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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

197 participants

Study Classification

OBSERVATIONAL

Study Start Date

2016-02-01

Study Completion Date

2018-12-19

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Chronic Kidney Disease Mineral and Bone Disorder

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Incident hemodialysis patients
* Non acute renal failure

Exclusion Criteria

* Previous treatment with cinacalcet
* Neoplasia
* Previous parathyrodectomy

Minimum Eligible Age

18 Years

Maximum Eligible Age

90 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Alejandro Martín Malo, MD

Role: PRINCIPAL_INVESTIGATOR

Andalusian Public Health System

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Hospital Universitario Reina Sofía

Córdoba, , Spain

Site Status

Countries

Review the countries where the study has at least one active or historical site.

Spain

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

PI-0311-2014

Identifier Type: -

Identifier Source: org_study_id