Harleah G Buck1, Barbara Riegel. 1. School of Nursing, University of Pennsylvania, Claire M. Fagin Hall, Philadelphia, USA. buckh@nursing.upenn.edu
Abstract
BACKGROUND/AIMS: Most heart failure (HF) hospital discharges involve people >65years, many frail. The purpose of this study was to determine if frailty explains variability in health related quality of life (HRQOL) in older adults with HF over and above known correlates. METHODS: A frailty index score was developed by weighting age, number of comorbid conditions, and symptom severity. A multivariate hierarchical regression analysis of known predictors of HRQOLgender, income, ethnicity, health perception, NYHA class - were entered first and then the frailty index was entered and regressed on HRQOL in 2 unique samples. RESULTS: When known predictors were tested on a sample they explained 11% (p 0.14) of the variance in HRQOL; when the frailty index score was added 24% (p 0.001) was explained. When the index was validated in a second sample, known predictors explained 15% (p 0.04) of the variance; with the frailty index score 40% (p 0.000) was explained. CONCLUSION: Frailty explains significant amounts of variance in HRQOL in HF. Treating comorbid conditions and controlling symptoms may improve HRQOL in HF patients. These findings support the need for further research into the impact of frailty on HRQOL in HF patients.
BACKGROUND/AIMS: Most heart failure (HF) hospital discharges involve people >65years, many frail. The purpose of this study was to determine if frailty explains variability in health related quality of life (HRQOL) in older adults with HF over and above known correlates. METHODS: A frailty index score was developed by weighting age, number of comorbid conditions, and symptom severity. A multivariate hierarchical regression analysis of known predictors of HRQOLgender, income, ethnicity, health perception, NYHA class - were entered first and then the frailty index was entered and regressed on HRQOL in 2 unique samples. RESULTS: When known predictors were tested on a sample they explained 11% (p 0.14) of the variance in HRQOL; when the frailty index score was added 24% (p 0.001) was explained. When the index was validated in a second sample, known predictors explained 15% (p 0.04) of the variance; with the frailty index score 40% (p 0.000) was explained. CONCLUSION: Frailty explains significant amounts of variance in HRQOL in HF. Treating comorbid conditions and controlling symptoms may improve HRQOL in HF patients. These findings support the need for further research into the impact of frailty on HRQOL in HF patients.
Authors: Quin E Denfeld; Kerri Winters-Stone; James O Mudd; Shirin O Hiatt; Christopher S Lee Journal: J Cardiovasc Nurs Date: 2018 Jan/Feb Impact factor: 2.083
Authors: Marie Bakitas; Meredith Macmartin; Kenneth Trzepkowski; Alina Robert; Lisa Jackson; Jeremiah R Brown; James N Dionne-Odom; Alan Kono Journal: J Card Fail Date: 2013-03 Impact factor: 5.712