods and analysis Our study will evaluate the feasibility, acceptability and diagnostic test accuracy of several screening instruments in diagnosing frailty among community-dwelling Australians aged 75+ years who have recently made an appointment to see their general practitioner (GP). We will recruit 240 participants across 2 general practice sites within South Australia. We will invite eligible patients to participate and consent to the study via mail. Consenting participants will attend a screening appointment to undertake the index tests: 2 self-reported (Reported Edmonton Frail Scale and Kihon Checklist) and 5 (Frail Scale, Groningen Frailty Index, Program on Research for Integrating Services for the Maintenance of Autonomy (PRISMA-7), Edmonton Frail Scale and Gait Speed Test) administered by a practice nurse (a Registered Nurse working in general practice). We will randomise test order to reduce bias. Psychosocial measures will also be collected via questionnaire at the appointment. A blinded researcher will then administer two reference standards (the Frailty Phenotype and Adelaide Frailty Index). We will determine frailty by a cut-point of 3 of 5 criteria for the Phenotype and 9 of 42 items for the AFI. We will determine accuracy by analysis of sensitivity, specificity, predictive values and likelihood ratios. We will assess feasibility and acceptability by: 1) collecting data about the instruments prior to collection; 2) interviewing screeners after data collection; 3) conducting a pilot survey with a 10% sample of participants. Ethics and dissemination The Torrens University Higher Research Ethics Committee has approved this study. We will disseminate findings via publication in peer-reviewed journals and presentation at relevant conferences

Recent developments in frailty identification, management, risk factors and prevention : A narrative review of leading journals in geriatrics and gerontology

Funding The Frailty Epidemiology Research Network (EPI-FRAIL) is an international collaborative project aimed at filling knowledge gaps in the field of frailty epidemiology. The network was established as part of a NWO/ZonMw Veni fellowship awarded to E.O. Hoogendijk (Grant no. 91618067). P. Hanlon is funded through a Clinical Research Training Fellowship from the Medical Research Council (Grant reference: MR/S021949/1). Z. Liu was supported by the Soft Science Research Program of Zhejiang Province (2023KXCX-KT011). J. Jylhävä has received grant support from the Swedish Research Council (grant no. 2018-02077), the Academy of Finland (grant no. 349335), the Sigrid Jusélius Foundation, the Yrjö Jahnsson Foundation and the Instrumentarium Science Foundation. M. Sim is supported by a Royal Perth Hospital Research Foundation Career Advancement Fellowship and an Emerging Leader Fellowship from the Future Health Research and Innovation Fund (Department of Health, Western Australia). R. Ambagtsheer receives funding from the Australian Medical Research Future Fund (grant #MRF2016140). D. L. Vetrano receives financial support from the Swedish Research Council (2021-03324). S. Shi reports funding from the National Institute of Aging, R03AG078894-01. None of the funding agencies had any role in the conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.Peer reviewedPublisher PD

Evolving Hybrid Partial Genetic Algorithm Classification Model for Cost-effective Frailty Screening: Investigative Study

BackgroundA commonly used method for measuring frailty is the accumulation of deficits expressed as a frailty index (FI). FIs can be readily adapted to many databases, as the parameters to use are not prescribed but rather reflect a subset of extracted features (variables). Unfortunately, the structure of many databases does not permit the direct extraction of a suitable subset, requiring additional effort to determine and verify the value of features for each record and thus significantly increasing cost. ObjectiveOur objective is to describe how an artificial intelligence (AI) optimization technique called partial genetic algorithms can be used to refine the subset of features used to calculate an FI and favor features that have the least cost of acquisition. MethodsThis is a secondary analysis of a residential care database compiled from 10 facilities in Queensland, Australia. The database is comprised of routinely collected administrative data and unstructured patient notes for 592 residents aged 75 years and over. The primary study derived an electronic frailty index (eFI) calculated from 36 suitable features. We then structurally modified a genetic algorithm to find an optimal predictor of the calculated eFI (0.21 threshold) from 2 sets of features. Partial genetic algorithms were used to optimize 4 underlying classification models: logistic regression, decision trees, random forest, and support vector machines. ResultsAmong the underlying models, logistic regression was found to produce the best models in almost all scenarios and feature set sizes. The best models were built using all the low-cost features and as few as 10 high-cost features, and they performed well enough (sensitivity 89%, specificity 87%) to be considered candidates for a low-cost frailty screening test. ConclusionsIn this study, a systematic approach for selecting an optimal set of features with a low cost of acquisition and performance comparable to the eFI for detecting frailty was demonstrated on an aged care database. Partial genetic algorithms have proven useful in offering a trade-off between cost and accuracy to systematically identify frailty

