PURPOSE: Age-associated cumulative decline across physiologic systems results in a diminished resistance to stressors, including cancer and its treatment, creating a vulnerable state known as frailty. Frailty is associated with increased risk of adverse outcomes in patients with cancer. Identification of frailty in administrative data can allow for assessment of prognosis and facilitate control for confounding variables. The purpose of this study was to assess frailty from claims-based data using the accumulation of deficits approach in veterans with multiple myeloma (MM). METHODS: From the Veterans Administration Central Cancer Registry, we identified patients who were diagnosed with MM between 1999 and 2014. Using the accumulation of deficits approach, we calculated a Frailty Index (FI) using 31 health-associated deficits and categorized scores into five groups: nonfrail (FI, 0 to 0.1), prefrail (FI, 0.11 to 0.20), mild frailty (FI, 0.21 to 0.30), moderate frailty (FI, 0.31 to 0.40), and severe frailty (FI, > 0.4). We used Cox proportional hazards regression analysis to assess association between FI score and mortality while adjusting for potential confounders. RESULTS: We calculated an FI for 3,807 veterans age 65 years or older. Among the cohort, 28.7% were classified as nonfrail, 41.3% prefrail, 21.6% mildly frail, 6.6% moderately frail, and 1.7% severely frail. Frailty was strongly associated with mortality independent of age, race, MM treatment, body mass index, or statin use. Higher FI score was associated with higher mortality with hazard ratios of 1.33 (95% CI, 1.21 to 1.47), 1.97 (95% CI, 1.70 to 2.20), 2.86 (95% CI, 2.45 to 3.34), and 3.22 (95% CI, 2.46 to 4.22) for prefrail, mildly frail, moderately frail, and severely frail, respectively. CONCLUSION: Frailty status is a significant predictor of mortality in older veterans with MM. Assessment of frailty status using the readily available electronic medical records data in administrative data allows for assessment of prognosis.
PURPOSE: Age-associated cumulative decline across physiologic systems results in a diminished resistance to stressors, including cancer and its treatment, creating a vulnerable state known as frailty. Frailty is associated with increased risk of adverse outcomes in patients with cancer. Identification of frailty in administrative data can allow for assessment of prognosis and facilitate control for confounding variables. The purpose of this study was to assess frailty from claims-based data using the accumulation of deficits approach in veterans with multiple myeloma (MM). METHODS: From the Veterans Administration Central Cancer Registry, we identified patients who were diagnosed with MM between 1999 and 2014. Using the accumulation of deficits approach, we calculated a Frailty Index (FI) using 31 health-associated deficits and categorized scores into five groups: nonfrail (FI, 0 to 0.1), prefrail (FI, 0.11 to 0.20), mild frailty (FI, 0.21 to 0.30), moderate frailty (FI, 0.31 to 0.40), and severe frailty (FI, > 0.4). We used Cox proportional hazards regression analysis to assess association between FI score and mortality while adjusting for potential confounders. RESULTS: We calculated an FI for 3,807 veterans age 65 years or older. Among the cohort, 28.7% were classified as nonfrail, 41.3% prefrail, 21.6% mildly frail, 6.6% moderately frail, and 1.7% severely frail. Frailty was strongly associated with mortality independent of age, race, MM treatment, body mass index, or statin use. Higher FI score was associated with higher mortality with hazard ratios of 1.33 (95% CI, 1.21 to 1.47), 1.97 (95% CI, 1.70 to 2.20), 2.86 (95% CI, 2.45 to 3.34), and 3.22 (95% CI, 2.46 to 4.22) for prefrail, mildly frail, moderately frail, and severely frail, respectively. CONCLUSION: Frailty status is a significant predictor of mortality in older veterans with MM. Assessment of frailty status using the readily available electronic medical records data in administrative data allows for assessment of prognosis.
Authors: David Cheng; Clark DuMontier; Cenk Yildirim; Brian Charest; Chelsea E Hawley; Min Zhuo; Julie M Paik; Enzo Yaksic; J Michael Gaziano; Nhan Do; Mary Brophy; Kelly Cho; Dae H Kim; Jane A Driver; Nathanael R Fillmore; Ariela R Orkaby Journal: J Gerontol A Biol Sci Med Sci Date: 2021-06-14 Impact factor: 6.053
Authors: David Cheng; Clark Dumontier; Ayesha R Sheikh; Jennifer La; Mary T Brophy; Nhan V Do; Jane A Driver; David P Tuck; Nathanael R Fillmore Journal: Cancer Med Date: 2022-03-26 Impact factor: 4.711