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Background Low and high body mass index (BMI) are reported to be associated with frailty in older adults. Since the optimal BMI associated with a low risk of mortality varies with age, the association between BMI and frailty might also differ by age. This study examined the association between BMI and frailty in middle-aged and older adults in Japan. Methods We conducted face-to-face and mail surveys in Settsu city and mail survey in Hannan city in Osaka, Japan. The association between BMI and frailty was analyzed among 8,815 participants using mail surveys. Frailty was evaluated using two tools, the Kihon Checklist (KCL) and the Frailty Screening Index (FSI). BMI (kg/m 2 ) was categorized into the < 18.5, ≥ 18.5–<20.0, ≥ 20–<22.5, ≥ 22.5–<25.0, ≥ 25.0–<27.5, and ≥ 27.5 kg/m 2 groups. We analyzed the association between BMI and frailty using multivariable logistic regression, with BMI ≥ 22.5–<25.0 kg/m 2 as the reference to calculate the odds ratios (OR) and 95% confidence intervals (95%CI). Restricted cubic spline analyses were also performed, with knots placed at the 5th, 50th (as reference), and 95th percentiles of BMI. We performed all analyses separately for the < 65 and ≥ 65 years age groups. Results BMI < 18.5 (OR = 1.882, 95%CI: 1.263–2.805) was significantly associated with KCL-measured frailty in individuals aged < 65 years, and BMI < 18.5 (OR = 1.807, 95%CI: 1.291–2.531) and ≥ 27.5 (OR = 1.562, 95%CI: 1.156–2.111) were significantly associated with KCL-measured frailty in those aged ≥ 65 years. BMI ≥ 25.0–<27.5 (OR = 1.426, 95%CI: 1.055–1.927) and ≥ 27.5 (OR = 1.473, 95%CI: 1.093–1.985) were significantly associated with FSI-measured frailty in individuals aged < 65 years, and BMI ≥ 27.5 (OR = 1.988, 95%CI: 1.432–2.759) was significantly associated with frailty in those aged ≥ 65 years. The spline models showed U-shaped associations for KCL-measured frailty for both age groups, a positive linear association for FSI-measured frailty among those aged < 65 years, and an L-shaped association for FSI-measured frailty among those aged ≥ 65 years. Conclusion The association between BMI and frailty differed by age. The collation of all the results of this study suggests that both low and high BMI are associated with frailty in middle-aged and older adults. Based on the results, it is speculated lifestyle habits that promote proximity to “normal weight” may help prevent frailty. Trial registration UMIN000008105 (Registration date: May 29th 2019; Website: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000042027 ).

Background/Objectives: Simple frailty assessments, such as the clinical frailty scale (CFS), are prognostic for worse outcomes in older adults with cancer and could support treatment decision-making. This interview study aims to explore clinicians’ experiences of using simple frailty assessments in oncology, including the impacts on patient care and barriers and facilitators to successful implementation. Methods: Semi-structured individual interviews were conducted with clinicians at three UK sites that had implemented CFS screening in lung cancer clinics as part of a national pilot, to explore how frailty assessments are applied and are impacting care. Purposive sampling targeted a range of professionals involved in assessing frailty and making treatment decisions. Recordings were transcribed verbatim and analysed thematically. Results: Ten clinicians participated, and four main themes were identified. ‘Assessing fitness and frailty’ explores the central role of performance status (PS), as well as its limitations, and what frailty assessments add. ‘Scoring and interpreting CFS’ describes the ease and relative yield of CFS use, particularly for patients with ‘borderline’ PS scores (e.g., PS 1–2 or 2–3), and the importance of contextual interpretation. ‘Role of frailty and impacts of assessment’ highlights how frailty assessments can enhance patient-centered care and support, and clinical and shared decision-making, with potential for streamlined care and system-level benefits. ‘Barriers and facilitators to implementation’ are described, including time, culture, guidance, and training, with recommendations provided. Conclusions: Assessing frailty has wide-ranging potential benefits for patients, oncology teams, and the wider system, but barriers must be overcome. Specific recommendations are provided to support the routine implementation of frailty assessments, which is a key step towards the benefits of frailty-informed care being realised at scale.

