Artificial Intelligence: a New Alternative to Analyse CKD-MBD in Hemodialysis

Chronic Kidney Disease Mineral and Bone Disorder Clinical Trial

Official title:

Artificial Intelligence: to Analyse CKD-MBD in Hemodialysis and Cardiovascular Risk

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT02697578
• Other study ID #: PI-0311-2014
• Status: Completed
• Start date: February 1, 2016
• Est. completion date: December 19, 2018

Study information

• Verified date: December 2018
• Source: Maimónides Biomedical Research Institute of Córdoba
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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..

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 197
• Est. completion date: December 19, 2018
• Est. primary completion date: December 19, 2018
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 90 Years

Eligibility

Inclusion Criteria:

• Incident hemodialysis patients

• Non acute renal failure

Exclusion Criteria:

• Previous treatment with cinacalcet

• Neoplasia

• Previous parathyrodectomy

Related Conditions & MeSH terms

• Bone Diseases
• Chronic Kidney Disease Mineral and Bone Disorder
• Chronic Kidney Disease-Mineral and Bone Disorder
• Kidney Diseases
• Renal Insufficiency, Chronic

Locations

Country	Name	City	State
Spain	Hospital Universitario Reina Sofía	Córdoba

Sponsors (1)

Lead Sponsor	Collaborator
Maimónides Biomedical Research Institute of Córdoba

Country where clinical trial is conducted

Spain, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change from Baseline Fibroblast growth factor 23 (pg/ml) at 24 months	Prospective analysis of fibroblast growth factor in a cohort of incident hemodialysis patients	Baseline, 24 months