Identification of Frailty in Primary Care: Feasibility and Acceptability of Recommended Case Finding Tools Within a Primary Care Integrated Seniors' Program

The feasibility, acceptability and appropriateness of screening for frailty in Australians aged 75 years and over attending Australian general practice

Background Globally, frailty is associated with a high prevalence of avoidable hospital admissions and emergency department visits, with substantial associated healthcare and personal costs. International guidelines recommend incorporation of frailty identification and care planning into routine primary care workflow to support patients who may be identified as pre-frail/frail. Our study aimed to: (1) determine the feasibility, acceptability, appropriateness and determinants of implementing a validated FRAIL Scale screening Tool into general practices in two disparate Australian regions (Sydney North and Brisbane South); and (2) map the resources and referral options required to support frailty management and potential reversal. Methods Using the FRAIL Scale Tool, practices screened eligible patients (aged ≥75years) for risk of frailty and referred to associated management options. The percentage of patients identified as frail/pre-frail, and management options and referrals made by practice staff for those identified as frail/pre-frail were recorded. Semi-structured qualitative interviews were conducted with practice staff to understand the feasibility, acceptability, appropriateness and determinants of implementing the Tool. Results The Tool was implemented by 19 general practices in two Primary Health Networks and 1071 consenting patients were assessed. Overall, 80% of patients (n =860) met the criterion for frailty: 33% of patients (n =352) were frail, and 47% were pre-frail (n =508). They were predominantly then referred for exercise prescription, medication reviews and geriatric assessment. The Tool was acceptable to staff and patients and compatible with practice workflows. Conclusions This study demonstrates that frailty is identified frequently in Australians aged ≥75years who visit their general practice. It's identification, linked with management support to reverse or reduce frailty risk, can be readily incorporated into the Medicare-funded annual 75+ Health Assessment.

Frailty detection among primary care older patients through the Primary Care Frailty Index (PC-FI)

The prompt identification of frailty in primary care is the first step to offer personalized care to older individuals. We aimed to detect and quantify frailty among primary care older patients, by developing and validating a primary care frailty index (PC-FI) based on routinely collected health records and providing sex-specific frailty charts. The PC-FI was developed using data from 308,280 primary care patients ≥ 60 years old part of the Health Search Database (HSD) in Italy (baseline 2013-2019) and validated in the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K; baseline 2001-2004), a well-characterized population-based cohort including 3363 individuals ≥ 60 years old. Potential health deficits part of the PC-FI were identified through ICD-9, ATC, and exemption codes and selected through an optimization algorithm (i.e., genetic algorithm), using all-cause mortality as the main outcome for the PC-FI development. The PC-FI association at 1, 3 and 5 years, and discriminative ability for mortality and hospitalization were tested in Cox models. The convergent validity with frailty-related measures was verified in SNAC-K. The following cut-offs were used to define absent, mild, moderate and severe frailty: < 0.07, 0.07-0.14, 0.14-0.21, and ≥ 0.21. Mean age of HSD and SNAC-K participants was 71.0 years (55.4% females). The PC-FI included 25 health deficits and showed an independent association with mortality (hazard ratio range 2.03-2.27; p < 0.05) and hospitalization (hazard ratio range 1.25-1.64; p < 0.05) and a fair-to-good discriminative ability (c-statistics range 0.74-0.84 for mortality and 0.59-0.69 for hospitalization). In HSD 34.2%, 10.9% and 3.8% were deemed mildly, moderately, and severely frail, respectively. In the SNAC-K cohort, the associations between PC-FI and mortality and hospitalization were stronger than in the HSD and PC-FI scores were associated with physical frailty (odds ratio 4.25 for each 0.1 increase; p < 0.05; area under the curve 0.84), poor physical performance, disability, injurious falls, and dementia. Almost 15% of primary care patients ≥ 60 years old are affected by moderate or severe frailty in Italy. We propose a reliable, automated, and easily implementable frailty index that can be used to screen the primary care population for frailty.

