Electronic Frailty Index Research Articles

Improving Surgical Outcomes for Older Adults with Adoption of Technological Advances in Comprehensive Geriatric Assessment

Frailty is a well-recognized predictor of poor surgical outcomes for older adults, yet effective measurements and interventions remain limited. Technological advances offer an opportunity to address this gap and improve surgical care for older adults. This paper reviews the background of frailty and comprehensive geriatric assessments in surgery, and how technological innovations can advance frailty measurement and intervention in surgical settings. We review two broad areas of technological advancement as applied to frailty in surgery: 1) Innovation in the use of electronic health records (EHR) using Artificial Intelligence (AI) and Machine Learning (ML), and 2) Novel uses for wearable sensors and mobile health (mHealth) applications. We explore the integration of AI and ML with EHR systems, which can surpass traditional comorbidity indices by providing comprehensive health assessments and enhancing prediction models. Innovations like the electronic Frailty Index (eFI) show promise in expanding the reach of frailty assessments and facilitating real-time screening. Additionally, wearable devices and mobile health (mHealth) applications offer new ways to monitor and improve physical activity, nutrition, and psychological well-being, supporting perioperative rehabilitation. While these technologies present challenges, such as the need for infrastructure, training, and data interoperability, they offer promising strategies to facilitate the assessment and management of frailty among surgical patients. Continued research and tailored implementation strategies will be essential to fully realize the potential of these advancements in improving surgical outcomes for frail older adults.

Development and validation of an electronic frailty index in a national health maintenance organization.

Frailty constitutes a major factor that puts the elderly at risk of health and functional deterioration. To develop and validate an Electronic Frailty Index based on electronic data routinely collected in the HMO. A retrospective cohort of the HMO members. 120,986 patients, aged 65 years and over at the beginning of 2023. A cumulative frailty index including 36 medical, functional, and social deficits. One-year all-cause mortality or hospitalization. One-year hazard ratios were estimated for composite outcome of mortality or hospitalization using multivariable hierarchical Cox regression. The mean EFI score increased with the Social Security Nursing Benefit. Compared to fit patients, mild, moderate, and severe frailty patients had 2.07, 3.35, and 4.4-fold increased risks of mortality or hospitalization, after controlling for covariates. The findings showed that the Electronic Frailty Index version we created is valid in predicting mortality or hospitalization. In addition, the Electronic Frailty Index converged with an independent measurement produced by National Social Security.

Impact of frailty in older people on health care demand: simulation modelling of population dynamics to inform service planning.

As populations age, frailty and the associated demand for health care increase. Evidence needed to inform planning and commissioning of services for older people living with frailty is scarce. Accurate information on incidence and prevalence of different levels of frailty and the consequences for health outcomes, service use and costs at population level is needed. To explore the incidence, prevalence, progression and impact of frailty within an ageing general practice population and model the dynamics of frailty-related healthcare demand, outcomes and costs, to inform the development of guidelines and tools to facilitate commissioning and service development. A retrospective observational study with statistical modelling to inform simulation (system dynamics) modelling using routine data from primary and secondary health care in England and Wales. Modelling was informed by stakeholder engagement events conducted in Hampshire, England. Data sources included the Royal College of General Practitioners Research and Surveillance Centre databank, and the Secure Anonymised Information Linkage Databank. Population prevalence, incidence and progression of frailty within an ageing cohort were estimated using the electronic Frailty Index tool, and associated service use and costs were calculated. Association of frailty with outcomes, service use and costs was explored with multistate and generalised linear models. Results informed development of a prototype system dynamics simulation model, exploring population impact of frailty and future scenarios over a 10-year time frame. Simulation model population projections were externally validated against retrospective data from Secure Anonymised Information Linkage. The Royal College of General Practitioners Research and Surveillance Centre sample comprised an open cohort of the primary care population aged 50 + between 2006 and 2017 (approx. 2.1 million people). Data were linked to Hospital Episode Statistics data and Office for National Statistics death data. A comparable validation data set from Secure Anonymised Information Linkage was generated. Electronic Frailty Index score calculated annually and stratified into Fit, Mild, Moderate and Severe frailty categories. Other variables included age, sex, Index of Multiple Deprivation score, ethnicity and Urban/rural. Frailty transitions, mortality, hospitalisations, emergency department attendances, general practitioner visits and costs. Frailty is already present in people aged 50-64. Frailty incidence was 47 cases per 1000 person-years. Frailty prevalence increased from 26.5% (2006) to 38.9% (2017). Older age, higher deprivation, female sex, Asian ethnicity and urban location independently predict frailty onset and progression; 4.8% of 'fit' people aged 50-64 years experienced a transition to a higher frailty state in a year, compared to 21.4% aged 75-84. Individual healthcare use rises with frailty severity, but Mild and Moderate frailty groups have higher overall costs due to larger population numbers. Simulation projections indicate frailty will increase by 7.1%, from 41.5% to 48.7% between 2017 and 2027, and associated costs will rise by £5.8 billion (in England) over an 11-year period. Simulation modelling indicates that frailty prevalence and associated service use and costs will continue to rise in the future. Scenario analysis indicates reduction of incidence and slowing of progression, particularly before the age of 65, has potential to substantially reduce future service use and costs, but reducing unplanned admissions in frail older people has a more modest impact. Study outputs will be collated into a commissioning toolkit, comprising guidance on drivers of frailty-related demand and simulation model outputs. This study is registered as NCT04139278 www.clinicaltrials.gov. This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 16/116/43) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 44. See the NIHR Funding and Awards website for further award information.

