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

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..

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Key information

Conditions

Chronic Kidney Disease Mineral and Bone Disorder Avitaminosis Bone Diseases Bone Diseases, Metabolic Calcium Metabolism Disorders Chronic Disease Chronic Kidney Disease-Mineral and Bone Disorder Deficiency Diseases Disease Attributes Endocrine System Diseases Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Hyperparathyroidism Hyperparathyroidism, Secondary Kidney Diseases Male Urogenital Diseases Malnutrition Metabolic Diseases Musculoskeletal Diseases Nutrition Disorders Nutritional and Metabolic Diseases Parathyroid Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Rickets Urogenital Diseases Urologic Diseases Vitamin D Deficiency

Age range

18 year–90 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Hospital Universitario Reina Sofía

Córdoba, 14004, Spain

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Incident hemodialysis patients
Non acute renal failure

Exclusion criteria

Previous treatment with cinacalcet
Neoplasia
Previous parathyrodectomy

Treatment and study plan

Primary outcomes

Change from Baseline Fibroblast growth factor 23 (pg/ml) at 24 months

Time frame: Baseline, 24 months

Prospective analysis of fibroblast growth factor in a cohort of incident hemodialysis patients

Sponsors and collaborators

Lead sponsor

Maimónides Biomedical Research Institute of Córdoba

Other

Registry information

Official study title

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

Important dates

Study start: 2016
Primary completion: 2018
Study completion: 2018
First posted: Mar 3, 2016
Registry last updated: Dec 21, 2018

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

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