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

Launched by UNIVERSITY OF TENNESSEE · Jan 27, 2022

Keywords

ClinConnect Summary

This clinical trial is exploring how well machine learning (ML) models can predict the progression of chronic kidney disease (CKD) in individuals with sickle cell anemia. The study aims to follow eligible participants for at least one year, and possibly up to four years, to gather important data on kidney health in this population.

To be eligible for the trial, participants must be between 18 and 65 years old and have specific types of sickle cell disease. They should also be in a stable health state, meaning they haven't had any painful crises requiring medical help in the last month. Those who are pregnant, have poorly controlled high blood pressure, or certain other health conditions, like diabetes that affects the kidneys, will not be able to participate. If you join the study, you can expect regular check-ups and monitoring, which will help researchers learn more about kidney disease in people with sickle cell anemia. This information could lead to better care and treatment options in the future.

Gender

ALL

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.