The Complex Relationship Between Social and Functional Needs in Frail Older Adults.

There has been a growing interest in integrating social and function-focused care into health care settings. Little is known about what older adults perceive as the needs that impact their lives, and the resources to address patients' social and functional needs often exist outside of traditional health care settings. Our objective was to understand frail older adults' and community organizations' perspectives on what social and functional needs impact older adults' health, the support they receive, and how organizations and health systems could partner to address these needs. We conducted semi-structured interviews with patients and community-based organizations. Patients were aged 65 years or older, frail (electronic frailty index greater than 0.21), and at an increased geographic risk of unmet social needs (Area Deprivation Index greater than or equal to the 75th percentile). Staff were from organizations that provided social and/or functional resources to older adults. We used an inductive content analysis approach and the constant comparative method to analyze the data and identify themes. We interviewed 23 patients and 28 staff from 22 distinct organizations. We found that social, financial, and functional needs were common and highly intertwined among older adults with frailty, but the support they received at home, from their health care providers, and from community organizations was highly varied. Our sample was limited to participants from one county, so the results may not be generalizable to other areas. We only inter-viewed organizations and patients with frailty. Health systems and community organizations have distinct areas of expertise, and purposeful collaboration between them could be important in addressing the needs of frail older adults.

The effect of frailty on mortality and hospital admission in patients with benign pleural disease in Wales: a cohort study

Pleural disease is common, representing 5% of the acute medical workload, and its incidence is rising, partly due to the ageing population. Frailty is an important feature and little is known about disease progression in patients with frailty and pleural disease. We aimed to examine the effect of frailty on mortality and other relevant outcomes in patients diagnosed with pleural disease. In this cohort study in Wales, the national Secure Anonymised Information Linkage databank was used to identify a cohort of individuals diagnosed with non-malignant pleural disease between Jan 1, 2005, and March 1, 2023, who were not known to have left Wales. Frailty was assessed at diagnosis of pleural disease using an electronic Frailty Index. The primary outcome was time from diagnosis to all-cause mortality for all patients. Data were analysed using multilevel mixed-effects Cox proportional hazards regression adjusting for the prespecified covariates of age, sex, Welsh Index of Multiple Deprivation quintile, smoking status, comorbidity, and subtype of pleural disease. 54 566individuals were included in the final sample (median age 66years [IQR 47-77]; 26 477 [48·5%] were female and 28 089 [51·5%] were male). By the end of the study period, 25 698 (47·1%) participants had died, with a median follow-up of 1·0years (IQR 0·2-3·6). There was an association between frailty and all-cause mortality, which increased as frailty worsened. Compared with fit individuals, there was increasing mortality for those with mild frailty (adjusted hazard ratio 1·11 [95% CI 1·08-1·15]; p<0·0001), moderate frailty (1·25 [1·20-1·31]; p<0·0001), and severe frailty (1·36 [1·28-1·44]; p<0·0001). Independent of age and comorbidities, frailty status at diagnosis of pleural disease appeared to be useful as a prognostic indicator. Patients with moderate or severe frailty had a rapid decline in health. Future patients should be assessed for frailty at the time of diagnosis of pleural disease and might benefit from optimised care and advance care planning. Cardiff University's Wellcome Trust iTPA funding award.

