title: Predicting intensive care and hospital outcome with the Dalhousie Clinical Frailty Scale: a pilot assessment.
abstract: Frailty may help to predict intensive care unit (ICU) patient outcome. The Dalhousie Clinical Frailty Scale (DCFS) is validated to assess frailty in ambulatory settings but has not been investigated in Australian ICUs. We conducted a prospective three-month study of patients admitted to a tertiary level ICU. Within 24 hours of ICU admission, the next of kin or nurse in charge assigned a DCFS score to the patient. Data were obtained to assess the association between frailty and patient outcome. The DCFS score was completed in 205 of 348 (59%) of eligible patient admissions. The mean DCFS score was 3.2 (±1.6). Overall frailty (DCFS>4) occurred in 28 of 205 patients (13%, confidence interval 9% to 17%), 13 of 93 (15%, confidence interval 10% to 25%) in patients aged >65 years and 5 of 11 (45%, confidence interval 21% to 71%) in those>85 years. Patients with chronic liver disease (P<0.001) and end-stage renal failure (P=0.009) were more likely to be frail. The DCFS score was not significantly associated with ICU or hospital mortality: odds ratio 0.98 (95% confidence interval 0.6 to 1.6) and odds ratio 1.07 (95% confidence interval 0.8 to 1.4), respectively. However, after adjustment for illness severity and requirement for palliative care, the DCFS score was significantly associated with increased (log) hospital length-of-stay (P=0.04) and age (P=0.001). Approximately 1 in 10 ICU patients were frail and this frequency increased with age. The DCFS was associated with patient age and comorbidities and potentially predicts increased hospital length-of-stay but not other outcomes. Strategies to improve compliance with DCFS completion are needed.
authors.affiliation: Department of Intensive Care Medicine, Austin Hospital, Heidelberg, Victoria.
authors.forename: C
authors.initials: C
authors.lastname: Fisher
authors.affiliation: Austin Health, Melbourne, Victoria.
authors.forename: D K
authors.initials: DK
authors.lastname: Karalapillai
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria.
authors.forename: M
authors.initials: M
authors.lastname: Bailey
authors.affiliation: School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria.
authors.forename: N G
authors.initials: NG
authors.lastname: Glassford
authors.affiliation: Austin Health and School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria.
authors.forename: R
authors.initials: R
authors.lastname: Bellomo
authors.affiliation: Critical Care Outreach, Austin Health and Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria.
authors.forename: D
authors.initials: D
authors.lastname: Jones
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001315:Australia; D003422:Critical Care / Q000706:statistics & numerical data*; D016638:Critical Illness; D005240:Feasibility Studies; D005260:Female; D006305:Health Status Indicators; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D010043:Outcome and Process Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data*; D010865:Pilot Projects; D011446:Prospective Studies; D015203:Reproducibility of Results; D012720:Severity of Illness Index
keywords: critical illness; frailty