Background Screening is often recommended as a first step in frailty management. Many guidelines call to implicate frailty screening into practice in the primary care setting. However, few countries or organizations implement it. Understanding and clarifying the stakeholders’ views and issues faced by the implementation is essential to the successful implementation of frailty screening. However, the systematic review on stakeholders’ views of frailty screening in primary care is decidedly limited. Our objective was to explore the perspective of older adults, caregivers, and healthcare providers on frailty screening and determine the enablers and barriers to implementing frailty screening in primary care. Methods A systematic search of six databases and other resources was conducted following JBI’s three-step search strategy. The search resulted in 7362 articles, of which 97 were identified for further assessment according to the inclusion criteria. After the full-text screening, quality assessment and data extraction were carried out using the tools from Joanna Briggs Institute (JBI). Moreover, reviewers used the approach of meta-aggregative of JBI to analyze data and synthesis the findings. Results Six studies were included. A total of 63 findings were aggregated into 12 categories and then further grouped into three synthesized findings:1) capacity of healthcare providers and older adults; 2) opportunity in the implementation of frailty screening; 3) motivation in the implementation of frailty screening. These themes can help identify what influences the implementation of screening from the perspective of stakeholders. Conclusions This meta-synthesis provides evidence on the barriers and enablers of frailty screening in primary care, from the aspects of psychological, physical, social, material, etc. However, stakeholder perspectives of frailty screening have not been adequately studied. More research and efforts are needed to explore the influencing factors and address the existing barriers.

Background COVID-19 pandemic has reminded how older adults with frailty are particularly exposed to adverse outcomes. In the acute care setting, consideration of evidence-based practice related to frailty screening and management is needed to improve the care provided to aging populations. It is important to assess for frailty in acute care so as to establish treatment priorities and goals for the individual. Our study explored understanding on frailty and practice of frailty screening among different acute care professionals in Singapore, and identify barriers and facilitators concerning frailty screening and its implementation. Methods A qualitative study using focus group discussion among nurses and individual interviews among physicians from four departments (Accident & Emergency, Anesthesia, General Surgery, Orthopedics) in three acute hospitals from the three public health clusters in Singapore. Participants were recruited through purposive sampling of specific clinicians seeing a high proportion of older patients at the hospitals. Thematic analysis of the data was performed using NVIVO 12.0. Results Frailty was mainly but inadequately understood as a physical and age-related concept. Screening for frailty in acute care was considered important to identify high risk patients, to implement targeted treatment and care, and to support decision making and prognosis estimation. Specific issues related to screening, management and implementation were identified: cooperation from patient/caregivers, acceptance from healthcare workers/hospital managers, need for dedicated resources, guidelines for follow-up management and consensus on the scope of measurement for different specialties. Conclusion Our findings indicated the need for 1) frailty-related education program for patients/care givers and stakeholders 2) inter-professional collaboration to develop integrated approach for screening and management of hospital patients with frailty and 3) hospital-wide consensus to adopt a common frailty screening tool.

BackgroundPopulation ageing is a worldwide phenomenon; thanks to improvements in medical care and living standards. The Office of National Statistics in the UK predicts that the fastest growing age group in coming decades will be those over 85 years. This is reflected in Trauma Audit and Research Network data, which has highlighted a shift in caseload from a majority of young males to elderly patients at UK Major Trauma Centres (MTC). This study of elderly trauma patients admitted to a UK MTC reviews the links between frailty, using the Canadian Study of Health and Aging Clinical Frailty Scale (CFS), and outcomes from trauma.MethodsA retrospective database review of patients > 65 years old admitted to our MTC was performed. We identified 1125 eligible patients of which 729 had a recorded CFS. Those without a CFS were omitted. The primary outcome measured was in-hospital mortality. Secondary measures were Injury Severity Score, length of stay, trauma team activation on arrival and discharge destination. Multivariate regression analyses were performed using STATA v 15.ResultsThose of CFS 5–9 (frail) were 2.6 times more likely to die than the CFS 1–4 (pre-frail) (OR 2.65, 95% CI 1.47–4.78). The frail group was also 56% less likely to have a trauma call on admission (OR 0.44, 95% CI 0.30–0.65) and 61% less likely to be discharged to their usual place of residence (OR 0.39, 95% CI 0.28–0.55).ConclusionWe advocate the use of the Clinical Frailty Scale as a screening tool for frailty in trauma patients, highlighting those at risk of increased length of stay and mortality, subsequently assisting healthcare providers with setting realistic expectations with family members.Level of evidenceLevel III, prognostic and epidemiological