The electronic frailty index and outcomes in patients with myocardial infarction.

Frailty is increasingly present in patients with acute myocardial infarction. The electronic Frailty Index (eFI) is a validated method of identifying vulnerable older patients in the community from routine primary care data. Our aim was to assess the relationship between the eFI and outcomes in older patients hospitalised with acute myocardial infarction. Retrospective cohort study using the DataLoch Heart Disease Registry comprising consecutive patients aged 65years or over hospitalised with a myocardial infarction between October 2013 and March 2021. Patients were classified as fit, mild, moderate, or severely frail based on their eFI score. Cox-regression analysis was used to determine the association between frailty category and all-cause mortality. In 4670 patients (median age 77years [71-84], 43% female), 1865 (40%) were classified as fit, with 1699 (36%), 798 (17%) and 308 (7%) classified as mild, moderate and severely frail, respectively. In total, 1142 patients died within 12months of which 248 (13%) and 147 (48%) were classified as fit and severely frail, respectively. After adjustment, any degree of frailty was associated with an increased risk of all-cause death with the risk greatest in the severely frail (reference = fit, adjusted hazard ratio 2.87 [95% confidence intervals 2.24 to 3.66]). The eFI identified patients at high risk of death following myocardial infarction. Automatic calculation within administrative data is feasible and could provide a low-cost method of identifying vulnerable older patients on hospital presentation.

The performance status gap in immunotherapy for frail patients with advanced non-small cell lung cancer

PurposeIn advanced non-small cell lung cancer (NSCLC), immune checkpoint inhibitor (ICI) monotherapy is often preferred over intensive ICI treatment for frail patients and those with poor performance status (PS). Among those with poor PS, the additional effect of frailty on treatment selection and mortality is unknown.MethodsPatients in the veterans affairs national precision oncology program from 1/2019–12/2021 who received first-line ICI for advanced NSCLC were followed until death or study end 6/2022. Association of an electronic frailty index with treatment selection was examined using logistic regression stratified by PS. We also examined overall survival (OS) on intensive treatment using Cox regression stratified by PS. Intensive treatment was defined as concurrent use of platinum-doublet chemotherapy and/or dual checkpoint blockade and non-intensive as ICI monotherapy.ResultsOf 1547 patients receiving any ICI, 66.2% were frail, 33.8% had poor PS (≥ 2), and 25.8% were both. Frail patients received less intensive treatment than non-frail patients in both PS subgroups (Good PS: odds ratio [OR] 0.67, 95% confidence interval [CI] 0.51 − 0.88; Poor PS: OR 0.69, 95% CI 0.44 − 1.10). Among 731 patients receiving intensive treatment, frailty was associated with lower OS for those with good PS (hazard ratio [HR] 1.53, 95% CI 1.2 − 1.96), but no association was observed with poor PS (HR 1.03, 95% CI 0.67 − 1.58).ConclusionFrail patients with both good and poor PS received less intensive treatment. However, frailty has a limited effect on survival among those with poor PS. These findings suggest that PS, not frailty, drives survival on intensive treatment.

Longitudinal trajectories of frailty are associated with short-term mortality in older people: a joint latent class models analysis using 2 UK primary care databases

ObjectivesFrailty is a dynamic health state that changes over time. Our hypothesis was that there are identifiable subgroups of the older population that have specific patterns of deterioration. The objective of this study was to evaluate the application of joint latent class model in identifying trajectories of frailty progression over time and their group-specific risk of death in older people. Study Design and SettingThe primary care records of UK patients, aged over 65 as of January 1, 2010, included in the Clinical Practice Research Datalink: GOLD and AURUM databases, were analyzed and linked to mortality data. The electronic frailty index (eFI) scores were calculated at baseline and annually in subsequent years (2010-2013). Joint latent class model was used to divide the population into clusters with different trajectories and associated mortality hazard ratios. The model was built in GOLD and validated in AURUM. ResultsFive trajectory clusters were identified and characterized based on baseline and speed of progression: low–slow, low–moderate, low–rapid, high–slow, and high–rapid. The high–rapid cluster had the highest average starting eFI score; 7.9, while the low–rapid cluster had the steepest rate of eFI progression; 1.7. Taking the low–slow cluster as reference, low–rapid and high–rapid had the highest hazard ratios: 3.73 (95% CI 3.71, 3.76) and 3.63 (3.57-3.69), respectively. Good validation was found in the AURUM population. ConclusionOur research found that there are vulnerable subgroups of the older population who are currently frail or have rapid frailty progression. Such groups may be targeted for greater healthcare monitoring.

