Artificial Intelligence: a New Alternative to Analyse CKD-MBD in Hemodialysis
Condition: Chronic Kidney Disease Mineral and Bone Disorder
MeSH terms: Bone Diseases; Chronic Kidney Disease-Mineral and Bone Disorder; Kidney Diseases; Renal Insufficiency, Chronic
Study type: Observational

Summary: 
      The regulation of calcium, phosphate and parathyroid hormone in hemodialysis is complex and
      each parameter is not independently regulated. Simultaneous modification in these three
      parameters are the result of abnormal mineral metabolism and the treatment used. The specific
      objective of this work is an accurate and exhaustive analysis and description of the complex
      relationships between clinically relevant parameters in chronic kidney disease metabolism
      bone disease. In order to achieve these objectives we have used a machine learning approach
      Random Forest able to extract useful knowledge from a large database. The analysis of the
      complex interactions between the different parameters needs an advance mathematical approach
      such as Random Forest . The second aim of this study is to determine whether calcium,
      phosphate and parathyroid hormone, Fibroblast growth factor 23 and calcitriol are long-term
      associated with demographic features, mortality, co-morbidity and the therapy prescribed. We
      will analyze in a prospective study on incident patients, whether the use of this new model
      may predict the cardiovascular risk..
    

Detailed description: 
      In hemodialysis patients, deviations of serum concentration of calcium, phosphate or
      parathyroid hormone from the values recommended by KDIGO are associated to a negative
      outcome. The regulation of calcium, phosphate and parathyroid hormone is complex and each
      parameter is not independently regulated. In hemodialysis patient's simultaneous modification
      in these three parameters are the result of abnormal mineral metabolism and the treatment
      used to correct these abnormalities that usually produce changes in more than one parameter.
      The specific objective of this work is an accurate and exhaustive analysis and description of
      the complex relationships between clinically relevant parameters in chronic kidney disease
      metabolism bone disease. In order to achieve these objectives we have used a machine learning
      approach Random Forest able to extract useful knowledge from a large database. The analysis
      of the complex interactions between the different parameters needs an advance mathematical
      approach such as Random Forest . The second aim of this study is to determine whether
      calcium, phosphate and parathyroid hormone, Fibroblast growth factor 23 and calcitriol are
      long-term associated with demographic features, mortality, co-morbidity and the therapy
      prescribed. Compare the predictions obtained with conventional statistical analysis versus
      the new model analysis based on artificial intelligence. Our preliminary results suggest that
      there are interactions between some parameters that are strong enough to question whether the
      evaluation of a given therapy can be based in the measurement of one single parameter.
      Subsequently, we will analyze in a prospective study on incident patients, whether the use of
      this new model may predict the cardiovascular risk and reduce the therapy cost.
    

Observational model: Cohort
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        Incident hemodialysis patients
      
Biospecimen description: 
      Pre-dialysis blood samples