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Derivation of a frailty index from the resident assessment instrument - home care adapted for Switzerland: a study based on retrospective data analysis.

ABSTRACT: BACKGROUND:The screening of frail individuals at risk for functional health decline and adverse health outcomes lies in the evolving agenda of home care providers. Such a screening can be based on a frailty index (FI) derived from data collected with interRAI instruments used in clinical routines to define care plans. The objective of this study was to assess the feasibility of deriving an FI from the Resident Assessment Instrument - Home Care adapted for Switzerland (Swiss RAI-HC). METHODS:Data were collected by the Geneva Institution for Homecare and Assistance in clinical routines. The sample consisted of 3714 individuals aged 65 or older (67.7% females) who had each received a Swiss RAI-HC upon admission in the year of 2015. The FI was derived from 52 variables identified and scored according to published guidelines. Adverse health outcomes were either assessed during follow-up assessments (falls, hospitalizations) or documented from administrative records (mortality). RESULTS:The results showed that the FI was distributed normally, with a mean of 0.24 (± 0.13), an interquartile range of 0.16, and values of 0.04 at percentile 1 and 0.63 at percentile 99. The effect of Age was significant (R2 = 0.011) with a slope of ? = 0.002, 95% CI = [0.001-0.002]. Sex as well as the Age × Sex interaction were not significant. The FI predicted deaths (OR = 9.99, 95% CI = [3.20-29.99]), hospitalizations (OR = 3.40, 95% CI = [1.78-6.32]), and falls (OR = 5.00, 95% CI = [2.68-9.38]). CONCLUSIONS:The results support the feasibility of an FI derivation from the Swiss RAI-HC, hence replicating previous demonstrations based on interRAI instruments. The results also replicated findings showing that the FI is a good predictor of adverse health outcomes. Yet, the results suggest that home care recipients demonstrate a frailty pattern different from the one reported in community dwellers but comparable to clinical samples. Further work is needed to assess the characteristics of the proposed index in community-dwelling, non-clinical samples for comparability with the existing literature and external validation TRIAL REGISTRATION: ClinicalTrials.gov NCT03139162 . Retrospectively registered May 2, 2017.

SUBMITTER: Ludwig C

PROVIDER: S-EPMC5590146 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Derivation of a frailty index from the resident assessment instrument - home care adapted for Switzerland: a study based on retrospective data analysis.

Ludwig Catherine C Busnel Catherine C

BMC geriatrics 20170907 1

<h4>Background</h4>The screening of frail individuals at risk for functional health decline and adverse health outcomes lies in the evolving agenda of home care providers. Such a screening can be based on a frailty index (FI) derived from data collected with interRAI instruments used in clinical routines to define care plans. The objective of this study was to assess the feasibility of deriving an FI from the Resident Assessment Instrument - Home Care adapted for Switzerland (Swiss RAI-HC).<h4>M ...[more]

PMID: 28882127

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Project description:BackgroundDespite an increasing importance of home care, quality assurance in this healthcare sector in Switzerland is hardly established. In 2010, Swiss home care quality indicators (QIs) based on the Resident Assessment Instrument-Home Care (RAI-HC) were developed. However, these QIs have not been revised since, although internationally new RAI-HC QIs have emerged. The objective of this study was to assess the appropriateness of RAI-HC QIs to measure quality of home care in Switzerland from a public health and healthcare providers' perspective.MethodsFirst, the appropriateness of RAI-HC QIs, identified in a recent systematic review, was assessed by a multidisciplinary expert panel based on the RAND/UCLA Appropriateness Method taking into account indicators' public health relevance, potential of influence, and comprehensibility. Second, the QIs selected by the experts were afterwards rated regarding their relevance, potential of influence, and practicability from a healthcare providers' perspective in focus groups with home care nurses based on the Nominal-Group-Technique. Data were analyzed using median scores and the Disagreement Index.Results18 of 43 RAI-HC QIs were rated appropriate by the experts from a public health perspective. The 18 QIs cover clinical, psychosocial, functional and service use aspects. Seven of the 18 QIs were subsequently rated appropriate by home care nurses from a healthcare providers' perspective. The focus of these QIs is narrow, because three of seven QIs are pain-related. From both perspectives, the majority of RAI-HC QIs were rated inappropriate because of insufficient potential of influence, with healthcare providers rating them more critically.ConclusionsThe study shows that the appropriateness of RAI-HC QIs differs according to the stakeholder perspective and the intended use of QIs. The findings of this study can guide policy-makers and home care organizations on selecting QIs and to critically reflect on their appropriate use.

