Imaina S Widagdo1, Nicole Pratt1, Mary Russell2, Elizabeth E Roughead1. 1. Quality Use of Medicines and Pharmacy Research Centre, Sansom Institute, School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA, Australia. 2. Occupational Therapy Board, Australian Health Practitioner Regulation Agency, Melbourne, VIC, Australia.
Abstract
BACKGROUND: There are several different frailty measures available for identifying the frail elderly. However, their predictive performance in an Australian population has not been examined. OBJECTIVE: To examine the predictive performance of four internationally validated frailty measures in an older Australian population. METHODS: A retrospective study in the Australian Longitudinal Study of Ageing (ALSA) with 2,087 participants. Frailty was measured at baseline using frailty phenotype (FP), simplified frailty phenotype (SFP), frailty index (FI) and prognostic frailty score (PFS). Odds ratios (OR) were calculated to measure the association between frailty and outcomes at Wave 3 including mortality, hospitalisation, nursing home admission, fall and a combination of all outcomes. Predictive performance was measured by assessing sensitivity, specificity, positive and negative predictive values (PPV and NPV) and likelihood ratio (LR). Area under the curve (AUC) of dichotomised and the multilevel or continuous model of the measures was examined. RESULTS: Prevalence of frailty varied from 2% up to 49% between the measures. Frailty was significantly associated with an increased risk of any outcome, OR (95% confidence interval) for FP: 1.9 (1.4-2.8), SFP: 3.6 (1.5-8.8), FI: 3.4 (2.7-4.3) and PFS: 2.3 (1.8-2.8). PFS had high sensitivity across all outcomes (sensitivity: 55.2-77.1%). The PPV for any outcome was highest for SFP and FI (70.8 and 69.7%, respectively). Only FI had acceptable accuracy in predicting outcomes, AUC: 0.59-0.70. CONCLUSIONS: Being identified as frail by any of the four measures was associated with an increased risk of outcomes; however, their predictive accuracy varied.
BACKGROUND: There are several different frailty measures available for identifying the frail elderly. However, their predictive performance in an Australian population has not been examined. OBJECTIVE: To examine the predictive performance of four internationally validated frailty measures in an older Australian population. METHODS: A retrospective study in the Australian Longitudinal Study of Ageing (ALSA) with 2,087 participants. Frailty was measured at baseline using frailty phenotype (FP), simplified frailty phenotype (SFP), frailty index (FI) and prognostic frailty score (PFS). Odds ratios (OR) were calculated to measure the association between frailty and outcomes at Wave 3 including mortality, hospitalisation, nursing home admission, fall and a combination of all outcomes. Predictive performance was measured by assessing sensitivity, specificity, positive and negative predictive values (PPV and NPV) and likelihood ratio (LR). Area under the curve (AUC) of dichotomised and the multilevel or continuous model of the measures was examined. RESULTS: Prevalence of frailty varied from 2% up to 49% between the measures. Frailty was significantly associated with an increased risk of any outcome, OR (95% confidence interval) for FP: 1.9 (1.4-2.8), SFP: 3.6 (1.5-8.8), FI: 3.4 (2.7-4.3) and PFS: 2.3 (1.8-2.8). PFS had high sensitivity across all outcomes (sensitivity: 55.2-77.1%). The PPV for any outcome was highest for SFP and FI (70.8 and 69.7%, respectively). Only FI had acceptable accuracy in predicting outcomes, AUC: 0.59-0.70. CONCLUSIONS: Being identified as frail by any of the four measures was associated with an increased risk of outcomes; however, their predictive accuracy varied.
Authors: Shelley A Sternberg; Andrea Wershof Schwartz; Sathya Karunananthan; Howard Bergman; A Mark Clarfield Journal: J Am Geriatr Soc Date: 2011-09-21 Impact factor: 5.562
Authors: Arnold Mitnitski; Xiaowei Song; Ingmar Skoog; G A Broe; Jafna L Cox; Eva Grunfeld; Kenneth Rockwood Journal: J Am Geriatr Soc Date: 2005-12 Impact factor: 5.562
Authors: Kristine E Ensrud; Susan K Ewing; Brent C Taylor; Howard A Fink; Peggy M Cawthon; Katie L Stone; Teresa A Hillier; Jane A Cauley; Marc C Hochberg; Nicolas Rodondi; J Kathleen Tracy; Steven R Cummings Journal: Arch Intern Med Date: 2008-02-25
Authors: Heather E Whitson; Harvey J Cohen; Kenneth E Schmader; Miriam C Morey; George Kuchel; Cathleen S Colon-Emeric Journal: J Am Geriatr Soc Date: 2018-03-25 Impact factor: 5.562
Authors: Allison Magnuson; Lianlian Lei; Nikesha Gilmore; Amber S Kleckner; Feng V Lin; Robert Ferguson; Arti Hurria; Marsha N Wittink; Benjamin T Esparaz; Jeffrey K Giguere; Jamal Misleh; Javier Bautista; Supriya G Mohile; Michelle C Janelsins Journal: J Am Geriatr Soc Date: 2019-05 Impact factor: 5.562
Authors: Yan Cheng; Yijun Shao; Charlene R Weir; Rashmee U Shah; Bruce E Bray; Jennifer H Garvin; Qing Zeng-Treitler Journal: Stud Health Technol Inform Date: 2017
Authors: Renly Lim; Lisa M Kalisch Ellett; Imaina S Widagdo; Nicole L Pratt; Elizabeth Ellen Roughead Journal: BMJ Open Date: 2019-09-04 Impact factor: 2.692
Authors: Renly Lim; Luke Bereznicki; Megan Corlis; Lisa M Kalisch Ellett; Ai Choo Kang; Tracy Merlin; Gaynor Parfitt; Nicole L Pratt; Debra Rowett; Stacey Torode; Joseph Whitehouse; Andre Q Andrade; Rebecca Bilton; Justin Cousins; Lan Kelly; Camille Schubert; Mackenzie Williams; Elizabeth Ellen Roughead Journal: BMJ Open Date: 2020-04-22 Impact factor: 2.692
Authors: Daniel Stow; Fiona E Matthews; Stephen Barclay; Steve Iliffe; Andrew Clegg; Sarah De Biase; Louise Robinson; Barbara Hanratty Journal: Age Ageing Date: 2018-07-01 Impact factor: 10.668
Authors: Andria Mousa; George M Savva; Arnold Mitnitski; Kenneth Rockwood; Carol Jagger; Carol Brayne; Fiona E Matthews Journal: Age Ageing Date: 2018-09-01 Impact factor: 10.668