Multidisciplinary, multicomponent interventions to reduce frailty among older persons in residents of residential care facilities: a scoping review.

BACKGROUND: Frailty reduction and reversal have been addressed successfully among older populations within community settings. However, these findings may not be applicable to residential care settings, largely due to the complex and multidimensional nature of the condition. Relatively, few attempts at frailty prevention exist in residential settings. This review aims to identify and describe best practice models of care for addressing frailty among older populations in residential care settings. This research also sets out to explore the impact of multidisciplinary health service delivery models on health outcomes such as mortality, hospitalisations, quality of life, falls and frailty. METHODS: A scoping review of the literature was conducted to address the project objectives. Reference lists of included studies, bibliographic databases and the grey literature were systematically searched for literature reporting multidisciplinary, multidimensional models of care for frailty. RESULTS: The scoping review found no interventions that met the inclusion criteria. Of the 704 articles screened, 664 were excluded as not relevant. Forty articles were fully assessed, and while no eligible studies were found, relevant data were extracted from 10 near-eligible studies that reported single disciplines or single dimensions rather than a model of care. The physical, nutritional, medicinal, social and cognitive aspects of the near eligible studies have been discussed as playing a key role in frailty reduction or prevention care models. CONCLUSION: This review has identified a paucity of interventions for addressing and reducing frailty in residential care settings. High-quality studies investigating novel models of care for addressing frailty in residential care facilities are required to address this knowledge gap. Similarly, there is a need to develop and validate appropriate screening and assessment tools for frailty in residential care populations. Health service providers and policy-makers should also increase their awareness of frailty as a dynamic and reversible condition. While age is a non-modifiable predictor of frailty, addressing modifiable factors through comprehensive care models may help manage and prevent the physical, social and financial impacts of frailty in the ageing population.

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.

Prevalence and associations of frailty in residents of Australian aged care facilities: findings from a retrospective cohort study.

OBJECTIVES: Studies conducted among older people have shown that frailty is a common condition associated with an array of adverse outcomes. The aims of this study were to identify the prevalence and associations of frailty in older people residing in several aged care facilities located in Queensland, Australia. METHODS: The database used for this study was drawn from the Aged Care Funding Instrument (ACFI) database of an Australian aged care provider, and contained data from ten aged care facilities in Queensland, Australia. A modification of an eFI originally developed by Clegg and colleagues and based on Rockwood's Frailty Index (FI) of cumulative deficits was used to identify frailty. RESULTS: In total, 592 participants aged 75 years and over were included in the study (66.6% female). Median (IQR) age was 88.0 (9.0) years. Frailty prevalence among the sample was 43.6%, with 46.3% pre-frail and 10.1% not frail. In a multivariate logistic regression analysis incorporating three different models, frailty was significantly associated with three ACFI domains (Nutrition, Depression and Complex Health Care), along with facility size, consistently across two models. In the third model, frailty was also significantly associated with arthritis, diabetes, hypertension, osteoporosis and vision problems, along with male gender. CONCLUSION: There is a need to develop frailty identification and management programs as part of standard care pathways for older adults residing in aged care facilities. Aged care facilities should consider regular frailty screening in residential aged care residents, along with interventions addressing specific issues such as dysphagia and depression.