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

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.

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

“…NNs were used to predict HD patients who need more frequent vitamin D dosage, using only simple clinical parameters [38]. However, analysis of the complex interactions between mineral metabolism parameters in ESRD may demand a more advanced data analysis system such as random forest (RF) [39].…”

Role of Artificial Intelligence in Kidney Disease

“…To quantify the degree of association between the three parameters, Mariano et al developed a data analysis system by RF from 1,758 HD patients. Comparing with Classical statistical methods, the power of prediction of the new model was markedly increased [80]. In 2018, Kleiman et al [81] used RF algorithms to build model predicting risks for development of calciphylaxis in CKD patients.With an AUC value of 0.872, the model could successfully predict calciphylaxis, provide an opportunity for clinical translation of the predictive models.…”

Parathyroidectomy in dialysis patients: Indications, methods, and consequences

“…During the early stages of CKD, both serum calcium (Ca) and P levels are maintained due to the elevation in FGF23 and PTH but these compensatory mechanisms fail with progressive deterioration of renal function. Then, serum Ca decreases and serum P increases, and both are responsible for the further development of SHPT . The low 1,25D 3 increases parathyroid function by acting on the parathyroid cell vitamin D receptor (VDR).…”

“…These failures have been ascribed to intrinsic factors associated with large hyperplasic parathyroid glands with decreased expression of VDR and Ca‐sensing receptors (CaSR). The inadequate response to 1,25D 3 is also attributed to a poor control of serum P . The abnormal parathyroid response to 1,25D 3 in uremic patients with severe parathyroid hyperplasia may be due, to a large extent, to the development of nodular hyperplasia as a result of the clonal transformation from diffuse hyperplasia .…”