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

About the study

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.

Study point of contact

Kenneth I Ataga, MD
[email protected]
Santosh Saraf, MD
[email protected]


18 Years - 65 Years

Study type






participation requirements

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

participation restrictions

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

Last updated 2022-07-14