Trajectories of Frailty and Clinical Outcomes in Older Adults With Atrial Fibrillation: Insights From the Shizuoka Kokuho Database.

The increasing prevalence of frailty has gained considerable attention due to its profound influence on clinical outcomes. However, our understanding of the progression of frailty and long-term clinical outcomes in older individuals with atrial fibrillation remains scarce. Using data from 2012 to 2018 from a comprehensive claims database incorporating primary and hospital care records in Shizuoka, Japan, we selected patients aged ≥65 years with atrial fibrillation who initiated oral anticoagulant therapy. The trajectory of frailty was plotted using Sankey plots, illustrating the annual changes in their frailty according to the electronic frailty index during a 3-year follow-up after oral anticoagulant initiation, along with the incidence of clinical adverse outcomes. For deceased patients, we assessed their frailty status in the year preceding their death. Of 6247 eligible patients (45.1% women; mean age, 79.3±8.0 years) at oral anticoagulant initiation, 7.7% were categorized as fit (electronic frailty index, 0-0.12), 30.1% as mildly frail (>0.12-0.24), 35.4% as moderately frail (>0.24-0.36), and 25.9% as severely frail (>0.36). Over the 3-year follow-up, 10.4% of initially fit patients transitioned to moderately frail or severely frail. Conversely, 12.5% of severely frail patients improved to fit or mildly frail. Death, stroke, and major bleeding occurred in 23.4%, 4.1%, and 2.2% of patients, respectively. Among the mortality cases, 74.8% (N=1183) and 3.5% (N=55) had experienced moderately or severely frail and either a stroke or major bleeding in the year preceding their death, respectively. In a contemporary era of atrial fibrillation management, a minor fraction of older patients on oral anticoagulants died following a stroke or major bleeding. However, their frailty demonstrated a dynamic trajectory, and a substantial proportion of death was observed after transitioning to a moderately or severely frail state.

Not All Frailty Assessments Are Created Equal: Comparability of Electronic Health Data-Based Frailty Assessments in Assessing Older People in Residential Care.

Objectives: To evaluate the comparability of frailty assessment tools - the electronic frailty index (eFI), retrospective electronic frailty index (reFI), and clinical frailty scale (CFS) - in older residents of care facilities. Methods: Data from 813 individuals aged 65 or older, with frailty and co-morbidities, collected between 2022 and 2023, were analysed using various statistical methods. Results: The results showed significant differences in frailty classification among the tools: 78.3% were identified as moderately to severely frail by eFI, 59.6% by reFI, and 92.1% by CFS. Statistical tests confirmed significant differences (p < .05) in their assessments, indicating variability in measurement methods. Discussion: This study advances the understanding of frailty assessment within aged-care settings, highlighting the differences in the efficacy of these assessment tools. It underscores the challenges in frailty assessments and emphasizes the need for continuous refinement of assessment methods to address the diverse facets of frailty in aged care.

Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults.

Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year. Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal-external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups. The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal-external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, -0.87; 95% CI: -0.96 to -0.78). Clinical utility on external validation was improved after recalibration. The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.

Primary and secondary care service use and costs associated with frailty in an ageing population: longitudinal analysis of an English primary care cohort of adults aged 50 and over, 2006-2017.

