ArticleJournal of clinical medicine2021
Static and Dynamic Prediction of Chronic Renal Disease Progression Using Longitudinal Clinical Data from Taiwan's National Prevention Programs.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b.Diagnostics (Basel, Switzerland) · 2024Article
- An Approach for Personalized Dynamic Assessment of Chronic Kidney Disease Progression Using Joint Model.Biomedicines · 2024Article
- Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression.Biomedicines · 2023Article
- Progression of chronic kidney disease among black patients attending a tertiary hospital in Johannesburg, South Africa.PloS one · 2023Article
- Machine Learning Models for the Prediction of Renal Failure in Chronic Kidney Disease: A Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Kidney diseases can cause severe morbidity, mortality, and health burden. Determining the risk factors associated with kidney damage and deterioration has become a priority for the prevention and treatment of kidney disease. This study followed 1042 chronic kidney disease (CKD) patients with Stage 3-5 kidney disease who were treated at a public veteran's hospital through the national prevention program. A total of 12.5 years of records of clinical measurements were collected and analyzed using dynamic and static Cox hazard models to predict the progression to dialysis treatment. The results showed that the statistical significance of several variables in patients with Stage 3-5 CKD was attenuated while the dynamic model was being used. The estimated glomerular filtration rate (eGFR) and urine protein to creatinine ratio (PCR) had the powerful ability to predict the progression of CKD patients with Stage 3a and Stage 3b-5 kidney disease, whereas serum calcium was also predictive for the progression of Stages 3b-5 CKD. Because these two sub-stages of Stage 3 CKD are often associated with differences in routine measurements and the risk analysis of renal dialysis, future research can use this predictive model as a reference while similar prevention programs are implemented.
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Registered trials
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