Introduction: Comprehensive geriatric assessment (CGA) is the gold standard for assessing older adults with cancer. CGA is a complex process which includes multi-domain geriatric evaluation, interventions and follow up. Geriatric oncology screening tools are brief clinical instruments which can help oncologists to quickly identify patients who could benefit from a CGA. Common tools are the G8, Vulnerable Elders Survey (VES-13), and the Frailty Phenotype (FP). Screening tools are traditionally validated based on their ability to predict for abnormal domains on CGA. Objectives: Analyze the incidence of frailty in patients over 75 years of age treated in external Medical Oncology Consultations for the first time and its relationship with impairments in the nutritional, functional, cognitive and social status. Methods: - All patients with cancer older than 75 years undergo a geriatric evaluation of an assessment of the following domains: nutritional (MST), socio-family (Gijon), functional (Barthel), cognitive (Pfeiffer) and a screening of frailty (G8). - Create a database to collect and analyze of all information Results:  Between July 1, 2017 and May 1, 2018, 139 patients with cancer older than 75 years were evaluated. 50.7% were women and 49.3% men, with a median age of 79 years. In 52.3% they debuted in stage IV.The median number of drugs the patients took was 6 and the median on the Charlson comorbidity scale 2. The median on the visual analog pain scale (VAS) was 1. 72.9% of the patients evaluated received some kind of antineoplastic treatment and 10.9% needed a dose reduction. The results of the geriatric assessment scales were: - Nutritional status. The nutritional assessment was made using the MST scale. In 36.1% of patients, the result was a risk of malnutrition (≥2). - Socio-family status. This domain was analyzed with the Gijon scale. 11.2% had some social risk factor (> 7) - Functional status. The Barthel scale was used to evaluate the functional status. 24.4% showed some degree of dependence (<90) - Cognitive status. 15.5% of the patients presented cognitive deterioration (from mild to severe) after evaluating the cognitive status using the Pfeiffer scale (>2) - Frailty screening. Frailty screening was performed using the G8 scale. 22.3% were frail according to this screening (≤14)  Conclusion: Patients with a positive result in the screening of frailty according to the G8 scale are the patients with the most alterations in the scales that evaluate the functional, cognitive, nutritional and socio-family domains. Therefore it seems a good screening tool for its ability to detect patients who have abnormal domains on a comprehensive geriatric assessment. We must continue working in this direction to be able to select the best treatment in elderly patients with cancer with the intention of not over or  undertreating.

Background Qualitative studies suggest discrepancies between older adults’ self-perceived and measured frailty. Quantification of this is limited. This study investigated the relationship between older adults’ self-perceived frailty and a measure of frailty derived from electronic heath record data (electronic Frailty Index [eFI] score). The eFI is derived routinely from available primary care electronic health record data and is based on the cumulative deficit model of frailty. Methods One thousand people (≥ 70 years), randomly selected from a GP practice, were sent a survey, asking them to rate their frailty (ordinal and binary scale), and complete self-rated health (SRH) and PRISMA-7 questionnaires. We analysed (a) agreement between self-perceived frailty (ordinal scale) and eFI categorised frailty; (b) discrimination of self-report measures for eFI defined frailty (threshold ≥ 0.12); and (c) predictors of self-perceived frailty (logistic regressions). Results 375 people were analysed (median age 76, 51% female). Agreement was ‘fair’ between self-perceived frailty and eFI (linear weighted Kappa 0.25, quadratic weighted Kappa 0.37). Agreement was higher with linear and quadratic weighted Gwet’s second order agreement co-efficient [AC2]), (0.65 and 0.81 respectively). As eFI increased, agreement with self-perceived frailty decreased. Disagreements commonly reflected self-perceived frailty reported as less severe than eFI. Self-perceived frailty poorly discriminated eFI defined frailty (AUC 0.59, 95%CI 0.55-0.63) as did SRH, while PRISMA-7 reached moderate discrimination (AUC 0.71, 95%CI 0.66-0.76). The optimal eFI cut-point for discriminating self-perceived frailty was 0.17. A multivariable regression model revealed increasing age (OR 1.10 per year, 95%CI 1.02-1.18) and depression (OR 1.51, 95%CI 1.31-1.74) were associated with self-perceived frailty, however, sex, anxiety, eFI score and deprivation were not. Conclusions The mismatch between self-perceived and eFI categorised frailty has implications for the social acceptability of screening and for meaningful engagement with frailty interventions including advance care planning.

