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Clinical Frailty Scale. 7 © 2007-2009. Version 1.2. All rights reserved. Geriatric Medicine Research, Dalhousie University, Halifax, Canada. Permission granted to copy for research and educational purposes only. 

Clinical Frailty Scale. 7 © 2007-2009. Version 1.2. All rights reserved. Geriatric Medicine Research, Dalhousie University, Halifax, Canada. Permission granted to copy for research and educational purposes only. 

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Article
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The performance of acute medical units (AMUs) against published quality indicators is variable. We aimed to identify the impact of case-mix and unit resources on timely assessment and discharge of patients admitted to 43 AMUs on a single day in June 2013, as part of the Society for Acute Medicine's benchmarking audit 2013. Performance against quali...

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... This is particularly relevant when trying to conceptualize an acute care system optimized for the challenges ahead. Frailty and severe acute illness tend to occur in tandem [14]. Modern services must be able to accommodate these elements simultaneously. ...
Article
Recent trends across Europe show a year-on-year increase in the number of patients with acute medical illnesses presenting to hospitals, yet there are no plans for a substantial expansion in acute hospital infrastructure or staffing to address demand. Strategies to meet increasing demand need to consider the fact that there is limited capacity in acute hospitals and focus on new care models in both hospital and community settings. Increasing the efficiency of acute hospital provision by reducing the length of stay entails supporting acute ambulatory care, where patients receive daily acute care interventions but do not stay overnight in the hospitals. This approach may entail daily transfer between home and an acute setting for ongoing treatment, which is unsuitable for some patients living with frailty. Acute hospital at home (HaH) is a care model which, thanks to advances in point of care diagnostic capability, can provide a credible model of acute medical assessment and treatment without the need for hospital transfer. Investment and training to support scaling up of HaH are key strategic aims for integrated healthcare systems. Abstract
... Here, we show that for every 1-point increment in the worst NEWS2-score during hospital stay, the mortality hazard ratios during a 20-month follow-up period increased by 14% (unadjusted HR 1.14 (1.04-1.24). NEWS2 correlated moderately with CFS scores, suggesting that patients with higher levels of frailty were sicker--a finding in agreement with others [2,19]. Although multicollinearity assessment between NEWS2 and CFS was negative, it should be noted that the HR estimate for NEWS2 approached 1.00 in adjusted analysis (1.09 (1.00-1.20)). ...
Article
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Purpose Study associations between frailty, illness severity and post-discharge survival in older adults admitted to medical wards with acute clinical conditions. Methods Prospective cohort study of 195 individuals (mean age 86; 63% females) admitted to two medical wards with acute illness, followed up for all-cause mortality for 20 months after discharge. Ward physicians screened for frailty and quantified its degree from one to eight using Clinical Frailty Scale (CFS), while clinical illness severity was estimated by New Early Warning Score 2 (NEWS2) and laboratory illness severity was calculated by a frailty index (FI-lab) using routine blood tests. Results CFS, NEWS2 and FI-lab scores were independently associated with post-discharge survival in an adjusted Cox proportional hazards model with age, ward category (acute geriatric and general medical) and comorbidity as covariates. Adjusted hazard ratios and 95% confidence intervals were 1.54 (1.24–1.91) for CFS, 1.12 (1.03–1.23) for NEWS2, and 1.02 (1.00–1.05) for FI-lab. A frailty × illness severity category interaction effect ( p = 0.003), suggested that the impact of frailty on survival was greater in those experiencing higher levels of illness severity. Among patients with at least moderate frailty (CFS six to eight) and high illness severity according to both NEWS2 and FI-lab, two (13%) were alive at follow-up. Conclusion Frailty screening aided prognostication of survival following discharge in older acutely ill persons admitted to medical wards. The prognostic value of frailty increased when combined with readily available illness severity markers acquired during admission.
... Furthermore, our data concur with previous literature associating male gender and NEWS with poorer COVID-19 outcomes in the elderly [50À59]. Of note previous pioneering work in the acute setting, prior to the COVID-19 pandemic, had already indicated that higher NEWS and CFS correlate, predicting illness severity, mortality and readmission rate, in the frailer population [59]. ...
