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

NCT ID: NCT05214105

Last Updated: 2023-12-14

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 400 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-07-05
• Study Completion Date: 2026-01-31

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.

Detailed Description

Sickle cell disease (SCD) is characterized by a vasculopathy affecting multiple end organs, with complications including ischemic stroke, pulmonary hypertension, and chronic kidney disease (CKD). Albuminuria, an early measure of glomerular injury and a manifestation of CKD, is common in SCD and predicts progressive kidney disease. Kidney function decline is faster in SCD patients than in the general African American population. The prevalence of rapid decline, commonly defined as an estimated glomerular filtration rate (eGFR) decline of >3 mL/min/1.73 m2 per year, is ~ 31% in SCD, 3-fold higher than in the general population. Furthermore, high-risk Apolipoprotein 1 (APOL1) variants are associated with an increased risk of albuminuria and progression of CKD in SCD. It is well recognized that kidney disease, regardless of severity, is associated with increased mortality in SCD. The investigators have recently observed that rapid eGFR decline is also independently associated with increased mortality in SCD. Early identification of patients at risk for progression of CKD is important to address potentially modifiable risk factors, slow eGFR decline and reduce mortality.

The investigators have previously reported that machine learning (ML) models can identify patients at high risk for rapid decline in kidney function. In this study, the investigators propose the conduct of a prospective, multi-center study to build a ML-based predictive model for progression of CKD in adults with SCD. A model with high predictive capacity for progression of CKD not only affords risk-stratification, but also offers opportunities to modify known risk factors in hopes of attenuating kidney function loss and decreasing mortality risk.

The overall hypothesis is that ML models utilizing clinical and laboratory characteristics, additional biomarkers and genetic assessments have a higher predictive capacity for progression of CKD than persistent albuminuria alone in adults with sickle cell anemia.

Conditions

Sickle Cell Disease; Kidney Diseases, Chronic

Keywords

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Patients with sickle cell anemia

Prospective longitudinal study of patients with sickle cell anemia

Biospecimen/DNA collection and analysis

Intervention Type: OTHER

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

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).

Intervention Type: OTHER

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Heart, Lung, and Blood Institute (NHLBI)

NIH

Sponsor Role: collaborator

University of Illinois at Chicago

OTHER

Sponsor Role: collaborator

University of Memphis

OTHER

Sponsor Role: collaborator

University of North Carolina, Charlotte

OTHER

Sponsor Role: collaborator

Wake Forest University

OTHER

Sponsor Role: collaborator

University of North Carolina, Chapel Hill

OTHER

Sponsor Role: collaborator

University of Tennessee

OTHER

Sponsor Role: lead

Responsible Party

Kenneth Ataga MD

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Kenneth I Ataga, MD

Role: PRINCIPAL_INVESTIGATOR

The University of Tennessee Health Science Center

Locations

University of Illinois at Chicago

Chicago, Illinois, United States

Site Status: RECRUITING

Wake Forest University

Winston-Salem, North Carolina, United States

Site Status: NOT_YET_RECRUITING

The University of Tennessee Health Science Center

Memphis, Tennessee, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Kenneth I Ataga, MD

Role: CONTACT

Phone: 901-448-2813

Email: kataga@uthsc.edu

Santosh Saraf, MD

Role: CONTACT

Phone: 312-996-5680

Email: ssaraf@uic.edu

Facility Contacts

Santosh Saraf, MD

Role: primary

Payal Desai, MD

Role: primary

Kenneth Ataga, MD

Role: primary

Other Identifiers

1R01HL159376-01

Identifier Type: NIH

Identifier Source: secondary_id

View Link

2021-0746

Identifier Type: -

Identifier Source: org_study_id