Should we screen for frailty in primary care settings? A fresh perspective on the frailty evidence base: A narrative review

With older adults living longer, health service providers have increasingly turned their attention towards frailty and its significant consequences for health and well-being. Consequently, frailty screening has gained momentum as a possible health policy answer to the question of what can be done to prevent frailty\u27s onset and progression. However, who should be screened for frailty, where and when remains a subject of extensive debate. The purpose of this narrative review is to explore the dimensions of this question with reference to Wilson and Jungner\u27s time-tested and widely accepted principles for acceptable screening within community settings. Although the balance of the emerging evidence to support frailty screening is promising, significant gaps in the evidence base remain. Consequently, when assessed against Wilson and Jungner\u27s principles, extensive population screening does not appear to be supported by the evidence. However, screening for the purpose of case-finding may prove useful among older adults

Application of an electronic frailty index in Australian primary care: data quality and feasibility assessment

Background: The primary care setting is the ideal location for identifying the condition of frailty in older adults. Aims: The aim of this pragmatic study was twofold: (1) to identify data items to extract the data required for an electronic Frailty Index (eFI) from electronic health records (EHRs); and (2) test the ability of an eFI to accurately and feasibly identify frailty in older adults. Methods: In a rural South Australian primary care clinic, we derived an eFI from routinely collected EHRs using methodology described by Clegg et al. We assessed feasibility and accuracy of the eFI, including complexities in data extraction. The reference standard for comparison was Fried’s frailty phenotype. Results: The mean (SD) age of participants was 80.2 (4.8) years, with 36 (60.0%) female (n = 60). Frailty prevalence was 21.7% by Fried’s frailty phenotype, and 35.0% by eFI (scores > 0.21). When deriving the eFI, 85% of EHRs were perceived as easy or neutral difficulty to extract the required data from. Complexities in data extraction were present in EHRs of patients with multiple health problems and/or where the majority of data items were located other than on the patient’s summary problem list. Discussion: This study demonstrated that it is entirely feasible to extract an eFI from routinely collected Australian primary care data. We have outlined a process for extracting an eFI from EHRs without needing to modify existing infrastructure. Results from this study can inform the development of automated eFIs, including which data items to best access data from

Recent developments in frailty identification, management, risk factors and prevention: A narrative review of leading journals in geriatrics and gerontology

Funding The Frailty Epidemiology Research Network (EPI-FRAIL) is an international collaborative project aimed at filling knowledge gaps in the field of frailty epidemiology. The network was established as part of a NWO/ZonMw Veni fellowship awarded to E.O. Hoogendijk (Grant no. 91618067). P. Hanlon is funded through a Clinical Research Training Fellowship from the Medical Research Council (Grant reference: MR/S021949/1). Z. Liu was supported by the Soft Science Research Program of Zhejiang Province (2023KXCX-KT011). J. Jylhävä has received grant support from the Swedish Research Council (grant no. 2018-02077), the Academy of Finland (grant no. 349335), the Sigrid Jusélius Foundation, the Yrjö Jahnsson Foundation and the Instrumentarium Science Foundation. M. Sim is supported by a Royal Perth Hospital Research Foundation Career Advancement Fellowship and an Emerging Leader Fellowship from the Future Health Research and Innovation Fund (Department of Health, Western Australia). R. Ambagtsheer receives funding from the Australian Medical Research Future Fund (grant #MRF2016140). D. L. Vetrano receives financial support from the Swedish Research Council (2021-03324). S. Shi reports funding from the National Institute of Aging, R03AG078894-01. None of the funding agencies had any role in the conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.Peer reviewedPublisher PD