Project description:BACKGROUND:One way of measuring the quality of home care are quality indicators (QIs) derived from data collected with the Resident Assessment Instrument-Home Care (RAI-HC). In order to produce meaningful results for quality improvement and quality comparisons across home care organizations (HCOs) and over time, RAI-HC QIs must be valid and reliable. The aim of this systematic review was to identify currently existing RAI-HC QIs and to summarize the scientific knowledge on the validity and reliability of these QIs. METHODS:A systematic review was performed using the electronic databases PubMed, CINAHL, Embase, PsycINFO and Cochrane Library. Studies describing the development process or the psychometric characteristics of RAI-HC QIs were eligible. The data extraction involved a general description of the included studies as well as the identified RAI-HC QIs and information on validity and reliability. The methodological quality of the identified RAI-HC QI sets was assessed using the Appraisal of Indicators through Research and Evaluation (AIRE) instrument. RESULTS:Four studies out of 659 initial hits met the inclusion criteria. The included studies described the development and validation process of three RAI-HC QI sets comprising 48 unique RAI-HC QIs, which predominantly refer to outcome of care. Overall, the validity and reliability of the identified RAI-HC QIs were not sufficiently tested. The methodological quality of the three identified RAI-HC QI sets varied across the four AIRE instrument domains. None of the QI sets reached high methodological quality, defined as scores of 50% and higher in all four AIRE instrument domains. CONCLUSIONS:This is the first review that systematically summarized and appraised the available scientific evidence on the validity and reliability of RAI-HC QIs. It identified insufficient reporting of RAI-HC QIs validation processes and reliability as well as missing state-of-the-art methodologies. The review provides guidance as to what additional validity and reliability testing are needed to strengthen the scientific soundness of RAI-HC QIs. Considering that RAI-HC QIs are already implemented and used to measure and compare quality of home care, further investigations on RAI-HC QIs reliability and validity is recommended.

Project description:BACKGROUND:Currently, there are no formalized measures for the quality of home based palliative care in Ontario. This study developed a set of potential quality indicators for seriously ill home care clients using a standardized assessment. METHODS:Secondary analysis of Resident Assessment Instrument for Home Care data for Ontario completed between 2006 and 2013 was used to develop quality indicators (QIs) thought to be relevant to the needs of older (65+) seriously ill clients. QIs were developed through a review of the literature and consultation with subject matter experts in palliative care. Serious illness was defined as a prognosis of less than 6 months to live or the presence of severe health instability. The rates of the QIs were stratified across Ontario's geographic regions, and across four common life-limiting illnesses to observe variation. RESULTS:Within the sample, 14,312 clients were considered to be seriously ill and were more likely to experience negative health outcomes such as cognitive performance (OR?=?2.77; 95% CI: 2.66-2.89) and pain (OR?=?1.59; 95% CI: 1.53-1.64). Twenty subject matter experts were consulted and a list of seven QIs was developed. Indicators with the highest overall rates were prevalence of falls (50%) prevalence of daily pain (47%), and prevalence of caregiver distress (42%). The range in QI rates was largest across regions for prevalence of caregiver distress (21.5%), the prevalence of falls (16.6%), and the prevalence of social isolation (13.7%). Those with some form of dementia were most likely to have a caregiver that was distressed (52.6%) or to experience a fall (53.3%). CONCLUSION:Home care clients in Ontario who are seriously ill are experiencing high rates of negative health outcomes, many of which are amenable to change. The RAI-HC can be a useful tool in identifying these clients in order to better understand their needs and abilities. These results contribute significantly to the process of creating and validating a standardized set of QIs that can be generated by organizations using the RAI-HC as part of normal clinical practice.

Project description:Background Little is known about whether off-the-shelf wearable sensor data can contribute to fall risk classification or complement clinical assessment tools such as the Resident Assessment Instrument-Home Care (RAI-HC).Objective This study aimed to (1) investigate the similarities and differences in physical activity (PA), heart rate, and night sleep in a sample of community-dwelling older adults with varying fall histories using a smart wrist-worn device and (2) create and evaluate fall risk classification models based on (i) wearable data, (ii) the RAI-HC, and (iii) the combination of wearable and RAI-HC data.Methods A prospective, observational study was conducted among 3 faller groups (G0, G1, G2+) based on the number of previous falls (0, 1, ?2 falls) in a sample of older community-dwelling adults. Each participant was requested to wear a smart wristband for 7 consecutive days while carrying out day-to-day activities in their normal lives. The wearable and RAI-HC assessment data were analyzed and utilized to create fall risk classification models, with 3 supervised machine learning algorithms: logistic regression, decision tree, and random forest (RF).Results Of 40 participants aged 65 to 93 years, 16 (40%) had no previous falls, whereas 8 (20%) and 16 (40%) had experienced 1 and multiple (?2) falls, respectively. Level of PA as measured by average daily steps was significantly different between groups (P=.04). In the 3 faller group classification, RF achieved the best accuracy of 83.8% using both wearable and RAI-HC data, which is 13.5% higher than that of using the RAI-HC data only and 18.9% higher than that of using wearable data exclusively. In discriminating between {G0+G1} and G2+, RF achieved the best area under the receiver operating characteristic curve of 0.894 (overall accuracy of 89.2%) based on wearable and RAI-HC data. Discrimination between G0 and {G1+G2+} did not result in better classification performance than that between {G0+G1} and G2+.Conclusions Both wearable data and the RAI-HC assessment can contribute to fall risk classification. All the classification models revealed that RAI-HC outperforms wearable data, and the best performance was achieved with the combination of 2 datasets. Future studies in fall risk assessment should consider using wearable technologies to supplement resident assessment instruments.

