The distribution of the frailty index among different arthritis patients and the association between the frailty index and mortality in rheumatoid arthritis patients among U.S. adults (results from NHANES 1999-2018)

美国成年人类风湿性关节炎患者中不同关节炎患者的衰弱指数分布及衰弱指数与死亡率之间的关联(1999-2018 年 NHANES 数据)

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Abstract

BACKGROUND: We conducted to examine the prevalence of frailty among different arthritis populations and to elucidate the associations between frailty and all-cause and cause-specific mortality among rheumatoid arthritis (RA) in the American adults. METHOD: National Health and Nutrition Examination Survey (NHANES) database spanning 1999 to 2018 was collected, supplemented with pertinent mortality records from the U.S. National Death Index (NDI) up to December 31, 2019. The Frailty Index defined by Searle was adopted. Different types of arthritis include: RA, Osteoarthritis (OA), Psoriatic Arthritis (PsA), and others. Statistical methods mainly included maximum selected rank statistics, univariate and multivariate COX regression models, and Restricted Cubic Spline (RCS). RESULTS: The frailty indices of different arthritis populations were 0.24, 0.21, 0.21, and 0.20, respectively. The cut-off value for the frailty index was 0.3. Compared to those with a lower frailty index, participants with a higher frailty index exhibited higher risks of all-cause mortality (HR = 2.11, 95% CI 1.70-2.62), heart disease mortality (HR = 2.38, 95% CI 1.53-3.70), lower respiratory disease mortality (HR = 3.79, 95% CI 1.62-8.88), and influenza and pneumonia mortality (HR = 6.08, 95% CI 2.12-17.42). Frailty-associated all-cause mortality risks increased with age among different age groups. CONCLUSION: Frailty was particularly pronounced among RA patients. Relatively high frailty index could predict higher all-cause and cause-specific mortality among RA adults in America, and the all-cause mortality related to frailty increased with age. Frailty index could be utilized as a prognostic marker in RA management. Timely interventions are paramount for optimizing patient outcomes.

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