Article
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Background: COVID-19 has resulted in the largest pandemic experienced since 1918, accounting for over 2 million deaths globally. Frail and older people are at the highest risk of mortality. The main objective of the present research was to quantify the impact of clinical frailty scale (CFS) by increasing severity of frailty and to identify other personal prognostic factors associated with increased mortality from COVID-19. Methods: This study offers a contemporary systematic review and meta-analysis to analyse the stratified mortality risk by increasing CFS sub-categories (1-3, 4-5 and 6-9). Databases searched included EMBASE, MEDLINE, CAB Abstracts, PsychInfo, and Web of Science with end-search restriction the 18th December 2020. Publications identified via MedRevix were followed up on the 23rd March 2021 in peer-reviewed database search, and citations were updated as published. Prospective and retrospective cohort studies which reported the association between CFS and COVID-19 mortality were included. Thirty-four studies were eligible for systematic review and seventeen for meta-analysis, with 81-87% (I2) heterogeneity. Findings: All studies [N: 34] included patients from a hospital setting, comprising a total of 18,042 patients with mean age 72.8 (Min: 56; Max: 86). The CFS 4-5 patient group had significantly increased mortality when compared to patients with CFS 1-3 [(RE) OR 1.95 (1.32 (95% CI), 2.87 (95% CI)); I2 81%; p = 0.0008]. Furthermore, CFS 6-9 patient group displayed an even more noticeable mortality increase when compared to patients with CFS 1-3 [(RE) OR 3.09 (2.03, 4.71); I2 87%; p<0.0001]. Generic inverse variance analysis of adjusted hazard ratio among included studies highlighted that CFS (p = 0.0001), male gender (p = 0.0009), National Early Warning Score (p = 0.0001), Ischaemic Heart Disease (IHD) (p = 0.07), Hypertension (HT) (p<0.0001), and Chronic Kidney Disease (CKD) (p = 0.0009) were associated with increased COVID-19 mortality. Interpretation: Our findings suggest a differential stratification of CFS scores in the context of COVID-19 infection, in which CFS 1-3 patients may be considered at lower risk, CFS 4-5 at moderate risk, and CFS 6-9 at high risk of mortality regardless of age. Overall, our study not only aims to alert clinicians of the value of CFS scores, but also highlight the multiple dimensions to consider such as age, gender and co-morbidities, even among moderately frail patients in relation to COVID-19 mortality. Funding: None.
... Heterogeneity in the groups of patients that units care for in different geographic locations means that any system of measurement needs to adjust for casemix, comparing the care of the more unwell patients, the frailer patients and those presenting with the most common syndromes. The metrics for this have been tested to a degree in the Society for Acute Medicine's Benchmarking Audit (SAMBA): 5,6 Age, gender, severity of illness as measured by the National Early Warning Score, frailty prior to the episode of acute illness and the character of existing support networks (living alone / living with someone / care home resident) all affect outcomes of care. Adjustment of case-mix might be complex. ...
... Equitable can be evidenced by demonstrating that patients from all patient groups including elderly patients and patients in remote geographic locations receive the same standard of care. 6 This is often a difficult argument given the broad range of patients' needs in Acute Care. 21 ...
Article
Acute Medicine is a specialty that is not defined by a single organ system and sits at the interface between primary and secondary care. In order to document improvements in the quality of care delivered a system of metrics is required. A number of frameworks for measurements exist to quantify quality of care at the level of patients, teams and organisations, such as measures of population health, patient satisfaction and cost per patient. Measures can capture whether care is safe, effective, patient-centred, timely, efficient and equitable. Measurement in Acute Medicine is challenged by the often-transient nature of the contact between Acute Medicine clinicians and patients, the lack of diagnostic labels, a low degree of standardisation and difficulties in capturing the patient experience in the context. In a time of increasing ecological and financial constraints, reflecting about the most appropriate metrics to document the impact of Acute Medicine is required.