Frailty becomes more prevalent and healthcare needs increase with age. Information on the impact of frailty on population level use of health services and associated costs is needed to plan for ageing populations. To describe primary and secondary care service use and associated costs by electronic Frailty Index (eFI) category. Retrospective cohort using electronic health records. Participants aged ≥50 registered in primary care practices contributing to the Oxford Royal College of General Practitioners Research and Surveillance Centre, 2006-2017. Primary and secondary care use (totals and means) were stratified by eFI category and age group. Standardised 2017 costs were used to calculate primary, secondary and overall costs. Generalised linear models explored associations between frailty, sociodemographic characteristics. Adjusted mean costs and cost ratios were produced. Individual mean annual use of primary and secondary care services increased with increasing frailty severity. Overall cohort care costs for were highest in mild frailty in all 12years, followed by moderate and severe, although the proportion of the population with severe frailty can be expected to increase over time. After adjusting for sociodemographic factors, compared to the fit category, individual annual costs doubled in mild frailty, tripled in moderate and quadrupled in severe. Increasing levels of frailty are associated with an additional burden of individual service use. However, individuals with mild and moderate frailty contribute to higher overall costs. Earlier intervention may have the most potential to reduce service use and costs at population level.

Perspectives on the representation of frailty in the electronic frailty index

BackgroundFrailty is a state of increased vulnerability from physical, social, and cognitive factors resulting in greater risk of negative health-related outcomes and increased healthcare expenditure. A 36-factor electronic frailty index (eFI) developed in the United Kingdom calculates frailty scores using electronic medical record data. There is currently no standardization of frailty screening in Canadian primary care. In order to implement the eFI in a Canadian context, adaptation of the tool is necessary because frailty is represented by different clinical terminologies in the UK and Canada. In considering the promise of implementing an eFI in British Columbia, Canada, we first looked at the content validation of the 36-factor eFI. Our research question was: Does the eFI represent frailty from the perspectives of primary care clinicians and older adults in British Columbia?MethodsA modified Delphi using three rounds of questionnaires with a panel of 23 experts (five family physicians, five nurse practitioners, five nurses, four allied health professionals, four older adults) reviewed and provided feedback on the 36-factor eFI. These professional groups were chosen because they closely work as interprofessional teams within primary care settings with older adults. Older adults provide real life context and experiences. Questionnaires involved rating the importance of each frailty factor on a 0–10 scale and providing rationale for ratings. Panelists were also given the opportunity to suggest additional factors that ought to be included in the screening tool. Suggested factors were similarly rated in two Delphi rounds.ResultsThirty-three of the 36 eFI factors achieved consensus (> 80% of panelists provided a rating of ≥ 8). Factors that did not achieve consensus were hypertension, thyroid disorder and peptic ulcer. These factors were perceived as easily treatable or manageable and/or not considered reflective of frailty on their own. Additional factors suggested by panelists that achieved consensus included: cancer, challenges to healthcare access, chronic pain, communication challenges, fecal incontinence, food insecurity, liver failure/cirrhosis, mental health challenges, medication noncompliance, poverty/financial difficulties, race/ethnic disparity, sedentary/low activity levels, and substance use/misuse. There was a 100% retention rate in each of the three Delphi rounds.Conclusions and next stepsThree key findings emerged from this study: the conceptualization of frailty varied across participants, identification of frailty in community/primary care remains challenging, and social determinants of health affect clinicians’ assessments and perceptions of frailty status. This study will inform the next phase of a broader mixed-method sequential study to build a frailty screening tool that could ultimately become a standard of practice for frailty screening in Canadian primary care. Early detection of frailty can help tailor decision making, frame discussions about goals of care, prevent advancement on the frailty trajectory, and ultimately decrease health expenditures, leading to improved patient and system level outcomes.

PeRsOnaliSed care Planning for oldER people with frailty (PROSPER): protocol for a randomised controlled trial

