RECRUITING

The Predictive Capacity of Machine Learning Models for Progressive Kidney Disease in Individuals With Sickle Cell Anemia

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Conditions

Sickle Cell DiseaseKidney Diseases, ChronicMachine Learning ModelsChronic Kidney DiseaseeGFRAnemia, Sickle CellAlbuminuriaRenal Insufficiency, ChronicRenal InsufficiencyAPOL1

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Study Overview

This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.

Description

This is a multicenter prospective, longitudinal cohort study which will evaluate the predictive capacity of machine learning (ML) models for progression of CKD in eligible patients for a minimum of 12 months and potentially for up to 4 years.

Official Title

Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models [PREMIER]

Quick Facts

Study Start:2022-07-05
Study Completion:2026-01-31
Study Type:Not specified
Phase:Not Applicable
Enrollment:Not specified
Status:RECRUITING

Study ID

NCT05214105

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Ages Eligible for Study:18 Years to 65 Years
Sexes Eligible for Study:ALL
Accepts Healthy Volunteers:No
Standard Ages:ADULT, OLDER_ADULT
Inclusion CriteriaExclusion Criteria
  1. 1. HbSS or HbSβ0 thalassemia, 18 - 65 years old;
  2. 2. non-crisis, "steady state" with no acute pain episodes requiring medical contact in preceding 4 weeks;
  3. 3. ability to understand the study requirements.
  1. 1. pregnant at enrollment;
  2. 2. poorly controlled hypertension;
  3. 3. long-standing diabetes with suspicion for diabetic nephropathy;
  4. 4. connective tissue disease such as systemic lupus erythematosus (SLE);
  5. 5. polycystic kidney disease or glomerular disease unrelated to SCD;
  6. 6. stem cell transplantation;
  7. 7. untreated human immunodeficiency virus (HIV), hepatitis B or C infection; h) history of cancer in last 5 years; i) End-stage renal disease (ESRD) on chronic dialysis; j) prior kidney transplantation.

Contacts and Locations

Study Contact

Kenneth I Ataga, MD
CONTACT
901-448-2813
kataga@uthsc.edu
Santosh Saraf, MD
CONTACT
312-996-5680
ssaraf@uic.edu

Principal Investigator

Kenneth I Ataga, MD
PRINCIPAL_INVESTIGATOR
The University of Tennessee Health Science Center

Study Locations (Sites)

University of Illinois at Chicago
Chicago, Illinois, 60612
United States
Wake Forest University
Winston-Salem, North Carolina, 27109
United States
The University of Tennessee Health Science Center
Memphis, Tennessee, 38104
United States

Collaborators and Investigators

Sponsor: University of Tennessee

  • Kenneth I Ataga, MD, PRINCIPAL_INVESTIGATOR, The University of Tennessee Health Science Center

Study Record Dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Registration Dates

Study Start Date2022-07-05
Study Completion Date2026-01-31

Study Record Updates

Study Start Date2022-07-05
Study Completion Date2026-01-31

Terms related to this study

Keywords Provided by Researchers

  • Machine Learning Models
  • Sickle Cell Disease
  • Chronic Kidney Disease
  • eGFR
  • Anemia, Sickle Cell
  • Albuminuria
  • Renal Insufficiency, Chronic
  • Renal Insufficiency
  • APOL1

Additional Relevant MeSH Terms

  • Sickle Cell Disease
  • Kidney Diseases, Chronic