... The audit has been conducted over a 24-hour period each June since 2012. [1][2][3][4][5][6][7][8][9][10][11][12] This eighth iteration was the largest SAMBA to date. In 2012, 30 units returned data on 1006 patients; in 2019 142 units (373% increase) returned data on 7170 patients (613% increase). ...
Article
Introduction: The eighth Society for Acute Medicine Benchmarking Audit (SAMBA19) took place on Thursday 27th June 2019. SAMBA gives a broad picture of acute medical care in the UK and allows individual units to compare their performance against their peers. Method: All UK hospitals were invited to participate. Unit and patient level were collected. Data were analysed against published Clinical Quality indicators (CQI) and standards. This was the biggest SAMBA to date, with data from 7170 patients across 142 units in 140 hospitals. Results: 84.5% of patients had an Early Warning Score measured within 30 minutes of arrival in hospital (SAMBA18 84.1%), 90.4% of patients were seen by a competent clinical decision maker within four hours of arrival in hospital (SAMBA18 91.4 %) and 68.6% of patients were seen by a consultant within the timeframe standard (SAMBA18 62.7%). Ambulatory Emergency Care is provided in 99.3% of hospitals. 61.8% of patients are initially seen in the Emergency Department (ED). Since SAMBA18 death rates and planned discharge rates, while the use of NEWS2 increased from 2.5% to 59.2% of hospitals. Conclusion: SAMBA19 highlighted the evolving complexity of acute medical pathways for patients. The challenge now is to increase sample frequency, assess the impact of SAMBA open a broader debate to define optimal CQIs.
... The Clinical Frailty Scale, the Frailty Index, and the Frailty Phenotype were the most common tools used to measure frailty (n = 28 each) (Additional file 1: Figure S5). The Clinical Frailty Scale was the most popular measure used in geriatric (n = 6/35; 17%) [34,66,94,110,185,212] and ICU disciplines (n = 6/10; 60%) [103,176,190,192,196,211] (Table 3), in Canada (n = 12/18; 67%) and the UK (n = 8/26; 31%) [103,116,139,180,188,202,207,213], and in observational articles (n = 26/ 202; 13%). The Frailty Index was the most popular measure used in emergency departments (n = 7/33; 21%) [93,106,125,131,163,165,195] and orthopedics (n = 4/ 18; 22%) [126,189,219,220] (Table 3), in Italy (n = 5/19; 26%) [93,104,106,133] and Denmark (n = 3/8; 38%) [189,219,220], and in experimental articles (n = 6/32; 19%) [50,62,166,189,219,220]. ...
Article
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Background: The ability of acute care providers to cope with the influx of frail older patients is increasingly stressed, and changes need to be made to improve care provided to older adults. Our purpose was to conduct a scoping review to map and synthesize the literature addressing frailty in the acute care setting in order to understand how to tackle this challenge. We also aimed to highlight the current gaps in frailty research. Methods: This scoping review included original research articles with acutely-ill Emergency Medical Services (EMS) or hospitalized older patients who were identified as frail by the authors. We searched Medline, CINAHL, Embase, PsycINFO, Eric, and Cochrane from January 2000 to September 2015. Results: Our database search initially resulted in 8658 articles and 617 were eligible. In 67% of the articles the authors identified their participants as frail but did not report on how they measured frailty. Among the 204 articles that did measure frailty, the most common disciplines were geriatrics (14%), emergency department (14%), and general medicine (11%). In total, 89 measures were used. This included 13 established tools, used in 51% of the articles, and 35 non-frailty tools, used in 24% of the articles. The most commonly used tools were the Clinical Frailty Scale, the Frailty Index, and the Frailty Phenotype (12% each). Most often (44%) researchers used frailty tools to predict adverse health outcomes. In 74% of the cases frailty predicted the outcome examined, typically mortality and length of stay. Conclusions: Most studies (83%) were conducted in non-geriatric disciplines and two thirds of the articles identified participants as frail without measuring frailty. There was great variability in tools used and more recently published studies were more likely to use established frailty tools. Overall, frailty appears to be a good predictor of adverse health outcomes. For frailty to be implemented in clinical practice frailty tools should help formulate the care plan and improve shared decision making. How this will happen has yet to be determined.
