Roman Romero-Ortuno1. 1. Department of Medical Gerontology, Trinity College Dublin, Old Stone Building, Trinity Centre for Health Sciences, St James's Hospital, James's Street, Dublin 8, Ireland, .
Abstract
PURPOSE: the Frailty Index (FI) is a popular operationalization of frailty. FI cut-off points have been proposed to define, regardless of age, frailty categories with increasing risk. Here, an alternative method is described that takes age into account. SUBJECTS AND METHODS: 29,905 participants aged ≥ 50 from the first wave of the Survey of Health, Ageing and Retirement in Europe. The mean follow-up for mortality was 2.4 years. Curve estimation procedures were carried out between age and a FI, and 50% Confidence Intervals (CI) for the regression mean were derived. As opposed to the usual method (FI ≤ 0.08: non-frail; FI ≥ 0.25: frail; rest: pre-frail), the alternative method defines as 'fit for their age' those with a FI below the lower 50% CI; 'frail for their age' those with a FI above the upper 50% CI; the rest as 'average for their age'. Using both methods, the prevalence of the frailty categories and their associated mortality rates were compared for each age group. RESULTS: The best fit between age the FI was by cubic regression (R2 = 0.174, P < 0.001). Among those in their 50s, 5% were frail by the usual method (mortality: 5%) and 14% by the alternative (mortality: 2%). Among those in their 90s, 64% were frail by the usual method (mortality: 43%) and 41% by the alternative (mortality: 48%). CONCLUSION: the alternative method may be more sensitive in younger ages and more specific in older ages. This may have implications for population screening.
PURPOSE: the Frailty Index (FI) is a popular operationalization of frailty. FI cut-off points have been proposed to define, regardless of age, frailty categories with increasing risk. Here, an alternative method is described that takes age into account. SUBJECTS AND METHODS: 29,905 participants aged ≥ 50 from the first wave of the Survey of Health, Ageing and Retirement in Europe. The mean follow-up for mortality was 2.4 years. Curve estimation procedures were carried out between age and a FI, and 50% Confidence Intervals (CI) for the regression mean were derived. As opposed to the usual method (FI ≤ 0.08: non-frail; FI ≥ 0.25: frail; rest: pre-frail), the alternative method defines as 'fit for their age' those with a FI below the lower 50% CI; 'frail for their age' those with a FI above the upper 50% CI; the rest as 'average for their age'. Using both methods, the prevalence of the frailty categories and their associated mortality rates were compared for each age group. RESULTS: The best fit between age the FI was by cubic regression (R2 = 0.174, P < 0.001). Among those in their 50s, 5% were frail by the usual method (mortality: 5%) and 14% by the alternative (mortality: 2%). Among those in their 90s, 64% were frail by the usual method (mortality: 43%) and 41% by the alternative (mortality: 48%). CONCLUSION: the alternative method may be more sensitive in younger ages and more specific in older ages. This may have implications for population screening.
Authors: Peter Lloyd-Sherlock; Martin McKee; Shah Ebrahim; Mark Gorman; Sally Greengross; Martin Prince; Rachel Pruchno; Gloria Gutman; Tom Kirkwood; Desmond O'Neill; Luigi Ferrucci; Stephen B Kritchevsky; Bruno Vellas Journal: Lancet Date: 2012-04-04 Impact factor: 79.321
Authors: L P Fried; C M Tangen; J Walston; A B Newman; C Hirsch; J Gottdiener; T Seeman; R Tracy; W J Kop; G Burke; M A McBurnie Journal: J Gerontol A Biol Sci Med Sci Date: 2001-03 Impact factor: 6.053
Authors: Gabor Abellan van Kan; Yves Rolland; Mathieu Houles; Sophie Gillette-Guyonnet; Maria Soto; Bruno Vellas Journal: Clin Geriatr Med Date: 2010-05 Impact factor: 3.076
Authors: Olga Theou; Thomas D Brothers; Michael R Rockwood; David Haardt; Arnold Mitnitski; Kenneth Rockwood Journal: Age Ageing Date: 2013-02-25 Impact factor: 10.668
Authors: José Juan García-González; Carmen García-Peña; Francisco Franco-Marina; Luis Miguel Gutiérrez-Robledo Journal: BMC Geriatr Date: 2009-11-03 Impact factor: 3.921
Authors: Mairead M Bartley; Yonas E Geda; Teresa J H Christianson; V Shane Pankratz; Rosebud O Roberts; Ronald C Petersen Journal: J Am Geriatr Soc Date: 2016-01 Impact factor: 5.562
Authors: Alice E Kane; Aniko Huizer-Pajkos; John Mach; Sarah J Mitchell; Rafael de Cabo; David G Le Couteur; Susan E Howlett; Sarah N Hilmer Journal: J Gerontol A Biol Sci Med Sci Date: 2017-07-01 Impact factor: 6.053
Authors: Quoc Dinh Nguyen; Erica M Moodie; Mark R Keezer; Christina Wolfson Journal: J Gerontol A Biol Sci Med Sci Date: 2021-10-13 Impact factor: 6.591
Authors: Anna Torné; Emma Puigoriol; Edurne Zabaleta-Del-Olmo; Juan-José Zamora-Sánchez; Sebastià Santaeugènia; Jordi Amblàs-Novellas Journal: Int J Environ Res Public Health Date: 2021-05-13 Impact factor: 3.390