ArticleClinical kidney journal2022
A prediction model with lifestyle factors improves the predictive ability for renal replacement therapy: a cohort of 442 714 Asian adults.
Article in Clinical kidney journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association of chronic kidney disease and risk of hearing loss: a systematic review and meta-analysis.Brazilian journal of otorhinolaryngologyPooled it
- Nomograms with arterial spin labeling for diagnosing early-stage chronic kidney disease.International urology and nephrology · 2026Article
- Cardiovascular-kidney-metabolic syndrome and all-cause and cardiovascular mortality: A retrospective cohort study.PLoS medicine · 2025 · on this mapArticle
- Resting Heart Rate Independent of Cardiovascular Disease Risk Factors Is Associated With End-Stage Renal Disease: A Cohort Study Based on 476 347 Adults.Journal of the American Heart Association · 2023Article
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5 authors.
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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, 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.
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