Project description:BackgroundThe Resident Assessment Instrument-Minimum Data Set (RAI-MDS) 2.0 is designed to collect the minimum amount of data to guide care planning and monitoring for residents in long-term care settings. These data have been used to compute indicators of care quality. Use of the quality indicators to inform quality improvement initiatives is contingent upon the validity and reliability of the indicators. The purpose of this review was to systematically examine published and grey research reports in order to assess the state of the science regarding the validity and reliability of the RAI-MDS 2.0 Quality Indicators (QIs).MethodsWe systematically reviewed the evidence for the validity and reliability of the RAI-MDS 2.0 QIs. A comprehensive literature search identified relevant original research published, in English, prior to December 2008. Fourteen articles and one report examining the validity and/or reliability of the RAI-MDS 2.0 QIs were included.ResultsThe studies fell into two broad categories, those that examined individual quality indicators and those that examined multiple indicators. All studies were conducted in the United States and included from one to a total of 209 facilities. The number of residents included in the studies ranged from 109 to 5758. One study conducted under research conditions examined 38 chronic care QIs, of which strong evidence for the validity of 12 of the QIs was found. In response to these findings, the 12 QIs were recommended for public reporting purposes. However, a number of observational studies (n = 13), conducted in "real world" conditions, have tested the validity and/or reliability of individual QIs, with mixed results. Ten QIs have been studied in this manner, including falls, depression, depression without treatment, urinary incontinence, urinary tract infections, weight loss, bedfast, restraint, pressure ulcer, and pain. These studies have revealed the potential for systematic bias in reporting, with under-reporting of some indicators and over-reporting of others.ConclusionEvidence for the reliability and validity of the RAI-MDS QIs remains inconclusive. The QIs provide a useful tool for quality monitoring and to inform quality improvement programs and initiatives. However, caution should be exercised when interpreting the QI results and other sources of evidence of the quality of care processes should be considered in conjunction with QI results.

Project description:AimsFrailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming, and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure.Methods and resultsThis was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson co-morbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A total of 8893 patients (median: age 81, Q1-Q3: 71-87 years old) were included, in whom 9% had 30 day mortality and 17% had 90 day mortality. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five-fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively.ConclusionsThe electronic frailty index based on co-morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.

Project description:Background: Frailty is characterized by loss of biological reserves and is associated with an increased risk of adverse health outcomes. Frailty can be operationalized using a Frailty Index (FI) based on the accumulation of health deficits; items under health evaluation in the well-established Comprehensive Geriatric Assessment (CGA) have been used to generate an FI-CGA. Traditionally, constructing the FI-CGA has relied on paper-based recording and manual data processing. As this can be time-consuming and error-prone, it limits widespread uptake of this proven type of frailty assessment. Here, we report the development of an electronic tool, the eFI-CGA, for use on personal computers by frontline healthcare providers, to collect CGA data and automate FI-CFA calculation. The ultimate goal is to support early identification and management of frailty at points-of-care, and make uptake in Electronic Medical Records (EMR) feasible and transparent. Methods: An electronic CGA (eCGA) form was implemented to operate on Microsoft's WinForms platform and coded using C# programming language. Users complete the eCGA form, from which items under the CGA evaluation are automatically retrieved and processed to output an eFI-CGA score. A user-friendly interface and secured data saving methods were implemented. The software was debugged and tested using systematically designed simulation data, addressing different logic, syntax, and application errors, and then tested with clinical assessment. The user manual and manual scoring were used as ground truth to compare eFI-CGA input and automated eFI score calculations. Frontline health-provider user feedback was incorporated to improve the end-user experience. Results: The Standalone eFI-CGA software tool was developed and optimized for use on personal computers. The user interface adapted the design of paper-based CGA form to facilitate familiarity for clinical users. Compared to known scores, the software tool generated eFI-CGA scores with 100% accuracy to four decimal places. The eFI-CGA allowed secure data storage and retrieval of multiple types, including user input, completed eCGA form, coded items, and calculated eFI-CGA scores. It also permitted recording of actions requiring clinical follow-up, facilitating care planning. Application bugs were identified and resolved at various stages of the implementation, resulting in efficient system performance. Discussion: Accurate, robust, and reliable computerized frailty assessments are needed to promote effective frailty assessment and management, as a key tool in health care systems facing up to frailty. Our research has enabled the delivery of the standalone eFI-CGA software technology to empower effective frailty assessment and management by various healthcare providers at points-of-care, facilitating integrated care of older adults.

