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

• ClinicalTrials.gov Identifier: NCT05214105
• Recruitment Status: Recruiting
• First Posted: January 28, 2022
• Last Update Posted: December 14, 2023
• Sponsor: University of Tennessee
• Collaborators: National Heart, Lung, and Blood Institute (NHLBI); University of Illinois at Chicago; University of Memphis; University of North Carolina, Charlotte; Wake Forest University; University of North Carolina, Chapel Hill
• Information provided by (Responsible Party): Kenneth Ataga MD, University of Tennessee

Study Description

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.

Condition or disease	Intervention/treatment
Sickle Cell Disease; Kidney Diseases, Chronic	Other: Biospecimen/DNA collection and analysis

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.

Study Design

• Study Type: Observational
• Estimated Enrollment: 400 participants
• Observational Model: Cohort
• Time Perspective: Prospective
• Official Title: Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models [PREMIER]
• Actual Study Start Date: July 5, 2022
• Estimated Primary Completion Date: January 31, 2026
• Estimated Study Completion Date: January 31, 2026

Groups and Cohorts

Group/Cohort	Intervention/treatment
Patients with sickle cell anemia; Prospective longitudinal study of patients with sickle cell anemia	Other: 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).

Outcome Measures

Primary Outcome Measures :

1. 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 ]
   At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.

Secondary Outcome Measures :

1. Alternate definitions of CKD progression as eGFR decline <90 mL/min/1·73 m2 and ≥50% drop in eGFR from baseline, and rapid eGFR decline as eGFR loss >5·0 mL/min/1·73 m2 per year will be evaluated. [ Time Frame: 12 months ]
   At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.
2. Evaluate the effect of APOL1 on the predictive capacity of ML models. Genomic DNA will be extracted from whole blood collected at baseline visits using standard techniques and genotyping will be performed as previously described. [ Time Frame: 12 months ]
   At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits

Biospecimen Retention:   Samples With DNA

Routine laboratory tests (CBC and chemistries), cystatin C, pregnancy tests (if female and of child-bearing capacity), urinalysis, spot urine albumin-creatinine ratio, and select plasma and urine biomarkers (ET-1, VEGF and soluble VCAM-1) and kidney function (urinary nephrin, KIM-1) and genomic DNA analyses for APOL1 G1/G2 alleles.

Eligibility Criteria

• Ages Eligible for Study: 18 Years to 65 Years (Adult, Older Adult)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No
• Sampling Method: Non-Probability Sample

Study Population

Four hundred patients with SCD (HbSS or HbSB0 thalassemia) between the ages of 18 and 65 who meet the eligibility criteria and provide consent to participate in the study, will be enrolled in this prospective longitudinal trial.

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.

Contacts and Locations

Contacts

Contact: Kenneth I Ataga, MD; 901-448-2813; kataga@uthsc.edu

Contact: Santosh Saraf, MD; 312-996-5680; ssaraf@uic.edu

Locations

United States, Illinois

University of Illinois at Chicago; Recruiting

Chicago, Illinois, United States, 60612

Contact: Santosh Saraf, MD ssaraf@uic.edu

Principal Investigator: Santosh Saraf, MD

United States, North Carolina

Wake Forest University; Not yet recruiting

Winston-Salem, North Carolina, United States, 27109

Contact: Payal Desai, MD Payal.desai@osumc.edu

Sub-Investigator: Payal Desai, MD

United States, Tennessee

The University of Tennessee Health Science Center; Recruiting

Memphis, Tennessee, United States, 38104

Contact: Kenneth Ataga, MD 901-448-2813 kataga@uthsc.edu

Principal Investigator: Kenneth Ataga, MD

Sub-Investigator: Robert Davis, MD

Sub-Investigator: Laila Elsherif, PhD

Sub-Investigator: Ugochi Ogu, MD

Sub-Investigator: Marquita Nelson, MD

Sponsors and Collaborators

University of Tennessee

National Heart, Lung, and Blood Institute (NHLBI)

University of Illinois at Chicago

University of Memphis

University of North Carolina, Charlotte

Wake Forest University

University of North Carolina, Chapel Hill

Investigators

• Principal Investigator: Kenneth I Ataga, MD; The University of Tennessee Health Science Center

More Information

• Responsible Party: Kenneth Ataga MD, Principal Investigator, University of Tennessee
• ClinicalTrials.gov Identifier: NCT05214105
• Other Study ID Numbers: 2021-0746; 1R01HL159376-01 ( U.S. NIH Grant/Contract )
• First Posted: January 28, 2022
• Last Update Posted: December 14, 2023
• Last Verified: December 2023

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: Yes
• Plan Description: De-identified data will be provided to other academic investigators, upon request, for the purposes of non-commercial research, utilizing institutional Material Transfer Agreement (MTA).
• Supporting Materials: Study Protocol; Clinical Study Report (CSR); Analytic Code
• Time Frame: From time of first patient enrollment to up to 7 years after completion of study.
• Access Criteria: Requests for data from academic investigators will be approved by the Executive Committee of the PREMIER Study. Following approval, de-identified data will be shared in a secure manner.

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Kenneth Ataga MD, University of Tennessee:

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

Additional relevant MeSH terms:

Kidney Diseases; Renal Insufficiency, Chronic; Anemia; Anemia, Sickle Cell; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Hematologic Diseases; Anemia, Hemolytic, Congenital; Anemia, Hemolytic; Hemoglobinopathies; Genetic Diseases, Inborn; Chronic Disease; Disease Attributes; Pathologic Processes; Renal Insufficiency