BackgroundFrailty is common in older age and is characterised by loss of biological reserves across multiple organ systems. These changes associated with frailty mean older people can be vulnerable to sudden, dramatic changes in health because of relatively small problems. Older people with frailty are at increased risk of adverse outcomes including disability, hospitalisation, and care home admission, with associated reduction in quality of life and increased NHS and social care costs. Personalised Care Planning offers an anticipatory, preventative approach to supporting older adults to live independently for longer, but it has not been robustly evaluated in a population of older adults with frailty.MethodsFollowing an initial feasibility study, this multi-centre, individually randomised controlled trial aims to establish whether personalised care planning for older people improves health-related quality of life. It will recruit 1337 participants from general practices across Yorkshire and Humber and Mid-Mersey in the North of England. Eligible patients will be aged 65 and over with an electronic frailty index score of 0.21 or above, living in their own homes, without severe cognitive impairment and not in receipt of end-of-life care. Following confirmation of eligibility, informed consent and baseline data collection, participants will be individually randomised to the PeRsOnaliSed care Planning for oldER people with frailty (PROSPER) intervention or usual care in a 2.6:1 allocation ratio. Participants will not be blinded to allocation, but data collection and analysis will be blinded. The intervention will be delivered over 12 weeks by a Personal Independence Co-ordinator worker based within a voluntary sector organisation, Age UK. The primary outcomes are health-related quality of life, measured using both the physical and mental components of the Short-Form 12 Item Health Questionnaire at 12 months after randomisation. Secondary outcomes comprise activities of daily living, self-management capabilities and loneliness, admission to care homes, hospitalisations, and health and social care resource use at 12 months post randomisation. Parallel cost-effectiveness and process evaluations will be conducted alongside the trial.DiscussionThe PROSPER study will evaluate the effectiveness and cost-effectiveness of a personalised care planning approach for older people with frailty and inform the process of its implementation.Trial registrationmailto:ISRCTN16123291. Registered on 28 August 2020.

A novel partnership for proactive person-centred anticipatory care in North Lanarkshire

Anticipatory Care is a shift from reactive, fragmented, episodic care to proactive, personalised and coordinated team based care that starts with what matters to the individual and takes an enablement and strength based approach. However in a post Covid-19 landscape many primary care teams are challenged to deliver proactive care for the growing number of people with frailty who will benefit from the approach. &#x0D; A novel collaborative approach was co-designed with primary care teams, patients and carers in one locality. Information from GP records and the electronic Frailty Index was used to identify individuals with escalating levels of frailty. In addition, professionals could identify individuals opportunistically. Prior to the pandemic, Health Visitors gathered information and performed clinical assessments at home and discussed issues such as Power of Attorney and Anticipatory Care Planning (ACP). The workforce disruption from the pandemic was a catalyst to test new workforce roles. The locality engaged an advocacy service partner to increase capacity for authentic person centred multi disciplinary team (MDT) care for older people with frailty. Other core members of the MDT are GP, geriatrician and pharmacist with support as required from allied health professionals, social care, dietician, community mental health team and third sector.&#x0D; The approach is goal--orientated ensuring the patient, their wishes and their family or carer remain the primary focus (Steele Gray et al, 2020). The advocacy worker supports the patient through a self assessment process before representing the patient within the MDT discussion, ensuring independent advocacy and support at each stage of the process and enabling the MDT to formulate interventions that are in keeping with the patient’s wishes and priorities for their health. Interventions range from polypharmacy reviews to care packages, referrals to other professionals and to social prescribing supports. Following the MDT the patient receives a phone call from the advocacy worker, outlining the proposed interventions and ensuring they are empowered to express their opinion and wishes. Thus the patient remains in control and the team avoids a paternalistic therapeutic approach (Dumez and Pomey, 2019).&#x0D; Between October 2019 and December 2021, 106 patients were discussed by the MDT. Median age was 78 (range 58 – 101) years, 58% were female and 73% were identified using routine GP data. Seventy had a home assessment and multifactorial interventions, the remainder had an ACP to share their wishes about future care. &#x0D; &#x0D; An enabler was the use of Microsoft Teams platform. Conducting MDT’s virtually allows seamless information sharing in real time for accurate and informed care planning despite health, social care and the third sector operating separate electronic systems.&#x0D; The advocacy service is now seen as a key partner in the MDT. Working in a transdisciplinary manner helped overcome service pressures. Extending roles outwith normal professional boundaries and across sectors creates capacity for managing frailty, ensures safe, effective and person centred care and enables relational practice and reciprocal learning (Batorowicz and Shepherd, 2008). The novel collaborative approach is now being rolled out across other localities.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

