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BackgroundHalf of those undergoing major lower limb amputation for peripheral arterial disease die within 1 year. Advance care planning reduces days in hospital and increases the chance of dying in a preferred place.AimTo investigate the prevalence and content of advance care planning for people having a lower limb amputation due to acute or chronic limb-threatening ischaemia or diabetes. Secondary aims were to explore its association with mortality, and length of hospital stay.DesignA retrospective observational cohort study. The intervention was advance care planning.Setting/participantsPatients admitted to the South West England Major Arterial Centre between 1 January 2019 and 1 January 2021 who received unilateral or bilateral below, above, or through knee amputation due to acute or chronic limb-threatening ischaemia or diabetes.Results116 patients were included in the study. 20.7% (n = 24) died within 1 year. 40.5% (n = 47) had an advance care planning discussion of which all included cardiopulmonary resuscitation decisions with few exploring other options. Patients who were more likely to have advance care planning discussions were ≥75 years (aOR = 5.58, 95%CI 1.56–20.0), female (aOR = 3.24, 95%CI 1.21–8.69), and had multimorbidity (Charlson Comorbidity Index ≥5, aOR = 2.97, 95%CI 1.11–7.92). Discussions occurred more often in the emergency pathway and were predominantly initiated by physicians. Advance care planning was associated with increased mortality (aHR = 2.63, 95%CI 1.01, 5.02) and longer hospital stay (aHR = 0.52, 95%CI 0.32–0.83).ConclusionsDespite a high risk of death for all patients in the months following amputation, advance care planning occurred in fewer than half of people and mostly focused on resuscitation.

Background Effective shielding measures and virus mutations have progressively modified the disease between the waves, likewise healthcare systems have adapted to the outbreak. Our aim was to compare clinical outcomes for older people with COVID-19 in Wave 1 (W1) and Wave 2 (W2). Methods All data, including the Clinical Frailty Scale (CFS), were collected for COVID-19 consecutive patients, aged ≥65, from 13 hospitals, in W1 (February–June 2020) and W2 (October 2020–March 2021). The primary outcome was mortality (time to mortality and 28-day mortality). Data were analysed with multilevel Cox proportional hazards, linear and logistic regression models, adjusted for wave baseline demographic and clinical characteristics. Results Data from 611 people admitted in W2 were added to and compared with data collected during W1 (N = 1340). Patients admitted in W2 were of similar age, median (interquartile range), W2 = 79 (73–84); W1 = 80 (74–86); had a greater proportion of men (59.4% vs. 53.0%); had lower 28-day mortality (29.1% vs. 40.0%), compared to W1. For combined W1–W2 sample, W2 was independently associated with improved survival: time-to-mortality adjusted hazard ratio (aHR) = 0.78 [95% confidence interval (CI) 0.65–0.93], 28-day mortality adjusted odds ratio = 0.80 (95% CI 0.62–1.03). W2 was associated with increased length of hospital stay aHR = 0.69 (95% CI 0.59–0.81). Patients in W2 were less frail, CFS [adjusted mean difference (aMD) = −0.50, 95% CI −0.81, −0.18], as well as presented with lower C-reactive protein (aMD = −22.52, 95% CI −32.00, −13.04). Conclusions COVID-19 older adults in W2 were less likely to die than during W1. Patients presented to hospital during W2 were less frail and with lower disease severity and less likely to have renal decline.