Protein-energy malnutrition (PEM)/undernutrition and frailty are prevalent, overlapping conditions impacting on functional and health outcomes of older adults, but are frequently unidentified and untreated in community settings in the United States. Using the World Health Organization criteria for effective screening programs, we reviewed validity, reliability, and feasibility of data-driven screening tools for identifying PEM and frailty risk among community-dwelling older adults. The SCREEN II is recommended for PEM screening and the FRAIL scale is recommended as the most promising frailty screening tool, based on test characteristics, cost, and ease of use, but more research on both tools is needed, particularly on predictive validity of favorable outcomes after nutritional/physical activity interventions. The Malnutrition Screening Tool (MST) has been recommended by one expert group as a screening tool for all adults, regardless of age/care setting. However, it has not been tested in US community settings, likely yields large numbers of false positives (particularly in community settings), and its predictive validity of favorable outcomes after nutritional interventions is unknown. Community subgroups at highest priority for screening are those at increased risk due to prior illness, certain demographics and/or domiciliary characteristics, and those with BMI < 20 kg/m 2 or < 22 if > 70 years or recent unintentional weight loss > 10% (who are likely already malnourished). Community-based health professionals can better support healthy aging by increasing their awareness/use of PEM and frailty screening tools, prioritizing high-risk populations for systematic screening, following screening with more definitive diagnoses and appropriate interventions, and re-evaluating and revising screening protocols and measures as more data become available.

Background Comprehensive frailty assessment is crucial for longitudinal care in older adults but is often impractical in community, rural, or understaffed settings. This study aimed to determine key identifiers of frailty from different assessment tools and use them to develop a screening tool for frailty status using data from community-dwelling older adults in Bangkok, Thailand. Methods This cross-sectional study recruited 848 community-dwelling participants aged above 60 years during October 2022 and November 2023. Comprehensive data, including demographics, socioeconomic status, physical health, lifestyle, and laboratory results were collected. Frailty status was determined using Fried Frailty Phenotype (FFP), the FRAIL scale, Kihon Checklist (KCL), and Thai Frailty Index (TFI). A decision tree classification model was developed using an exhaustive CHAID algorithm, with variables selected via initial screening for multicollinearity and multinomial logistic regression. The dataset was split into training (80%) and testing (20%) sets for validation. Results Frailty status varied across assessment tools and showed some discrepancies. The final three-layer decision tree using five key predictors: 4-meter gait speed lower than 1 m/s, unintentional weight loss in one year more than 5%, experiencing difficulties in chewing hard/solid food, KCL1 (Do you go out by bus or train by yourself? ), and KCL25 (In the last 2 weeks, have you felt tired without a reason? ). Gait speed was identified as the most important predictor. The model achieved an overall accuracy of 0.751 on the training data and 0.776 on the testing data. It demonstrated strong performance in identifying robust individuals, with a correct classification rate (recall) of 1.000 in the testing dataset, and 0.603 and 0.731, for pre-frail and frail individuals respectively, in the testing dataset. The area under the receiver operating characteristic curve (AUC) values were 0.896 for robust, 0.740 for pre-frail, and 0.813 for frail classifications. For practicality, the decision tree can be collapsed down to only 3 questions to identify “non-robust” individuals for comprehensive frailty assessment referral. Conclusions Our study developed a practical and scalable data-driven decision tree for rapid community-based frailty identification. By combining a simple physical test (gait speed), percentage of weight loss, and a specific KCL item, this tool offers a cost-effective method to classify frailty status, particularly robust individuals. Its simplicity makes it suitable for use even by less experienced staff, supporting early identification and referral of at-risk older adults who would benefit from timely intervention.

Electronic medical record (EMR) systems in primary care present an opportunity to address frailty, a significant health concern for older adults. Researchers in the UK used Read codes to develop a 36-factor electronic frailty index (eFI), which produces frailty scores for patients in primary care settings. We aimed to translate the 36-factor eFI to a Canadian context. We used manual and automatic mapping to develop a coding set based on standardized terminologies used in Canada to reflect the 36 factors of the eFI. Manual mapping was completed independently by two coders, followed by group consensus among the research team. Automatic mapping was completed using Apelon TermWorks. We then used EMR data from the British Columbia Canadian Primary Care Sentinel Surveillance Network. We searched structured data fields related to diagnoses and reasons for patient visits to develop a list of free text terms associated with any of the 36 factors. A total of 3768 terms were identified; 3021 were codes. A total of 747 free text terms were identified from 527,521 reviewed data entries. Of the 36 frailty factors, 24 were captured mostly by codes; 7 mostly by free text; and 4 approximately equally by codes and free text. Three key findings emerged from this study: (1) It is difficult to capture frailty using only standardized terminologies currently used in Canada and a combination of standardized codes and free text terms better captures the complexity of frailty; (2) EMRs in primary care can be better optimized; (3) Output from this study allows for the development of a frailty screening algorithm that could be implemented in primary care settings to improve individual and system level outcomes related to frailty.