Eralda Hegendörfer1, Bert Vaes2, Gijs Van Pottelbergh2, Catharina Matheï2, Jan Verbakel3, Jean-Marie Degryse4. 1. Department of Public Health and Primary Care, Katholieke Universiteit Leuven, Belgium; Institute of Health and Society, Université Catholique de Louvain, Belgium. Electronic address: eralda.turkeshi@kuleuven.be. 2. Department of Public Health and Primary Care, Katholieke Universiteit Leuven, Belgium. 3. Department of Public Health and Primary Care, Katholieke Universiteit Leuven, Belgium; Nuffield Department of Primary Care Health Sciences, University of Oxford, United Kingdom. 4. Department of Public Health and Primary Care, Katholieke Universiteit Leuven, Belgium; Institute of Health and Society, Université Catholique de Louvain, Belgium.
Abstract
OBJECTIVES: To compare the predictive performance of 3 frailty identification tools for mortality, hospitalization, and functional decline in adults aged ≥80 years using risk reclassification statistics and decision curve analysis. DESIGN: Population-based, prospective cohort. SETTING: BELFRAIL study, Belgium. PARTICIPANTS: 560 community-dwelling adults aged ≥80 years. MEASUREMENTS: Frailty by Cardiovascular Health Study (CHS) phenotype, Longitudinal Aging Study Amsterdam (LASA) markers, and Groeningen Frailty Indicator (GFI); mortality until 5.1 ± 0.25 years from baseline and hospitalization until 3.0 ± 0.25 years; and functional status assessed by activities of daily living at baseline and after 1.7 ± 0.21 years. RESULTS: Frailty prevalence was 7.3% by CHS phenotype, 21.6% by LASA markers, and 22% by GFI. Participants determined to be frail by each tool had a significantly higher risk for all-cause mortality and first hospitalization. For functional decline, only frail by GFI had a higher adjusted odds ratio. Harrell 's C-statistic for mortality and hospitalization and area under receiver operating characteristic curve for functional decline were similar for all tools and <0.70. Reclassification statistics showed improvement only by LASA markers for hospitalization and mortality. In decision curve analysis, all tools had higher net benefit than the 2 default strategies of "treat all" and "treat none" for mortality risk ≥20%, hospitalization risk ≥35%, and functional decline probability ≥10%, but their curves overlapped across all relevant risk thresholds for these outcomes. CONCLUSIONS AND IMPLICATIONS: In a cohort of adults aged ≥80 years, 3 frailty tools based on different conceptualizations and assessment sources had comparable but unsatisfactory discrimination for predicting mortality, hospitalization, and functional decline. All showed clinical utility for predicting these outcomes over relevant risk thresholds, but none was significantly superior. Future research on frailty tools should include a focus on the specific group of adults aged ≥80 years, and the predictive accuracy for adverse outcomes of different tools needs a comprehensive assessment that includes decision curve analysis.
OBJECTIVES: To compare the predictive performance of 3 frailty identification tools for mortality, hospitalization, and functional decline in adults aged ≥80 years using risk reclassification statistics and decision curve analysis. DESIGN: Population-based, prospective cohort. SETTING: BELFRAIL study, Belgium. PARTICIPANTS: 560 community-dwelling adults aged ≥80 years. MEASUREMENTS: Frailty by Cardiovascular Health Study (CHS) phenotype, Longitudinal Aging Study Amsterdam (LASA) markers, and Groeningen Frailty Indicator (GFI); mortality until 5.1 ± 0.25 years from baseline and hospitalization until 3.0 ± 0.25 years; and functional status assessed by activities of daily living at baseline and after 1.7 ± 0.21 years. RESULTS: Frailty prevalence was 7.3% by CHS phenotype, 21.6% by LASA markers, and 22% by GFI. Participants determined to be frail by each tool had a significantly higher risk for all-cause mortality and first hospitalization. For functional decline, only frail by GFI had a higher adjusted odds ratio. Harrell 's C-statistic for mortality and hospitalization and area under receiver operating characteristic curve for functional decline were similar for all tools and <0.70. Reclassification statistics showed improvement only by LASA markers for hospitalization and mortality. In decision curve analysis, all tools had higher net benefit than the 2 default strategies of "treat all" and "treat none" for mortality risk ≥20%, hospitalization risk ≥35%, and functional decline probability ≥10%, but their curves overlapped across all relevant risk thresholds for these outcomes. CONCLUSIONS AND IMPLICATIONS: In a cohort of adults aged ≥80 years, 3 frailty tools based on different conceptualizations and assessment sources had comparable but unsatisfactory discrimination for predicting mortality, hospitalization, and functional decline. All showed clinical utility for predicting these outcomes over relevant risk thresholds, but none was significantly superior. Future research on frailty tools should include a focus on the specific group of adults aged ≥80 years, and the predictive accuracy for adverse outcomes of different tools needs a comprehensive assessment that includes decision curve analysis.
Authors: Michael C J Van der Elst; Birgitte Schoenmakers; Linda P M Op Het Veld; Ellen E De Roeck; Anne Van der Vorst; Gertrudis I J M Kempen; Nico De Witte; Jan De Lepeleire; Jos M G A Schols Journal: BMC Geriatr Date: 2019-12-10 Impact factor: 3.921