Sara Belga1, Sumit R Majumdar1,2, Sharry Kahlon1, Jenelle Pederson1, Darren Lau1, Raj S Padwal1,2, Mary Forhan3, Jeffrey A Bakal2, Finlay A McAlister1,2. 1. Division of General Internal Medicine, University of Alberta, Alberta, Edmonton, Alberta, Canada. 2. Patient Health Outcomes Research and Clinical Effectiveness Unit, University of Alberta, Alberta, Edmonton, Canada. 3. Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Alberta, Edmonton, Canada.
Abstract
BACKGROUND: Multiple tools are used to identify frailty. OBJECTIVE: To compare the global Clinical Frailty Scale (CFS) with more objective phenotypic tools (modified Fried score and the Timed Up and Go Test [TUGT]). DESIGN: Prospective cohort study. SETTING: General medical wards in Edmonton, Canada. PARTICIPANTS: Adults being discharged back to the community. MEASUREMENTS: All frailty assessments were done within 24 hours of discharge. Patients were classified as frail if they scored ≥5 on the CFS and/or ≥3 on the modified Fried score, and/or had reduced mobility (>20 seconds on the TUGT). The main outcome was readmission or death within 30 days. RESULTS: Of 495 patients, 211 (43%) were frail according to at least 1 assessment, 46 (9%) met all 3 frailty definitions, and 17% died or were readmitted to the hospital within 30 days. Although patients classified as frail on the CFS exhibited significantly higher 30-day readmission/death rates (23% vs 14% for not frail, P = 0.005; 28% vs. 12% in the elderly, P < 0.001), even after adjusting for age and sex (adjusted odds ratio [aOR]: 2.02, 95% confidence interval [CI]: 1.19-3.41 for all adults; aOR: 3.20, 95% CI: 1.55-6.60 for the elderly), patients meeting either of the phenotypic definitions for frailty but not the CFS definition were not at higher risk of 30-day readmission/death (aOR: 0.87, 95% CI: 0.34-2.19 for all adults and aOR: 1.41, 95% CI: 0.72-2.78 for the elderly). CONCLUSIONS: Frailty has a significant impact on postdischarge outcomes, and the CFS is the most useful of the frequently used frailty tools for predicting poor outcomes after discharge. Journal of Hospital Medicine 2016;11:556-562.
BACKGROUND: Multiple tools are used to identify frailty. OBJECTIVE: To compare the global Clinical Frailty Scale (CFS) with more objective phenotypic tools (modified Fried score and the Timed Up and Go Test [TUGT]). DESIGN: Prospective cohort study. SETTING: General medical wards in Edmonton, Canada. PARTICIPANTS: Adults being discharged back to the community. MEASUREMENTS: All frailty assessments were done within 24 hours of discharge. Patients were classified as frail if they scored ≥5 on the CFS and/or ≥3 on the modified Fried score, and/or had reduced mobility (>20 seconds on the TUGT). The main outcome was readmission or death within 30 days. RESULTS: Of 495 patients, 211 (43%) were frail according to at least 1 assessment, 46 (9%) met all 3 frailty definitions, and 17% died or were readmitted to the hospital within 30 days. Although patients classified as frail on the CFS exhibited significantly higher 30-day readmission/death rates (23% vs 14% for not frail, P = 0.005; 28% vs. 12% in the elderly, P < 0.001), even after adjusting for age and sex (adjusted odds ratio [aOR]: 2.02, 95% confidence interval [CI]: 1.19-3.41 for all adults; aOR: 3.20, 95% CI: 1.55-6.60 for the elderly), patients meeting either of the phenotypic definitions for frailty but not the CFS definition were not at higher risk of 30-day readmission/death (aOR: 0.87, 95% CI: 0.34-2.19 for all adults and aOR: 1.41, 95% CI: 0.72-2.78 for the elderly). CONCLUSIONS: Frailty has a significant impact on postdischarge outcomes, and the CFS is the most useful of the frequently used frailty tools for predicting poor outcomes after discharge. Journal of Hospital Medicine 2016;11:556-562.
Authors: Maria Queralt Salas; Eshetu G Atenafu; Ora Bascom; Leeann Wilson; Wilson Lam; Arjun Datt Law; Ivan Pasic; Dennis Dong Hwan Kim; Fotios V Michelis; Zeyad Al-Shaibani; Armin Gerbitz; Auro Viswabandya; Jeffrey Howard Lipton; Jonas Mattsson; Shabbir M H Alibhai; Rajat Kumar Journal: Bone Marrow Transplant Date: 2020-06-30 Impact factor: 5.483
Authors: Yibo Li; Jenelle L Pederson; Thomas A Churchill; Adrian S Wagg; Jayna M Holroyd-Leduc; Kannayiram Alagiakrishnan; Raj S Padwal; Rachel G Khadaroo Journal: CMAJ Date: 2018-02-20 Impact factor: 8.262
Authors: Shivani Shah; David S Goldberg; David E Kaplan; Vinay Sundaram; Tamar H Taddei; Nadim Mahmud Journal: Liver Transpl Date: 2020-10-28 Impact factor: 5.799
Authors: Cheng-Fu Lin; Yu-Hui Huang; Li-Ying Ju; Shuo-Chun Weng; Yu-Shan Lee; Yin-Yi Chou; Chu-Sheng Lin; Shih-Yi Lin Journal: Int J Environ Res Public Health Date: 2020-07-24 Impact factor: 3.390