ArticlePloS one2016
A New Data Analysis System to Quantify Associations between Biochemical Parameters of Chronic Kidney Disease-Mineral Bone Disease.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Effects of increasing levels of dietary cation-anion difference on growth performance, nutrient digestibility, rumen fermentation, and rumen microbiota in fattening Hu sheep.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2025Article
- Contemporary Perspectives on Chronic Renal Disorders.Chronic diseases and translational medicine · 2025Review
- Article
- Machine learning progressive CKD risk prediction model is associated with CKD-mineral bone disorder.Bone reports · 2024Article
- The value of clinical-ultrasonographic feature model to predict the severity of secondary hyperparathyroidism.Renal failure · 2022Observational
- Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease.Frontiers in medicine · 2022Review
- Predicting mortality in hemodialysis patients using machine learning analysis.Clinical kidney journal · 2021Article
- Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.BioMed research international · 2020Review
- Role of Artificial Intelligence in Kidney Disease.International journal of medical sciences · 2020Review
- Long-term effectiveness of cinacalcet in non-dialysis patients with chronic kidney disease and secondary hyperparathyroidism.Kidney research and clinical practice · 2019Article
- Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other.Journal of renal injury prevention · 2017Article
- Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects.Kidney diseases (Basel, Switzerland)Article
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn hemodialysis patients, deviations from KDIGO recommended values of individual parameters, phosphate, calcium or parathyroid hormone (PTH), are associated with increased mortality. However, it is widely accepted that these parameters are not regulated independently of each other and that therapy aimed to correct one parameter often modifies the others. The aim of the present study is to quantify the degree of association between parameters of chronic kidney disease and mineral bone disease (CKD-MBD).
methodsData was extracted from a cohort of 1758 adult HD patients between January 2000 and June 2013 obtaining a total of 46.141 records (10 year follow-up). We used an advanced data analysis system called Random Forest (RF) which is based on self-learning procedure with similar axioms to those utilized for the development of artificial intelligence. This new approach is particularly useful when the variables analyzed are closely dependent to each other.
resultsThe analysis revealed a strong association between PTH and phosphate that was superior to that of PTH and Calcium. The classical linear regression analysis between PTH and phosphate shows a correlation coefficient is 0.27, p<0.001, the possibility to predict PTH changes from phosphate modification is marginal. Alternatively, RF assumes that changes in phosphate will cause modifications in other associated variables (calcium and others) that may also affect PTH values. Using RF the correlation coefficient between changes in serum PTH and phosphate is 0.77, p<0.001; thus, the power of prediction is markedly increased. The effect of therapy on biochemical variables was also analyzed using this RF.
conclusionOur results suggest that the analysis of the complex interactions between mineral metabolism parameters in CKD-MBD may demand a more advanced data analysis system such as RF.
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