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

Tracking Information

• First Submitted Date: December 17, 2021
• First Posted Date: January 28, 2022
• Last Update Posted Date: December 14, 2023
• Actual Study Start Date: July 5, 2022
• Estimated Primary Completion Date: January 31, 2026 (Final data collection date for primary outcome measure)

Current Primary Outcome Measures (submitted: January 27, 2022)

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.

• Original Primary Outcome Measures: Same as current

Current Secondary Outcome Measures (submitted: January 27, 2022)

• 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.
• 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

• Original Secondary Outcome Measures: Same as current
• Current Other Pre-specified Outcome Measures: Not Provided
• Original Other Pre-specified Outcome Measures: Not Provided

Descriptive Information

• Brief Title: The Predictive Capacity of Machine Learning Models for Progressive Kidney Disease in Individuals With Sickle Cell Anemia
• Official Title: Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models [PREMIER]
• 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.

• Study Type: Observational
• Study Design: Observational Model: Cohort; Time Perspective: Prospective
• Target Follow-Up Duration: Not Provided

Biospecimen

Retention:   Samples With DNA

Description:

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.

• 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.

Condition

• Sickle Cell Disease
• Kidney Diseases, Chronic

Intervention

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).

Study Groups/Cohorts

Patients with sickle cell anemia

Prospective longitudinal study of patients with sickle cell anemia

Intervention: Other: Biospecimen/DNA collection and analysis

• Publications *: Not Provided

Recruitment Information

• Recruitment Status: Recruiting
• Estimated Enrollment (submitted: January 27, 2022): 400
• Original Estimated Enrollment: Same as current
• Estimated Study Completion Date: January 31, 2026
• Estimated Primary Completion Date: January 31, 2026 (Final data collection date for primary outcome measure)

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.

Sex/Gender

• Sexes Eligible for Study: All

• Ages: 18 Years to 65 Years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Contacts

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

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

• Listed Location Countries: United States

Administrative Information

• NCT Number: NCT05214105
• Other Study ID Numbers: 2021-0746; 1R01HL159376-01 ( U.S. NIH Grant/Contract )
• Has Data Monitoring Committee: No

U.S. FDA-regulated Product

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

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
• Supporting Materials: Clinical Study Report (CSR)
• Supporting Materials: 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.

• Current Responsible Party: Kenneth Ataga MD, University of Tennessee
• Original Responsible Party: Same as current
• Current Study Sponsor: University of Tennessee
• Original Study Sponsor: Same as current

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

Investigators

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

• PRS Account: University of Tennessee
• Verification Date: December 2023