Strategies for implementing the interRAI home care frailty scale with home-delivered meal clients

Introduction

Frailty is a complex condition that is highly associated with health decline and the loss of independence. Home-delivered meal programs are designed to provide older adults with health and nutritional support that can attenuate the risk of frailty. However, home-delivered meal agencies do not routinely assess frailty using standardized instruments, leading to uncertainty over the longitudinal impact of home-delivered meals on frailty levels. Considering this knowledge gap, this study aimed to facilitate home-delivered meal staff's implementation of a standardized frailty instrument with meal clients as part of routine programming. This article (a) describes the use of Implementation Mapping principles to develop strategies supporting frailty instrument implementation in one home-delivered meal agency and (b) examines the degree to which a combination of strategies influenced the feasibility of frailty instrument use by home-delivered meal staff at multiple time points.

Methods and materials

This retrospective observational study evaluated staff's implementation of the interRAI Home Care Frailty Scale (HCFS) with newly enrolled home-delivered meal clients at baseline-, 3-months, and 6-months. The process of implementing the HCFS was supported by five implementation strategies that were developed based on tenets of Implementation Mapping. Rates of implementation and reasons clients were lost to 3- and 6-month follow-up were evaluated using univariate analyses. Client-level data were also examined to identify demographic factors associated with attrition at both follow-up time points.

Results

Staff implemented the HCFS with 94.8% (n = 561) of eligible home-delivered meal clients at baseline. Of those clients with baseline HCFS data, staff implemented the follow-up HCFS with 43% of clients (n = 241) at 3-months and 18.0% of clients (n = 101) at 6-months. Insufficient client tracking and documentation procedures complicated staff's ability to complete the HCFS at follow-up time points.

Discussion

While the HCFS assesses important frailty domains that are relevant to home-delivered meal clients, its longitudinal implementation was complicated by several agency- and client-level factors that limited the extent to which the HCFS could be feasibly implemented over multiple time points. Future empirical studies are needed to design and test theoretically derived implementation strategies to support frailty instrument use in the home- and community-based service setting.

Practitioner perceptions of the feasibility of common frailty screening instruments within general practice settings: a mixed methods study

BACKGROUND

Frailty is a highly prevalent clinical syndrome increasing older people's vulnerability to risk of adverse outcomes. Better frailty identification through expanded screening implementation has been advocated within general practice settings, both internationally and within Australia. However, little is known about practitioner perceptions of the feasibility of specific instruments, and the underlying motivations behind those perceptions. Consequently, the purpose of this study was to explore the attitudes and perceptions of a convenience and volunteer sample of Australian general practitioners (GPs) and practice nurses (PNs) towards common frailty screening instruments.

METHODS

The feasibility of several frailty screening instruments (PRISMA-7 [P7], Edmonton Frail Scale [EFS], FRAIL Questionnaire [FQ], Gait Speed Test [GST], Groningen Frailty Indicator [GFI], Kihon Checklist [KC] and Timed Up and Go [TUG]) to 43 Australian GPs and PNs was assessed. The study adopted a concurrent embedded mixed-methods design incorporating quantitative (ranking exercise) and qualitative (content analysis) data collection integrated during the analysis phase.

RESULTS

Practitioners assessed multi-dimensional instruments (EFS, GFI, KC) as having relatively higher clinical utility, better integration into existing assessment processes and stronger links to intervention over uni-dimensional (GST, TUG) and simple (FQ, P7) instruments.

CONCLUSIONS

While existing frailty screening instruments show promise as an initial step in supporting better care for older people, all the included instruments were associated with perceived advantages and disadvantages. Ultimately, clinicians will need to weigh several factors in their selection of the optimal screening instrument. Further translational research, with a focus on contextual fit, is needed to support clinical decision-making on the selection of instruments for frailty screening.

