Recruiting

Observational Study

Sponsor:

University of Tennessee

Code:

NCT05214105

Conditions

Sickle Cell Disease

Kidney Diseases, Chronic

Eligibility Criteria

Sex: All

Age: 18 - 65

Healthy Volunteers: Not accepted

Interventions

Biospecimen/DNA collection and analysis

Study Details

Brief summary:

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.

Conditions

Sickle Cell Disease

Kidney Diseases, Chronic

Study ID

NCT05214105

Start date

Jul 5, 2022

Status verified date

Dec, 2023

Completion date

Jan 31, 2026

Anticipated

Primary completion date

Jan 31, 2026

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18 - 65

Healthy Volunteers: Not accepted

Inclusion Criteria:

1. HbSS or HbSβ0 thalassemia, 18 - 65 years old;
2. non-crisis, "steady state" with no acute pain episodes requiring medical contact in preceding 4 weeks;
3. ability to understand the study requirements.

Exclusion Criteria:

1. pregnant at enrollment;
2. poorly controlled hypertension;
3. long-standing diabetes with suspicion for diabetic nephropathy;
4. connective tissue disease such as systemic lupus erythematosus (SLE);
5. polycystic kidney disease or glomerular disease unrelated to SCD;
6. stem cell transplantation;
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.

Study Design

Enrollment

400 participants

Anticipated

Interventions and Outcome Measures

Arms

Patients with sickle cell anemia

Prospective longitudinal study of patients with sickle cell anemia

Interventions

Biospecimen/DNA collection and analysis

Patients will be followed longitudinally with collection of CBC and chemistries as well as research biomarkers (urine, plasma, and genomic materials).

Primary outcome measure

  • Develop two separate predictive models for progression of CKD (eGFR <90 mL/min/1·73 m2 and ≥25% drop in eGFR from baseline) and rapid eGFR decline (eGFR loss >3·0 mL/min/1·73 m2 per year) over the 12 months following the baseline clinic evaluation. [ Time Frame: 12 months ]

Central Contacts and Locations

Central contacts

Kenneth I Ataga, MD

901-448-2813kataga@uthsc.edu

Santosh Saraf, MD

312-996-5680ssaraf@uic.edu

Locations

University of Illinois at Chicago

Recruiting

Chicago, Illinois, United States, 60612

Contacts

Santosh Saraf, MD

ssaraf@uic.edu

Principal Investigator:

Santosh Saraf, MD

The University of Tennessee Health Science Center

Recruiting

Memphis, Tennessee, United States, 38104

Contacts

Kenneth Ataga, MD

901-448-2813kataga@uthsc.edu

Principal Investigator:

Kenneth Ataga, MD

More Information

Sponsor

University of Tennessee

Last update posted

Dec 14, 2023

Last verified

Dec, 2023

Keywords

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

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-09. This information was provided to ClinicalTrials.gov by University of Tennessee on 2023-12-14.