Jai N Darvall1, Rinaldo Bellomo2, Eldho Paul3, Michael Bailey4, Paul J Young5, Alice Reid5, Kenneth Rockwood6, David Pilcher7. 1. Department of Intensive Care, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Critical Care, The University of Melbourne, Melbourne, VIC, Australia. Electronic address: jai.darvall@mh.org.au. 2. Department of Intensive Care, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Critical Care, The University of Melbourne, Melbourne, VIC, Australia; Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Data Analytics Research & Evaluation Centre, The University of Melbourne and Austin Hospital, Melbourne, VIC, Australia. 3. Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia. 4. Department of Critical Care, The University of Melbourne, Melbourne, VIC, Australia; Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia. 5. Medical Research Institute of New Zealand, Wellington, New Zealand. 6. Divisions of Geriatric Medicine & Neurology, and the Geriatric Medicine Research Unit, Division of Geriatric Medicine, Department of Medicine, Dalhousie University and Nova Scotia Health Authority, NS, Canada. 7. Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Department of Intensive Care, Alfred Hospital, Melbourne, VIC, Australia; Centre for Outcome and Resource Evaluation, Australian and New Zealand Intensive Care Society, Melbourne, VIC, Australia.
Abstract
BACKGROUND: Frailty is associated with poor outcomes in critical illness. However, it is unclear whether frailty screening on admission to the ICU can be conducted routinely at the population level and whether it has prognostic importance. RESEARCH QUESTION: Can population-scale frailty screening with the Clinical Frailty Scale (CFS) be implemented for critically ill adults in Australia and New Zealand (ANZ) and can it identify patients at risk of negative outcomes? STUDY DESIGN AND METHODS: We conducted a binational prospective cohort study of critically ill adult patients admitted between July 1, 2018, and June 30, 2020, in 175 ICUs in ANZ. We classified frailty with the CFS on admission to the ICU. The primary outcome was in-hospital mortality; secondary outcomes were length of stay (LOS), discharge destination, complications (delirium, pressure injury), and duration of survival. RESULTS: We included 234,568 critically ill patients; 45,245 (19%) were diagnosed as living with frailty before ICU admission. Patients with vs without frailty had higher in-hospital mortality (16% vs 5%; P < .001), delirium (10% vs 4%; P < .001), longer LOS in the ICU and hospital, and increased new chronic care discharge (3% vs 1%; P < .001), with worse outcomes associated with increasing CFS category. Of patients with very severe frailty (CFS score, 8), 39% died in hospital vs 2% of very fit patients (CFS score, 1; multivariate categorical CFS score, 8 [reference, 1]; OR, 7.83 [95% CI, 6.39-9.59]; P < .001). After adjustment for illness severity, frailty remained highly significantly predictive of mortality, including among patients younger than 50 years, with improvement in the area under the receiver operating characteristic curve of the Acute Physiology and Chronic Health Evaluation III-j score to 0.882 (95% CI, 0.879-0.885) from 0.868 (95% CI, 0.866-0.871) with the addition of frailty (P < .001). INTERPRETATION: Large-scale population screening for frailty degree in critical illness was possible and prognostically important, with greater frailty (especially CFS score of ≥ 6) associated with worse outcomes, including among younger patients.
BACKGROUND: Frailty is associated with poor outcomes in critical illness. However, it is unclear whether frailty screening on admission to the ICU can be conducted routinely at the population level and whether it has prognostic importance. RESEARCH QUESTION: Can population-scale frailty screening with the Clinical Frailty Scale (CFS) be implemented for critically ill adults in Australia and New Zealand (ANZ) and can it identify patients at risk of negative outcomes? STUDY DESIGN AND METHODS: We conducted a binational prospective cohort study of critically ill adult patients admitted between July 1, 2018, and June 30, 2020, in 175 ICUs in ANZ. We classified frailty with the CFS on admission to the ICU. The primary outcome was in-hospital mortality; secondary outcomes were length of stay (LOS), discharge destination, complications (delirium, pressure injury), and duration of survival. RESULTS: We included 234,568 critically ill patients; 45,245 (19%) were diagnosed as living with frailty before ICU admission. Patients with vs without frailty had higher in-hospital mortality (16% vs 5%; P < .001), delirium (10% vs 4%; P < .001), longer LOS in the ICU and hospital, and increased new chronic care discharge (3% vs 1%; P < .001), with worse outcomes associated with increasing CFS category. Of patients with very severe frailty (CFS score, 8), 39% died in hospital vs 2% of very fit patients (CFS score, 1; multivariate categorical CFS score, 8 [reference, 1]; OR, 7.83 [95% CI, 6.39-9.59]; P < .001). After adjustment for illness severity, frailty remained highly significantly predictive of mortality, including among patients younger than 50 years, with improvement in the area under the receiver operating characteristic curve of the Acute Physiology and Chronic Health Evaluation III-j score to 0.882 (95% CI, 0.879-0.885) from 0.868 (95% CI, 0.866-0.871) with the addition of frailty (P < .001). INTERPRETATION: Large-scale population screening for frailty degree in critical illness was possible and prognostically important, with greater frailty (especially CFS score of ≥ 6) associated with worse outcomes, including among younger patients.
Authors: Mark H Kuniholm; Elizabeth Vásquez; Allison A Appleton; Lawrence Kingsley; Frank J Palella; Matthew Budoff; Erin D Michos; Ervin Fox; Deborah Jones; Adaora A Adimora; Igho Ofotokun; Gypsyamber D'souza; Kathleen M Weber; Phyllis C Tien; Michael Plankey; Anjali Sharma; Deborah R Gustafson Journal: AIDS Date: 2022-02-01 Impact factor: 4.632
Authors: Ilena Müller; Marco Mancinetti; Anja Renner; Pierre-Olivier Bridevaux; Martin H Brutsche; Christian Clarenbach; Christian Garzoni; Alexandra Lenoir; Bruno Naccini; Sebastian Ott; Lise Piquilloud; Maura Prella; Yok-Ai Que; Paola Marina Soccal; Christophe von Garnier; Thomas K Geiser; Manuela Funke-Chambour; Sabina Guler Journal: BMJ Open Respir Res Date: 2022-04
Authors: Jai N Darvall; Rinaldo Bellomo; Michael Bailey; Paul J Young; David Pilcher Journal: Intensive Care Med Date: 2022-08-08 Impact factor: 41.787
Authors: Jai N Darvall; Rinaldo Bellomo; Michael Bailey; Paul J Young; Kenneth Rockwood; David Pilcher Journal: Intensive Care Med Date: 2022-02-04 Impact factor: 41.787