Frailty is a distinct clinical syndrome wherein the individual has low reserves and is highly vulnerable to internal and external stressors. Although it is associated with disability and multiple comorbidities, it can also be present in individuals who seem healthy. Frailty is multidimensional and its pathophysiology is complex. Early identification and intervention can potentially decrease or reverse frailty, especially in the early stages. Primary care physicians, community nurses and community social networks have important roles in the identification of pre-frail and frail elderly through the use of simple frailty screening tools and rapid geriatric assessments. Appropriate interventions that can be initiated in a primary care setting include a targeted medical review for reversible medical causes of frailty, medication appropriateness, nutritional advice and exercise prescription. With ongoing training and education, the multidisciplinary engagement and coordination of care of the elderly in the community can help to build resilience and combat frailty in our rapidly ageing society.

Introduction: Our aim was to study the prevalence of frailty and its associated factors in a subacute geriatric ward. Methods: This was a cross-sectional study of 167 participants between June 2018 and June 2019. Baseline demographics and participants’ Mini Nutritional Assessment, Geriatric Depression Scale, Mini Mental State Examination, Charlson’s Comorbidity Index and LACE index scores were obtained. Functional measurements such as modified Barthel’s Index scores and hand grip strength (HGS) were taken. Frailty was assessed using the Clinical Frailty Scale (CFS) and the FRAIL scale. Data on history of healthcare utilisation, medications, length of stay, selected blood investigations and presence of geriatric syndromes was also collected. Results: The prevalence of pre-frailty (CFS 4) and frailty (CFS ≥ 5) was 16.2% and 63.4%, respectively. There were significant associations between CFS and age (pre-frail vs. non-frail: odds ratio [OR] 1.14, 95% confidence interval [CI] 1.04–1.25, p = 0.006; frail vs. non-frail: OR 1.08, 95% CI 1.01–1.15, p = 0.021), HGS at discharge (frail vs. non-frail: OR 0.90, 95% CI 0.82–0.99, p = 0.025), serum albumin (frail vs. non-frail: OR 0.90, 95% CI 0.82–0.99, p = 0.035) and the presence of urinary incontinence (frail vs. non-frail: OR 3.03, 95% CI 1.19–7.77, p = 0.021). Conclusion: Frailty is highly prevalent in the subacute geriatric setting and has many associated factors. In this study, independent factors associated with frailty were age, HGS at discharge, serum albumin and urinary incontinence. This has implications for future resource allocation for frail older inpatients and may help direct further research to study the effectiveness of frailty-targeted interventions.

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Background. Unplanned readmission to hospital is common among older adults and contributes to considerable healthcare costs and hospital-associated complications. We aimed to identify predictors of 30-day post-discharge unplanned readmission among older adults in our subacute geriatric ward, and to determine the prevalence of geriatric syndromes and develop a new predictive model for readmission of subacute geriatric patients.Methods: Consecutive patients admitted to our subacute geriatric ward between June 2018 and June 2019 were invited to participate. Data collected included patient age, sex, weight, height, race, type of housing, destination upon discharge, functional and frailty status, presence of conduits (urinary catheters and nasogastric tubes), polypharmacy, high-risk medications, healthcare utilisation 6 months prior, laboratory test results, length of hospital stay, Charlson Comorbidity Index, and LACE index. Patients were assessed using the Mini Nutritional Assessment -Short Form, Geriatric Depression Scale, Mini-Mental State Examination, Clinical Frailty Scale, FRAIL scale, modified Barthel Index, hand grip strength, and gait speed. Patients with or without 30-day post-discharge unplanned readmission were compared. Multivariate logistic regression was used to identify independent predictors. Results: Of 284 patients followed up at 30 days post-discharge, 63 (22.2%) had unplanned hospital readmission within 30 days of discharge, with associated factors being history of myocardial infarction, moderate or severe liver or renal disease, low albumin level, history of emergency department visits, hospitalisation in the preceding 6 months, and discharge to a destination other than home. The prevalence of geriatric syndromes of falls, frailty, and immobility was 62.3%, 64.7%, and 86.6%, respectively. Independent predictors of 30-day post-discharge unplanned readmission were history of hospitalisation in the preceding 6 months (odds ratio=2.62, p=0.045) and discharge destination other than home (odds ratio=3.10, p=0.006). The area under the receiver operating characteristics curve for the predictive models was between 0.6 and 0.7, and Brier score was around 0.16. The discrimination ability of the models was weak. Conclusion:History of hospitalisation in the preceding 6 months and not being discharged to home were independent predictors for 30-day post-discharge unplanned readmission.