FRAILTY ASSESSMENT FROM PAPER TO ONLINE: THE E-FI-CGA WEB APP

Abstract Background Early frailty assessment is crucial for improving care for older adults through enhancing frailty-informed care planning and execution. To increase the access and use of the established electronic Frailty Index based on the Comprehensive Geriatric Assessment tool, we developed an online software application: the web-based eFI-CGA. Methods End-user requirements primarily adapted the standalone version of the eFI-CGA and covered assessment competency, interface familiarity, accessibility and convenient use by front-line health professionals. Web app-bounded features included user-account authentication and authorization, user-space management, record-search and retrieval, and functions for data management, analysis and display. The web app development used the Microsoft Azure web server with the ASP.NET framework, combined Webforms and Model-View-Controller architecture, and C# programming language with standard client-side libraries incorporating scripting and mark-up languages. Results The web-based eFI-CGA was technically released and accessible online (efi-cga). Tests on the web pages for all the web pages (e.g., Home, Signup Login, Assessment, Search, and Analysis) using large sizes of systematically designed test data and mocked patient cohorts showed that the web software functions meet the requirements with 100% accuracy. Conclusion The web-based eFI-CGA provides a valuable remote frailty assessment and information retrieval method for healthcare professionals, promoting effective multidisciplinary integrated care of older adults. Further work is needed to allow the safe use of the web app. We are also planning a patient-oriented frailty assessment tool for community-dwelling older adults.

Frailty assessed by the electronic frailty index and its impact on health outcomes in older adults with chronic diseases: a systematic review

Purpose: The electronic frailty index (eFI), which is derived from electronic health records, has been recommended as screening tool for frailty due to its accessibility and ease of use. The objective of this systematic review was to identify the prevalence of frailty assessed by the eFI and its influence on health outcomes in older adults with chronic diseases. Methods: We searched PubMed, Embase, Web of Science, CINAHL, SCOPUS, Cochrane, Google search, and nursing journals in Korean from January 2016 to December 2022. Results: Twelve studies were analyzed. The eFI score, based on routine clinical data, was associated with adverse health outcomes. The most frequent outcome studied was mortality, and the eFI was associated with increased mortality in nine studies. Other outcomes studied included hospitalization, length of stay, readmission, and institutionalization in relation to hospital care usage, and cardiovascular events, stroke, GI bleeding, falls, and instrumental activities of daily life as health conditions. Conclusion: Early identification of frailty in older adults with chronic diseases can decrease the burden of disease and adverse health outcomes. The eFI has a good discriminative capacity to identify frail older adults with chronic diseases.

Frailty measured using an electronic frailty index and outcomes in older patients with myocardial infarction

Abstract Background Frailty is common amongst older patients with myocardial infarction and is an independent predictor of poor clinical outcomes. Despite this, frailty is not systematically or objectively measured nor routinely used in risk stratification. The electronic Frailty Index (eFI) is a frailty assessment tool which uses electronic health records to identify and classify frailty and has been shown to correlate well with in-person frailty assessment. However, the performance of the eFI in disease specific populations and its ability to predict individual patient outcomes is unclear. Purpose To assess the relationship between frailty, measured using the eFI, and the management and outcomes of older patients with myocardial infarction. Methods Consecutive patients admitted to hospital in the South East of Scotland between 01/10/2014 and the 01/03/2021 with a primary diagnosis of myocardial infarction were identified using International Classification of Disease (ICD-10) code I21 or I22. Patients under the age of 65 (n= 3,601) and those with no biomarker evidence of myocardial injury were excluded (n=1,301). The provision of guideline recommended therapy, recurrent hospital admission due to myocardial infarction, heart failure or stroke and 12-month all-cause mortality were compared between groups based on eFI classification (fit, mild, moderate or severely frail). Results Of the 3,930 patients (mean age 78 [SD 8] years, 43% female, 83% Caucasian), 2,324 (59%) were classified as frail with 1,421 (36%), 664 (17%), and 239 (6%) classified as mild, moderate and severely frail, respectively. The eFI was measurable in all patients. Patients with any degree of frailty were less likely to receive anti-platelet therapy, ACE inhibitor or angiotensin receptor blocker or beta-blocker therapy or undergo revascularisation with fewer than 1 in 10 patients with severe frailty undergoing inpatient coronary catheterisation (7.5% vs 57% non-frail, P &amp;lt;0.001 for all). Frailty was independently associated with an increased incidence of recurrent myocardial infarction, heart failure or stroke within 12 months with the odds greatest in those with severe frailty (OR 1.93 (95% CI 1.42-2.61%)). The primary outcome of all-cause death at 12-months occurred in 355 (25%), 226 (34%) and 109 (46%) patients with mild, moderate and severe frailty compared with 176 (11%) in non-frail patients (Figure 1). Frailty status was an independent risk factor for all-cause mortality with the risk of death greatest in those with severe frailty (HR 2.64 (95% CI 1.63 – 3.67, p&amp;lt;0.001)). Conclusion The eFI identified patients at increased risk of in-patient death, major adverse cardiovascular events, and all-cause mortality within 12 months following myocardial infarction. Measurement of the eFI from linked healthcare data is feasible and could be used to aide personalised risk stratification and service utilisation in older patients admitted with myocardial infarction.Cumulative incidence plotForest plot with adjusted hazard ratio