Frailty, a concept which defies consensus definition is nevertheless widely acknowledged to be an important and growing public health issue facing our ageing population [1]. It has become the focus of much research aiming to mitigate its costly adverse outcomes; falls and increased fracture risk, hospitalisation, institutionalisation, dependency and death [2]. Individuals living with frailty are living in an increasingly precarious situation, where insults or shocks (events such as an infection or a sudden bereavement) become harder for their bodies’ physiology and social support networks to withstand. In the UK, the 2010 Equalities Act enshrines in law the right to protection from discrimination because of certain protected characteristics, including age and disability [3]. Frailty is not specified as a protected characteristic. However, frailty can be thought of as aligned with disability; given that physical dependency is a common outcome, and overlapping with age; given that frailty, while not an inevitable consequence, becomes more common as we age [4]. In our attempt to offer appropriate healthcare for older people, frailty screening has become the norm in UK primary and secondary healthcare [5, 6]. Particularly ubiquitous is Rockwood’s Clinical Frailty scale, a pictorial and vignette grading system developed from the Canadian Study of Health and Aging’s frailty index [7]. Guidelines now recommend screening based on our electronic healthcare records and for unplanned hospital admissions [8-10]. This recognition of frailty, suggests a recognition of differential care needs, based on frailty’s association with increased risk. However, balancing an assessment of population risk with individualised care is challenging, and frailty-based discrimination could result from both system-level or individual-level problems getting this right. Objectives The aim of this scoping review is to explore frailty-based discrimination, or ‘frailism’, in order to better characterise the phenomenon and its effects, to examine the extent of its inclusion in any text, and identify gaps for future investigation [10]. Findings may suggest a literature base for a narrower population systematic review. 1. Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019 Oct 12;394(10206):1365-75. 2. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013 Mar 02;381(9868):752-62. 3. Equality and Human Rights Commission. Equality Act 2010. 2018 [cited 2022 11th March]; Available from: https://www.equalityhumanrights.com/en/equality-act/equality-act-2010. 4. Junius-Walker U, Onder G, Soleymani D, Wiese B, Albaina O, Bernabei R, et al. The essence of frailty: A systematic review and qualitative synthesis on frailty concepts and definitions. Eur J Intern Med. 2018 Oct;56:3-10. 5. Church S, Rogers E, Rockwood K, Theou O. A scoping review of the Clinical Frailty Scale. BMC Geriatr. 2020 Oct 7;20(1):393. 6. Clegg A, Bates C, Young J, Ryan R, Nichols L, Ann Teale E, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016 May;45(3):353-60. 7. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005 Aug 30;173(5):489-95. 8. National Institute for Health and Care Excellence. Multimorbidity: clinical assessment and management. NICE; 2016 [cited 2022 11th March]; Available from: https://www.nice.org.uk/guidance/ng56. 9. NHS England. NHS RightCare: Frailty Toolkit Optimising a frailty system. https://www.england.nhs.uk/rightcare/products/pathways/frailty/: NHS England; 2019 [cited 2022 11th March]; Available from: https://www.england.nhs.uk/rightcare/wp-content/uploads/sites/40/2019/07/frailty-toolkit-june-2019-v1.pdf. 10. Clegg A, Rockwood K, Romero-Ortuno R, Cunningham C. Silver Book II: Frailty. In: Rockwood K, editor. Silver Book II Quality care for older people with urgent care needs2021.

Additional file 1.

Supplemental material, sj-pdf-1-taw-10.1177_2042098620985690 for Routine use of immunosuppressants is associated with mortality in hospitalised patients with COVID-19 by Phyo K. Myint, Ben Carter, Fenella Barlow-Pay, Roxanna Short, Alice G. Einarsson, Eilidh Bruce, Kathryn McCarthy, Alessia Verduri, Jemima Collins, James Hesford, Frances Rickard, Emma Mitchell, Mark Holloway, Aine McGovern, Arturo Vilches-Moraga, Philip Braude, Lyndsay Pearce, Michael Stechman, Angeline Price, Terence J. Quinn, Enrico Clini, Susan Moug and Jonathan Hewitt in Therapeutic Advances in Drug Safety

Background In response to the COVID-19 pandemic, many countries mandated staying at home to reduce transmission. This study examined the association between living arrangements (house occupancy numbers) and outcomes in COVID-19. Methods Study population was drawn from the COPE study, a multicentre cohort study. House occupancy was defined as: living alone; living with one other person; living with multiple other people; or living in a nursing/residential home. Outcomes were time from admission to mortality and discharge (Cox regression), and Day 28 mortality (logistic regression) analyses were adjusted for key comorbidities and covariates including admission: age, sex, smoking, heart failure, admission C-reactive protein (CRP), chronic obstructive pulmonary disease, estimated glomerular filtration rate, frailty and others. Results A total of 1584 patients were included from 13 hospitals across UK and Italy: 676 (42.7%) were female, 907 (57.3%) were male, median age was 74 years (range: 19–101). At 28 days, 502 (31.7%) had died. Median admission CRP was 67, 82, 79.5 and 83 mg/l for those living alone, with someone else, in a house of multiple occupancy and in a nursing/residential home, respectively. Compared to living alone, living with anyone was associated with increased mortality: within a couple [adjusted hazard ratios (aHR) = 1.39, 95% confidence intervals (CI) 1.09–1.77, P = 0.007]; living in a house of multiple occupancy (aHR = 1.67, 95% CI 1.17–2.38, P = 0.005); and living in a residential home (aHR = 1.36, 95% CI 1.03–1.80, P = 0.031). Conclusion For patients hospitalized with COVID-19, those living with one or more people had an increased association with mortality, they also exhibited higher CRP indicating increased disease severity suggesting they delayed seeking care.