Christine E Haugen1, Alden Gross2, Nadia M Chu1,2, Silas P Norman3, Daniel C Brennan4, Qian-Li Xue2,5,6, Jeremy Walston5, Dorry L Segev1,2, Mara McAdams-DeMarco1,2. 1. Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland. 2. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 3. Department of Medicine, Division of Nephrology, University of Michigan, Ann Arbor. 4. Department of Medicine, Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland. 5. Department of Medicine, Division of Geriatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland. 6. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Abstract
BACKGROUND: Physical frailty phenotype is characterized by decreased physiologic reserve to stressors and associated with poor outcomes, such as delirium and mortality, that may result from post-kidney transplant (KT) inflammation. Despite a hypothesized underlying pro-inflammatory state, conventional measures of frailty typically do not incorporate inflammatory biomarkers directly. Among KT candidates and recipients, we evaluated the inclusion of inflammatory biomarkers with traditional physical frailty phenotype components. METHODS: Among 1154 KT candidates and recipients with measures of physical frailty phenotype and inflammation (interleukin 6 [IL6], tumor necrosis factor alpha [TNFα], C-reactive protein [CRP]) at 2 transplant centers (2009-2017), we evaluated construct validity of inflammatory-frailty using latent class analysis. Inflammatory-frailty measures combined 5 physical frailty phenotype components plus the addition of an individual inflammatory biomarkers, separately (highest tertiles) as a sixth component. We then used Kaplan-Meier methods and adjusted Cox proportional hazards to assess post-KT mortality risk by inflammatory-frailty (n = 378); Harrell's C-statistics assessed risk prediction (discrimination). RESULTS: Based on fit criteria, a 2-class solution (frail vs nonfrail) for inflammatory-frailty was the best-fitting model. Five-year survival (frail vs nonfrail) was: 81% versus 93% (IL6-frailty), 87% versus 89% (CRP-frailty), and 83% versus 91% (TNFα-frailty). Mortality was 2.07-fold higher for IL6-frail recipients (95% CI: 1.03-4.19, p = .04); there were no associations between the mortality and the other inflammatory-frailty indices (TNFα-frail: 1.88, 95% CI: 0.95-3.74, p = .07; CRP-frail: 1.02, 95% CI: 0.52-2.03, p = .95). However, none of the frailty-inflammatory indices (all C-statistics = 0.71) improved post-KT mortality risk prediction over the physical frailty phenotype (C-statistics = 0.70). CONCLUSIONS: Measurement of IL6-frailty at transplantation can inform which patients should be targeted for pre-KT interventions. However, the traditional physical frailty phenotype is sufficient for post-KT mortality risk prediction.
BACKGROUND: Physical frailty phenotype is characterized by decreased physiologic reserve to stressors and associated with poor outcomes, such as delirium and mortality, that may result from post-kidney transplant (KT) inflammation. Despite a hypothesized underlying pro-inflammatory state, conventional measures of frailty typically do not incorporate inflammatory biomarkers directly. Among KT candidates and recipients, we evaluated the inclusion of inflammatory biomarkers with traditional physical frailty phenotype components. METHODS: Among 1154 KT candidates and recipients with measures of physical frailty phenotype and inflammation (interleukin 6 [IL6], tumor necrosis factor alpha [TNFα], C-reactive protein [CRP]) at 2 transplant centers (2009-2017), we evaluated construct validity of inflammatory-frailty using latent class analysis. Inflammatory-frailty measures combined 5 physical frailty phenotype components plus the addition of an individual inflammatory biomarkers, separately (highest tertiles) as a sixth component. We then used Kaplan-Meier methods and adjusted Cox proportional hazards to assess post-KT mortality risk by inflammatory-frailty (n = 378); Harrell's C-statistics assessed risk prediction (discrimination). RESULTS: Based on fit criteria, a 2-class solution (frail vs nonfrail) for inflammatory-frailty was the best-fitting model. Five-year survival (frail vs nonfrail) was: 81% versus 93% (IL6-frailty), 87% versus 89% (CRP-frailty), and 83% versus 91% (TNFα-frailty). Mortality was 2.07-fold higher for IL6-frail recipients (95% CI: 1.03-4.19, p = .04); there were no associations between the mortality and the other inflammatory-frailty indices (TNFα-frail: 1.88, 95% CI: 0.95-3.74, p = .07; CRP-frail: 1.02, 95% CI: 0.52-2.03, p = .95). However, none of the frailty-inflammatory indices (all C-statistics = 0.71) improved post-KT mortality risk prediction over the physical frailty phenotype (C-statistics = 0.70). CONCLUSIONS: Measurement of IL6-frailty at transplantation can inform which patients should be targeted for pre-KT interventions. However, the traditional physical frailty phenotype is sufficient for post-KT mortality risk prediction.
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