A New Data Analysis System to Quantify Associations between Biochemical Parameters of Chronic Kidney Disease-Mineral Bone Disease

Mariano Rodriguez, M Dolores Salmeron, Alejandro Martin-Malo, Carlo Barbieri, Flavio Mari, Rafael I Molina, Pedro Costa and Pedro Aljama

PLOS ONE, 2016, vol. 11, issue 1, 1-14

Abstract: Background: In 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). Methods: Data 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. Results: The 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

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0146801

DOI: 10.1371/journal.pone.0146801

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