Peter Hartley1, Jennifer Adamson1, Carol Cunningham1, Georgina Embleton2, Roman Romero-Ortuno3,4. 1. Department of Physiotherapy, Addenbrooke's Hospital, Cambridge, United Kingdom. 2. Department of Physiotherapy, Luton and Dunstable Hospital, Luton, United Kingdom. 3. Department of Medicine for the Elderly, Addenbrooke's Hospital, Cambridge, United Kingdom. 4. Clinical Gerontology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
Abstract
AIM: Frailty predicts inpatient mortality and length of stay, but its link to functional trajectories is under-researched. Addenbrooke's Hospital, Cambridge, UK, collects the Clinical Frailty Scale (CFS) within 72 h of admission for those aged ≥75 years. We studied whether the CFS links to functional trajectories in hospitalized older adults. METHODS: This was a retrospective observational study in an English university hospital. We analyzed all first episodes of county residents aged ≥75 years admitted to the Department of Medicine for the Elderly wards between December 2014 and May 2015. Data were extracted from the hospital's information systems. Patients were classified as non-frail (CFS 1-4), moderately frail (CFS 5-6) and severely frail (CFS 7-8). Function was retrospectively measured with the modified Rankin Scale (mRS) at preadmission, admission and discharge. RESULTS: Of 539 eligible patients, 46 died during admission (mortality rates: 2% in CFS 1-4, 5% in CFS 5-6, 19% in CFS 7-8). Among the 493 survivors, 121 were non-frail, 235 moderately and 137 severely frail. The mean mRS of the non-frail was 1.8 (95% CI 1.7-2.0) at baseline, 3.3 (95% CI 3.1-3.5) on admission and 2.2 (95% CI 2.0-2.3) on discharge (mean length of stay 9 days). The moderately frail had a mean mRS of 2.9 (95% CI 2.8-3.0) at baseline, 4.0 (95% CI 3.8-4.1) on admission and 3.2 (95% CI 3.1-3.3) on discharge (mean length of stay 15 days). The severely frail had mean mRS of 3.5 (95% CI 3.3-3.6) at baseline, 4.3 (95% CI 4.1-4.4) on admission and 3.7 (95% CI 3.6-3.9) on discharge, respectively (mean length of stay 17 days). CONCLUSIONS: In older inpatients, frailty might be linked to lower and slower functional recovery. Prospective work is required to confirm these trajectories and understand how to influence them. Geriatr Gerontol Int 2017; 17: 1063-1068.
AIM: Frailty predicts inpatient mortality and length of stay, but its link to functional trajectories is under-researched. Addenbrooke's Hospital, Cambridge, UK, collects the Clinical Frailty Scale (CFS) within 72 h of admission for those aged ≥75 years. We studied whether the CFS links to functional trajectories in hospitalized older adults. METHODS: This was a retrospective observational study in an English university hospital. We analyzed all first episodes of county residents aged ≥75 years admitted to the Department of Medicine for the Elderly wards between December 2014 and May 2015. Data were extracted from the hospital's information systems. Patients were classified as non-frail (CFS 1-4), moderately frail (CFS 5-6) and severely frail (CFS 7-8). Function was retrospectively measured with the modified Rankin Scale (mRS) at preadmission, admission and discharge. RESULTS: Of 539 eligible patients, 46 died during admission (mortality rates: 2% in CFS 1-4, 5% in CFS 5-6, 19% in CFS 7-8). Among the 493 survivors, 121 were non-frail, 235 moderately and 137 severely frail. The mean mRS of the non-frail was 1.8 (95% CI 1.7-2.0) at baseline, 3.3 (95% CI 3.1-3.5) on admission and 2.2 (95% CI 2.0-2.3) on discharge (mean length of stay 9 days). The moderately frail had a mean mRS of 2.9 (95% CI 2.8-3.0) at baseline, 4.0 (95% CI 3.8-4.1) on admission and 3.2 (95% CI 3.1-3.3) on discharge (mean length of stay 15 days). The severely frail had mean mRS of 3.5 (95% CI 3.3-3.6) at baseline, 4.3 (95% CI 4.1-4.4) on admission and 3.7 (95% CI 3.6-3.9) on discharge, respectively (mean length of stay 17 days). CONCLUSIONS: In older inpatients, frailty might be linked to lower and slower functional recovery. Prospective work is required to confirm these trajectories and understand how to influence them. Geriatr Gerontol Int 2017; 17: 1063-1068.
Authors: Henry de Berker; Archy de Berker; Htin Aung; Pedro Duarte; Salman Mohammed; Hamsaraj Shetty; Tom Hughes Journal: Clin Med (Lond) Date: 2021-03 Impact factor: 2.659
Authors: Joe Hollinghurst; Gemma Housley; Alan Watkins; Andrew Clegg; Thomas Gilbert; Simon P Conroy Journal: Age Ageing Date: 2021-06-28 Impact factor: 10.668
Authors: Peter Hartley; Victoria L Keevil; Kate Westgate; Tom White; Søren Brage; Roman Romero-Ortuno; Christi Deaton Journal: Curr Gerontol Geriatr Res Date: 2018-10-18