Project description:Demographic changes have led to an increase in the number of elderly frail persons and, consequently, systematic geriatric assessment is more important than ever. Frailty Indexes (FI) may be particularly useful to discriminate between various degrees of frailty but are not routinely assessed due, at least in part, to the large number of deficits assessed (from 30 to 70). Therefore, we have developed a new, more concise FI for rapid geriatric assessment (RGA)-the Frail-VIG index ("VIG" is the Spanish/Catalan abbreviation for Comprehensive Geriatric Assessment), which contains 22 simple questions that assess 25 different deficits. Here we describe this FI and report its ability to predict mortality at 24 months.Prospective, observational, longitudinal study of geriatric patients followed for 24 months or until death. The study participants were patients (n =?590) admitted to the Acute Geriatric Unit at the at the University Hospital of Vic (Barcelona) during the year 2014. Participants were classified into one of seven groups based on their Frail-VIG score (0-0.15; 0.16-0.25; 0.26-0.35; 0.36-0.45; 0.46-0.55; 0.56-0.65; and 0.66-1). Survival curves for these groups were compared using the log-rank test. ROC curves were used to assess the index's capacity to predict mortality at 24 months.Mean (standard deviation) patient age was 86.4 (5.6) years. The 24-month mortality rate was 57.3% for the whole sample. Significant between-group (deceased vs. living) differences (p <?0.05) were observed for most index variables. Survival curves for the seven Frail-VIG groups differed significantly (X2 =?433.4, p <?0.001), with an area under the ROC curve (confidence interval) of 0.90 (0.88-0.92) at 12 months and 0.85 (0.82-0.88) at 24 months. Administration time for the Frail-VIG index ranged from 5 to 10 min.The Frail-VIG index, which requires less time to administer than previously validated FIs, presents a good discriminative capacity for the degree of frailty and a high predictive capacity for mortality in the present cohort. Although more research is needed to confirm the validity of this instrument in other populations and settings, the Frail-VIG may provide clinicians with a RGA method and also a reliable tool to assess frailty in routine practice.

Project description:Background Frailty is increasingly applied as a measure to predict clinical outcomes, but data on the predictive abilities of frailty measures for non-home discharge and functional decline in acutely hospitalized geriatric patients are scarce. Objectives The aim of this study was to investigate the predictive ability of the frailty phenotype and a frailty index currently validated as part of the ongoing Swiss Frailty Network and Repository Study based on clinical admission data for non-home discharge and functional decline in acutely hospitalized older patients. Design Prospective cohort study. Setting and Participants Data were analyzed from 334 consecutive hospitalized patients of a tertiary acute care geriatric inpatient clinic admitted between August 2020 and March 2021. Measurements We assessed frailty using 1) the frailty phenotype and 2) the Swiss Frailty Network and Repository Study (SFNR) frailty index based on routinely available clinical admission data. Predictive abilities of both frailty measures were analyzed for the clinical outcomes of non-home discharge and functional decline using multivariate logistic regression models and receiver operating characteristic curves (ROC). Results Mean age was 82.8 (SD 7.2) years and 55.4% were women. Overall, 170 (53.1%) were frail based on the frailty phenotype and 220 (65.9%) based on the frailty index. Frail patients based on the frailty phenotype were more likely to be discharged non-home (55 (32.4%) vs. 26 (17.3%); adjusted OR 2.4 (95% CI, 1.4, 5.1)). Similarly, frail patients based on the frailty index were more likely to be discharged non-home compared to non-frail patients (76 (34.6%) vs. 9 (7.9%); adjusted OR, 5.5 (95% CI, 2.6, 11.5)). Both, the frailty phenotype and the frailty index were similarly associated with functional decline (adjusted OR 2.7 (95% CI, 1.5, 4.9); adjusted OR 2.8 (95% CI 1.4, 5.5)). ROC analyses showed best discriminatory accuracy for the frailty index for non-home discharge (area under the curve 0.76). Conclusions Frailty using the SFNR-frailty index and the frailty phenotype is a promising measure for prediction of non-home discharge and functional decline in acutely hospitalized geriatric patients. Further study is needed to define the most valid frailty measure. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.14283/jfa.2022.44.

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Derivation of a frailty index from the resident assessment instrument - home care adapted for Switzerland: a study based on retrospective data analysis.

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Derivation of a frailty index from the resident assessment instrument - home care adapted for Switzerland: a study based on retrospective data analysis.

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