A Solution for the Remote Care of Frail Elderly Individuals via Exergames

Internet of Things (IoT) solutions are a concrete answer to many needs in the healthcare framework since they enable remote support for patients and foster continuity of care. Currently, frail elderly people are among end users who most need and would benefit from IoT solutions from both a social and a healthcare point of view. Indeed, IoT technologies can provide a set of services to monitor the healthcare of the elderly or support them in order to reduce the risk of injuries, and preserve their motor and cognitive abilities. The main feature of IoT solutions for the elderly population is ease of use. Indeed, to fully exploit the potential of an IoT solution, patients should be able to autonomously deal with it. The remote-monitoring validation engineering system (ReMoVES) described here is an IoT solution that caters to the specific needs of frail elderly individuals. Its architecture was designed for use at rehabilitation centers and at patients' homes. The system is user-friendly and comfortably usable by persons who are not familiar with technology. In addition, exergames enhance patient engagement in order to curb therapy abandonment. Along with the technical presentation of the solution, a real-life scenario application is described referring to sit-to-stand activity.

The Seniors' Community Hub: An Integrated Model of Care for the Identification and Management of Frailty in Primary Care

(1) Background: Integrated models of primary care deliver the comprehensive and preventative approach needed to identify and manage frailty in older people. Seniors' Community Hub (SCH) was developed to deliver person-centered, evidence-informed, coordinated, and integrated care services to older community dwelling adults living with frailty. This paper aims to describe the SCH model, and to present patient-oriented results of the pilot. (2) Methods: SCH was piloted in an academic clinic with six family physicians. Eligible patients were community dwelling, 65 years of age and older, and considered to be at risk of frailty (eFI > 0.12). Health professionals within the clinic received training in geriatrics and interprofessional teamwork to form the SCH team working with family physicians, patients and caregivers. The SCH intervention consisted of a team-based multi-domain assessment with person-centered care planning and follow-up. Patient-oriented outcomes (EQ-5D-5L and EQ-VAS) and 4-metre gait speed were measured at initial visit and 12 months later. (3) Results: 88 patients were enrolled in the pilot from April 2016-December 2018. No statistically significant differences in EQ-5D-5L/VAS or the 4-metre gait speed were detected in 38 patients completing the 12-month assessment. (4) Conclusions: Future larger scale studies of longer duration are needed to demonstrate impacts of integrated models of primary care on patient-oriented outcomes for older adults living with frailty.

Predictive validity of PRISMA-7 as a screening instrument for frailty in a hospital setting

OBJECTIVES

Frailty is a major clinical geriatric syndrome associated with serious adverse events including functional disability, falls, hospitalisation, increased morbidity and mortality. The aim of this study was to study the associations between frailty defined as Program of Research to Integrate Services for the Maintenance of Autonomy (PRISMA-7) score ≥3 and use of healthcare resources in hospital and in the municipality as well as association between frailty and mortality.

DESIGN

Register-based retrospective study.

SETTING

The target population consists of patients aged 75 years or above who, during hospital stay, were assessed by a physiotherapist, and at discharge from hospital were prescribed further physical training in the community.

PARTICIPANTS

973 individuals aged 75+ years were included.

OUTCOME MEASURES

We examined associations between frailty and use of healthcare resources in hospital and in the municipality as well as the association between frailty and mortality.

RESULTS

973 individuals aged 75+ years were included. Of these, 63.9% had a PRISMA-7 score ≥3 and were thus defined as frail. Frail individuals were older compared with non-frail with mean ages of 84.6 and 80.4 years, respectively, p>0.001. Age and gender-adjusted mortality after 1 year was higher among the frail (OR 2.46, 95% CI 1.53 to 3.97). Use of healthcare services in the municipality as well as hospital admissions was significantly higher among frail individuals.

CONCLUSIONS

Based on these findings we consider PRISMA-7 to be useful in an in-hospital setting as a screening tool to identify frail elderly patients who may profit from further geriatric assessment during hospital stay.

TRIAL REGISTRATION NUMBER

ID REG-070-2017.

My-AHA: Software Platform to Promote Active and Healthy Ageing

The population is getting old, and the use of technology has improved the quality of life of the senior population. This is confirmed by the increasing number of solutions targeting healthy and active ageing. Such solutions keep track of the daily routine of the elderly and combine it with other relevant information (e.g., biosignals, physical activity, social activity, nutrition) to help identify early signs of decline. Caregivers and elders use this information to improve their routine, focusing on improving the current condition. With that in mind, we have developed a software platform to support My-AHA, which is composed of a multi-platform middleware, a decision support system (DSS), and a dashboard. The middleware seamlessly merges data coming from multiple platforms targeting health and active ageing, the DSS performs an intelligent computation on top of the collected data, and the dashboard provides a user’s interaction with the whole system. To show the potential of the proposed My-AHA software platform, we introduce the My Personal Dashboard web-based application over a frailty use case to illustrate how senior well-being can benefit from the use of technology.

