Limin Wang1, Han Lu1, Hongbo Chen2, Shida Jin1, Mengqi Wang1, Shaomei Shang3. 1. School of Nursing, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China. 2. School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China. 3. School of Nursing, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China. shangshaomei@126.com.
Abstract
OBJECTIVES: We aimed to develop a model for predicting the 4-year risk of knee osteoarthritis (KOA) based on survey data obtained via a random, nationwide sample of Chinese individuals. METHODS: Data was analyzed from 8193 middle-aged and older adults included in the China Health and Retirement Longitudinal Study (CHARLS). The incident of symptomatic KOA was defined as participants who were free of symptomatic KOA at baseline (CHARLS2011) and diagnosed with symptomatic KOA at the 4-year follow-up (CHARLS2015). The effects of potential predictors on the incident of KOA were estimated using logistic regression models and the final model was internally validated using the bootstrapping technique. Model performance was assessed based on discrimination-area under the receiver operating characteristic curve (AUC)-and calibration. RESULTS: A total of 815 incidents of KOA were identified at the 4-year follow-up, resulting in a cumulative incidence of approximately 9.95%. The final multivariable model included age, sex, waist circumference, residential area, difficulty with activities of daily living (ADLs)/instrumental activities of daily living (IADLs), history of hip fracture, depressive symptoms, number of chronic comorbidities, self-rated health status, and level of moderate physical activity (MPA). The risk model showed good discrimination with AUC = 0.719 (95% confidence interval [CI] 0.700-0.737) and optimism-corrected AUC = 0.712 after bootstrap validation. A satisfactory agreement was observed between the observed and predicted probability of incident symptomatic KOA. And a simple clinical score model was developed for quantifying the risk of KOA. CONCLUSION: Our prediction model may aid the early identification of individuals at the greatest risk of developing KOA within 4 years.
OBJECTIVES: We aimed to develop a model for predicting the 4-year risk of knee osteoarthritis (KOA) based on survey data obtained via a random, nationwide sample of Chinese individuals. METHODS: Data was analyzed from 8193 middle-aged and older adults included in the China Health and Retirement Longitudinal Study (CHARLS). The incident of symptomatic KOA was defined as participants who were free of symptomatic KOA at baseline (CHARLS2011) and diagnosed with symptomatic KOA at the 4-year follow-up (CHARLS2015). The effects of potential predictors on the incident of KOA were estimated using logistic regression models and the final model was internally validated using the bootstrapping technique. Model performance was assessed based on discrimination-area under the receiver operating characteristic curve (AUC)-and calibration. RESULTS: A total of 815 incidents of KOA were identified at the 4-year follow-up, resulting in a cumulative incidence of approximately 9.95%. The final multivariable model included age, sex, waist circumference, residential area, difficulty with activities of daily living (ADLs)/instrumental activities of daily living (IADLs), history of hip fracture, depressive symptoms, number of chronic comorbidities, self-rated health status, and level of moderate physical activity (MPA). The risk model showed good discrimination with AUC = 0.719 (95% confidence interval [CI] 0.700-0.737) and optimism-corrected AUC = 0.712 after bootstrap validation. A satisfactory agreement was observed between the observed and predicted probability of incident symptomatic KOA. And a simple clinical score model was developed for quantifying the risk of KOA. CONCLUSION: Our prediction model may aid the early identification of individuals at the greatest risk of developing KOA within 4 years.
Authors: Uyen-Sa D T Nguyen; Yuqing Zhang; Yanyan Zhu; Jingbo Niu; Bin Zhang; David T Felson Journal: Ann Intern Med Date: 2011-12-06 Impact factor: 25.391
Authors: Weiya Zhang; Daniel F McWilliams; Sarah L Ingham; Sally A Doherty; Stella Muthuri; Kenneth R Muir; Michael Doherty Journal: Ann Rheum Dis Date: 2011-05-25 Impact factor: 19.103