Clinical trial NCT02697578: Artificial Intelligence: a New Alternative to Analyse CKD-MBD in Hemodialysis
Official title: Artificial Intelligence: to Analyse CKD-MBD in Hemodialysis and Cardiovascular Risk
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Chronic Kidney Disease Mineral and Bone Disorder
Lead sponsor: Maimónides Biomedical Research Institute of Córdoba
Sponsor class: OTHER
First posted: 2016-03-03
Enrollment: 197
Sex: ALL
Minimum age (years): 18
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Incident hemodialysis patients

Brief 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..

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.

Eligibility criteria: Inclusion Criteria:

* Incident hemodialysis patients
* Non acute renal failure

Exclusion Criteria:

* Previous treatment with cinacalcet
* Neoplasia
* Previous parathyrodectomy

Primary outcomes:
measure: Change from Baseline Fibroblast growth factor 23 (pg/ml) at 24 months
timeFrame: Baseline, 24 months
Secondary outcomes:
Other outcomes:
Locations:
city: Córdoba
country: Spain
facility: Hospital Universitario Reina Sofía
Study officials:
affiliation: Andalusian Public Health System
name: Alejandro Martín Malo, MD
role: PRINCIPAL_INVESTIGATOR
References: