ArticleJournal of translational medicine2017
Application of survival tree analysis for exploration of potential interactions between predictors of incident chronic kidney disease: a 15-year follow-up study.
Article in Journal of translational medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Modifiable Lifestyle Factors for Primary Prevention of CKD: A Systematic Review and Meta-Analysis.Journal of the American Society of Nephrology : JASN · 2021Pooled it
- Interaction Patterns Among Risk Factors for Liver Cancer in Patients With Type 2 Diabetes in Primary Care: A Retrospective Cohort Study.Health science reports · 2026Article
- Comparison of Risk Factors, Their Interaction Patterns, and Scoring Systems for Liver Cancer Between Patients With and Those Without Diabetes: Retrospective Cohort Study Using Electronic Health Records and Tree-Structured Algorithms.JMIR medical informatics · 2025Article
- Functional Performance Decline Outperforms Sarcopenia and Its Components in Predicting New-Onset Chronic Kidney Disease: A Nationwide Multicenter Study.Kidney medicine · 2025Article
- Survival Tree Analysis of Interactions Among Factors Associated With Colorectal Cancer Risk in Patients With Type 2 Diabetes: Retrospective Cohort Study.JMIR public health and surveillance · 2025Article
- On a Bayesian multivariate survival tree approach based on three frailty models.Scientific reports · 2025Article
- The interactions among factors associated with the risk of lung cancer among diabetes patients: a survival tree analysis.NPJ primary care respiratory medicine · 2025Article
- Association of physical activity with risk of chronic kidney disease in China: A population-based cohort study.Journal of sport and health science · 2024Article
- Comparison of models for stroke-free survival prediction in patients with CADASIL.Scientific reports · 2023Observational
- Geriatric Assessment Predictors of 1-Year Mortality in Older Adults With GI Malignancies: A Survival Tree Analysis.JCO clinical cancer informatics · 2022Article
- Impact of self-reported walking habit on slower decline in renal function among the general population in a longitudinal study: the Japan Specific Health Checkups (J-SHC) Study.Journal of nephrology · 2021Article
- Application of ensemble clustering and survival tree analysis for identifying prognostic clinicogenomic features in patients with colorectal cancer from the 100,000 Genomes Project.BMC research notes · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundChronic kidney disease (CKD) is a growing public health challenges worldwide. Various studies have investigated risk factors of incident CKD; however, a very few studies examined interaction between these risk factors. In an attempt to clarify the potential interactions between risk factors of CKD, we performed survival tree analysis.
methodsA total of 8238 participants (46.1% men) aged > 20 years without CKD at baseline [(1999-2001) and (2002-2005)], were followed until 2014. The first occurrence of CKD, defined as the estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m
resultsThe crude incidence rates of CKD were 20.2 and 35.2 per 1000 person-years in men and women, respectively. The Cox PH identified the main effect of significant predictors of CKD incidence in men and women. In addition, using a limited number of predictors, survival trees identified 12 and 10 subgroups among men and women, respectively, with different survival probability. Accordingly, a group of men with eGFR > 74 ml/min/1.73 m
conclusionIn this post hoc analysis, we found the independent predictors of CKD using Cox PH; furthermore, by applying survival tree analysis we identified several numbers of homogeneous subgroups with different risk for incidence of CKD. Our study suggests that two methods can be used simultaneously to provide new insights for intervention programs and improve clinical decision making.
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