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

NCT02697578 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 197

Last updated 2018-12-21

No results posted yet for this study

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..

Conditions

  • Chronic Kidney Disease Mineral and Bone Disorder

Sponsors & Collaborators

  • Maimónides Biomedical Research Institute of Córdoba

    lead OTHER

Principal Investigators

  • Alejandro Martín Malo, MD · Andalusian Public Health System

Eligibility

Min Age
18 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2016-02-01
Primary Completion
2018-12-19
Completion
2018-12-19

Countries

  • Spain

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT02697578 on ClinicalTrials.gov