Automated Electronic Frailty Index–Identified Frailty Status and Associated Postsurgical Adverse Events

Electronic frailty index (eFI) is an automated electronic health record (EHR)-based tool that uses a combination of clinical encounters, diagnosis codes, laboratory workups, medications, and Medicare annual wellness visit data as markers of frailty status. The association of eFI with postanesthesia adverse outcomes has not been evaluated. To examine the association of frailty, calculated as eFI at the time of the surgical procedure and categorized as fit, prefrail, or frail, with adverse events after elective noncardiac surgery. This cohort study was conducted at a tertiary care academic medical center in Winston-Salem, North Carolina. The cohort included patients 55 years or older who underwent noncardiac surgery of at least 1 hour in duration between October 1, 2017, and June 30, 2021. Frailty calculated by the eFI tool. Preoperative eFI scores were calculated based on available data 1 day prior to the procedure and categorized as fit (eFI score: ≤0.10), prefrail (eFI score: >0.10 to ≤0.21), or frail (eFI score: >0.21). The primary outcome was a composite of the following 8 adverse component events: 90-item Patient Safety Indicators (PSI 90) score, hospital-acquired conditions, in-hospital mortality, 30-day mortality, 30-day readmission, 30-day emergency department visit after surgery, transfer to a skilled nursing facility after surgery, or unexpected intensive care unit admission after surgery. Secondary outcomes were each of the component events of the composite. Of the 33 449 patients (median [IQR] age, 67 [61-74] years; 17 618 females [52.7%]) included, 11 563 (34.6%) were classified as fit, 15 928 (47.6%) as prefrail, and 5958 (17.8%) as frail. Using logistic regression models that were adjusted for age, sex, race and ethnicity, and comorbidity burden, patients with prefrail (odds ratio [OR], 1.24; 95% CI, 1.18-1.30; P < .001) and frail (OR, 1.71; 95% CI, 1.58-1.82; P < .001) statuses were more likely to experience postoperative adverse events compared with patients with a fit status. Subsequent adjustment for all other potential confounders or covariates did not alter this association. For every increase in eFI of 0.03 units, the odds of a composite of postoperative adverse events increased by 1.06 (95% CI, 1.03-1.13; P < .001). This cohort study found that frailty, as measured by an automatically calculated index integrated within the EHR, was associated with increased risk of adverse events after noncardiac surgery. Deployment of eFI tools may support screening and possible risk modification, especially in patients who undergo high-risk surgery.

Understanding the Contribution of Community Organisations to Healthy Ageing and Integrated Place-Based Care: Evidence from Integrated Care Data.

(1) Background. There is interest in the role community organisations can play to support healthy ageing and the integration of health and social care. This study explored the contribution community organisations can make to this goal through the Leeds (UK) Neighbourhood Networks (LNNs), a novel example of community-based support. (2) Methods. An observational study of 148 LNN beneficiaries compared to the Leeds population aged 64 and over (n = 143,418) using the Leeds Data Model, and an analytical resource developed to support care planning. Measures included demographic characteristics, Electronic Frailty Index (EFI), the number of long-term health conditions (LTCs), and public health management cohort categorisation. (3) Results. LNN's are primarily focussed on older people who are fit (44 percent) or experiencing the onset of LTCs (27 percent) and/or mild frailty (41 percent). However, they also support smaller numbers of people with moderate/severe frailty (15 percent) and five or more long-term conditions (19 percent). (4) Conclusions. Community organisations are well placed to support the ambitions of integrated care by providing support for older people with mild to moderate health and care needs. They also have the capacity to support older people with more severe needs if resourced to do so.