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

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

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..

Study Overview

• Status: Completed
• Conditions: Chronic Kidney Disease Mineral and Bone Disorder

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.

• Study Type: Observational
• Enrollment (Actual): 197

Contacts and Locations

Study Locations

• Spain
  • Córdoba, Spain, 14004
    • Hospital Universitario Reina Sofia

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 90 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Incident hemodialysis patients

Description

Inclusion Criteria:

• Incident hemodialysis patients
• Non acute renal failure

Exclusion Criteria:

• Previous treatment with cinacalcet
• Neoplasia
• Previous parathyrodectomy

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Maimónides Biomedical Research Institute of Córdoba
• Investigators: Principal Investigator: Alejandro Martín Malo, MD, Andalusian Public Health System

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2016
• Primary Completion (Actual): December 19, 2018
• Study Completion (Actual): December 19, 2018

Study Registration Dates

• First Submitted: February 23, 2016
• First Submitted That Met QC Criteria: February 26, 2016
• First Posted (Estimate): March 3, 2016

Study Record Updates

• Last Update Posted (Actual): December 21, 2018
• Last Update Submitted That Met QC Criteria: December 20, 2018
• Last Verified: December 1, 2018

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Metabolic Diseases; Urologic Diseases; Endocrine System Diseases; Renal Insufficiency; Nutrition Disorders; Musculoskeletal Diseases; Parathyroid Diseases; Avitaminosis; Deficiency Diseases; Malnutrition; Bone Diseases, Metabolic; Calcium Metabolism Disorders; Rickets; Vitamin D Deficiency; Hyperparathyroidism, Secondary; Hyperparathyroidism; Kidney Diseases; Renal Insufficiency, Chronic; Bone Diseases; Chronic Kidney Disease-Mineral and Bone Disorder
• Other Study ID Numbers: PI-0311-2014

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No