... 7-10 To capture casemix, the audit uses a measure of severity of illness with the National Early Warning Score (NEWS) 10 and a measure of frailty (Clinical Frailty Scale (CFS)). 11 NEWS correlates with prognosis 12 and length of stay in hospital. 13 Patients who are frailer have a higher risk of death and significant physiological abnormalities. ...
... 13 Patients who are frailer have a higher risk of death and significant physiological abnormalities. 12 SAMBA'15 was the fourth national audit performed by the Society for Acute Medicine. In addition to National CQIs, this audit also examined specialty input into the AMU patient care and patient experience using the Friends and Family tool. ...
... The current SAMBA data set collects data on staffing levels, but the significant variations in shift patterns, inter-disciplinary working and the way in which hospital admission processes are configured have made it difficult in the past to determine which models of service delivery are likely to achieve the best quality of care. 12 The enormous variation in hospital configuration makes comparisons difficult. A system to classify typical configurations of hospitals would help those working in similar units to learn from each other. ...
Article
Background: The Society for Acute Medicine's Benchmarking Audit (SAMBA) annually examines Clinical Quality Indicators (CQIs) of the care of patients admitted to UK hospitals as medical emergencies. Aim: The aim of this study is to review the impact of consultant specialty on discharge decisions in the SAMBA data-set. Design and methods: Prospective audit of patients admitted to acute medical units (AMUs) on 25 June 2015 to participating hospitals throughout the UK with subgroup analysis. Results: Eighty-three units submitted patient data from 3138 patients.Nearly 1845 (58%, IQR for units 50-69%) of patients were referrals from Emergency Medicine, 1072 (32%, IQR for units 24-44%) were referrals from Primary Care. The mean age was 65 (SD 20). One hundred and forty-one (4.5%) patients were admitted from care homes and 951 (30%) of patients were at least 'mildly frail' and 407 (13%) had signs of physiological instability. The median and the mean time to being seen by a doctor were 1 h 20 min and 2 h 3 min, respectively. The median and the mean time to being seen by senior specialist were 3 h 55 min and 5 h 56 min, respectively. By 72 h, 29 (1%) patients had died in the AMU, 73 were admitted to critical care units, 1297 (41%) had been discharged to their own home and 60 to nursing or residential homes. For every 100 patients seen specialists in acute medicine discharged 12 more patients than specialists from other disciplines of medicine (P < 0.001). The difference remained significant after adjustment for case mix. Conclusion: Specialist in acute care might facilitate discharge in a higher proportion of patients.
Article
the Clinical Frailty Scale (CFS) was validated as a predictor of adverse outcomes in community-dwelling older people. In our hospital, the use of the CFS in emergency admissions of people aged ≥ 75 years was introduced under the Commissioning for Quality and Innovation payment framework. we retrospectively studied the association of the CFS with patient characteristics and outcomes. retrospective observational study in a large tertiary university National Health Service hospital in England. the CFS was correlated with transfer to specialist Geriatric ward, length of stay (LOS), in-patient mortality, and 30-day readmission rate. between 1(st) August 2013 and 31(st) July 2014, there were 11271 emergency admission episodes of people aged ≥ 75 years (all specialties), corresponding to 7532 unique patients (first admissions); of those, 5764 had the CFS measured by the admitting team (81% of them within 72 hours of admission). After adjustment for age, gender, Charlson comorbidity index, and history of dementia and/or current cognitive concern, the CFS was an independent predictor of in-patient mortality (OR = 1.60, 95% CI: 1.48 - 1.74, P < 0.001), transfer to Geriatric ward (OR = 1.33, 95% CI: 1.24 - 1.42, P < 0.001), and LOS ≥ 10 days (OR = 1.19, 95% CI: 1.14 - 1.23, P < 0.001). The CFS was not a multivariate predictor of 30-day readmission. the CFS may help predict in-patient mortality and target specialist geriatric resources within the hospital. Usual hospital metrics such as mortality and LOS should take into account measurable patient complexity. © The Author 2015. Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For Permissions, please email: journals.permissions@oup.com.