Feasibility and acceptability of commonly used screening instruments to identify frailty among community-dwelling older people: a mixed methods study

Background

Frailty exposes older people to an elevated risk of a range of negative outcomes. Emerging evidence that frailty can be effectively treated within community settings has stimulated calls for more proactive screening within primary care. Assessing feasibility is a critical preliminary step in assessing the efficacy of interventions such as screening. However, few studies have explored the feasibility and acceptability of administering frailty screening instruments within general practice, and even fewer have incorporated patient perspectives. Our study had three objectives: To 1) assess overall feasibility of the instruments (completion time and rate); 2) assess patient acceptability towards the instruments; and 3) assess the feasibility and acceptability of the instruments to administering nurses.

Methods

The feasibility and acceptability of several frailty screening instruments (PRISMA-7, Edmonton Frail Scale, FRAIL Scale Questionnaire, Gait Speed, Groningen Frailty Indicator, Reported Edmonton Frail Scale and Kihon Checklist) was explored within the context of a larger diagnostic test accuracy (DTA) study. Completion time and rate was collected for all participants (N = 243). A sub-sample of patients (n = 30) rated each instrument for ease of completion and provided comment on perceived acceptability. Lastly, five of six administering nurses involved in the DTA study participated in semi-structured face-to-face interviews, rating the instruments against several feasibility and acceptability criteria (time, space, equipment, skill required to implement, acceptability to patients and nurses, ease of scoring) and providing comment on their responses.

Results

The PRISMA-7 returned the highest overall feasibility and acceptability, requiring minimal space, equipment, skills and time to implement, and returning the fastest completion rate and highest patient and nurse acceptability rating. All screening instruments were faster to implement than the two reference standards (Fried’s Frailty Phenotype and Frailty Index). Self-administered instruments were subject to lower rates of completion than nurse-administered instruments.

Conclusions

This study has demonstrated that a number of commonly used frailty screening instruments are potentially feasible for implementation within general practice. Ultimately, more research is needed to determine how contextual factors, such as differences in individual patient and clinician preferences, setting and system factors, impact on the feasibility of screening in practice.

The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set

INTRODUCTION

Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilities or nursing homes). However, progress on effective identification of frailty within residential care remains at an early stage, necessitating the development of new methods for accurate and efficient screening.

OBJECTIVES

We aimed to determine the effectiveness of artificial intelligence (AI) algorithms in accurately identifying frailty among residents aged 75 years and over in comparison with a calculated electronic Frailty Index (eFI) based on a routinely-collected residential aged care administrative data set drawn from 10 residential care facilities located in Queensland, Australia. A secondary objective included the identification of best-performing candidate algorithms.

METHODS

We designed a frailty prediction system based on the eFI identification of frailty, allocating 84.5 % and 15.5 % of the data to training and test data sets respectively. We compared the performance of 18 specific scenarios to predict frailty against eFI based on unique combinations of three ML algorithms (support vector machines [SVM], decision trees [DT] and K-nearest neighbours [KNN]) and six cases (6, 10, 11, 14, 39 and 70 input variables). We calculated accuracy, percentage positive and negative agreement, sensitivity, specificity, Cohen's kappa and Prevalence- and Bias- Adjusted Kappa (PABAK), table frequencies and positive and negative predictive values.

RESULTS

Of 592 eligible resident records, 500 were allocated to the training set and 92 to the test set. Three scenarios (10, 11 and 70 input variables), all based on SVM algorithm, returned overall accuracy above 75 %.

CONCLUSIONS

There is some potential for AI techniques to contribute towards better frailty identification within residential care. However, potential benefits will need to be weighed against administrative burden, data quality concerns and presence of potential bias.