國家衛生研究院 NHRI:Item 3990099045/14349
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/14349


    Title: A prediction model with lifestyle factors improves the predictive ability for renal replacement therapy: A cohort of 442 714 Asian adults
    Authors: Tsai, MK;Gao, WN;Chien, KL;Hsu, CC;Wen, CP
    Contributors: Institute of Population Health Sciences
    Abstract: Background There are limited renal replacement therapy (RRT) prediction models with good performance in the general population. We developed a model that includes lifestyle factors to improve predictive ability for RRT in the population at large. Methods We used data collected between 1996 and 2017 from a medical screening in a cohort comprising 442 714 participants aged 20 years or over. After a median follow-up of 13 years, we identified 2212 individuals with end-stage renal disease (RRT, n: 2091; kidney transplantation, n: 121). We built three models for comparison: model 1: basic model, Kidney Failure Risk Equation with four variables (age, sex, estimated glomerular filtration rate and proteinuria); model 2: basic model + medical history + lifestyle risk factors; and model 3: model 2 + all significant clinical variables. We used the Cox proportional hazards model to construct a points-based model and applied the C statistic. Results Adding lifestyle factors to the basic model, the C statistic improved in model 2 from 0.91 to 0.94 (95% confidence interval: 0.94, 0.95). Model 3 showed even better C statistic value i.e., 0.95 (0.95, 0.96). With a cut-off score of 33, model 3 identified 3% of individuals with RRT risk in 10 years. This model detected over half of individuals progressing to RRT, which was higher than the sensitivity of cohort participants with stage 3 or higher chronic kidney disease (0.53 versus 0.48). Conclusions Our prediction model including medical history and lifestyle factors improved the predictive ability for end-stage renal disease in the general population in addition to chronic kidney disease population.
    Date: 2022-04-30
    Relation: Clinical Kidney Journal. 2022 Apr 30;15(10):1896-1907.
    Link to: http://dx.doi.org/10.1093/ckj/sfac119
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2048-8505&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000807878700001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85153953743
    Appears in Collections:[Chi-Pang Wen(2001-2010)] Periodical Articles
    [Chih-Cheng Hsu] Periodical Articles

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