Using machine learning to predict kidney disease progression in sickle cell anemia patients

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

This study is testing if machine learning can help predict how kidney disease will progress in people with sickle cell anemia.

Quick facts

• Study type: Observational
• Enrollment: 400 (estimated)
• Ages: 18 Years to 65 Years
• Sex: All
• Sponsor: University of Tennessee (Academic / other)
• Locations: 3 sites (Chicago, Illinois and 2 other locations)
• Trial ID: NCT05214105 on ClinicalTrials.gov

What this trial studies

This multicenter prospective cohort study aims to evaluate the predictive capacity of machine learning models for the progression of chronic kidney disease (CKD) in individuals with sickle cell disease (SCD). The study will follow eligible patients for a minimum of 12 months, potentially extending up to 4 years, to assess kidney function decline and identify risk factors associated with rapid progression. By analyzing biospecimens and clinical data, the researchers hope to develop a model that can effectively predict which patients are at higher risk for worsening kidney health. This approach is particularly important given the higher prevalence of CKD and associated mortality in SCD patients compared to the general population.

Who should consider this trial

Good fit: Ideal candidates for this study are adults aged 18 to 65 with HbSS or HbSβ0 thalassemia who are in a non-crisis, steady state.

Not a fit: Patients with conditions such as poorly controlled hypertension, diabetic nephropathy, or end-stage renal disease on chronic dialysis may not benefit from this study.

Why it matters

Potential benefit: If successful, this study could lead to earlier identification and intervention for patients at risk of rapid kidney disease progression, potentially improving their health outcomes.

How similar studies have performed: Previous studies have shown that machine learning models can effectively identify patients at high risk for rapid kidney function decline, indicating a promising approach in this area.

Eligibility criteria

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.

Where this trial is running

Chicago, Illinois and 2 other locations

• University of Illinois at Chicago — Chicago, Illinois, United States (Recruiting)
• Wake Forest University — Winston-Salem, North Carolina, United States (Not_yet_recruiting)
• The University of Tennessee Health Science Center — Memphis, Tennessee, United States (Recruiting)

Study contacts

• Principal investigator: Kenneth I Ataga, MD — The University of Tennessee Health Science Center
• Study coordinator: Kenneth I Ataga, MD

Conditions Sickle Cell Disease; Kidney Diseases, Chronic; Machine Learning Models; Chronic Kidney Disease; eGFR; Anemia, Sickle Cell; Albuminuria; Renal Insufficiency, Chronic