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

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

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 Overview

• Status: Recruiting
• Conditions: Sickle Cell Disease; Kidney Diseases, Chronic
• Intervention / Treatment: 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 Type: Observational
• Enrollment (Estimated): 400

Contacts and Locations

• Study Contact: Name: Kenneth I Ataga, MD; Phone Number: 901-448-2813; Email: kataga@uthsc.edu
• Study Contact Backup: Name: Santosh Saraf, MD; Phone Number: 312-996-5680; Email: ssaraf@uic.edu

Study Locations

• United States
  • Illinois
    • Chicago, Illinois, United States, 60612
      • Recruiting
      • University of Illinois at Chicago
      • Principal Investigator: Santosh Saraf, MD
      • Contact: Santosh Saraf, MD; Email: ssaraf@uic.edu
  • North Carolina
    • Winston-Salem, North Carolina, United States, 27109
      • Not yet recruiting
      • Wake Forest University
      • Contact: Payal Desai, MD; Email: Payal.desai@osumc.edu
      • Sub-Investigator: Payal Desai, MD
  • Tennessee
    • Memphis, Tennessee, United States, 38104
      • Recruiting
      • The University Of Tennessee Health Science Center
      • Contact: Kenneth Ataga, MD; Phone Number: 901-448-2813; Email: 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

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 65 years (Adult, Older Adult)
• 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.

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

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).

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.	12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.	12 months
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.	At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits	12 months

Collaborators and Investigators

• Sponsor: University of Tennessee
• Collaborators: National Heart, Lung, and Blood Institute (NHLBI); University of Illinois at Chicago; University of North Carolina, Chapel Hill; University of Memphis; Wake Forest University; University of North Carolina, Charlotte
• Investigators: Principal Investigator: Kenneth I Ataga, MD, The University Of Tennessee Health Science Center

Study record dates

Study Major Dates

• Study Start (Actual): July 5, 2022
• Primary Completion (Estimated): January 31, 2026
• Study Completion (Estimated): January 31, 2026

Study Registration Dates

• First Submitted: December 17, 2021
• First Submitted That Met QC Criteria: January 27, 2022
• First Posted (Actual): January 28, 2022

Study Record Updates

• Last Update Posted (Estimated): December 14, 2023
• Last Update Submitted That Met QC Criteria: December 13, 2023
• Last Verified: December 1, 2023

More Information

Terms related to this study

• Keywords: Chronic Kidney Disease; Renal Insufficiency, Chronic; Renal Insufficiency; Sickle Cell Disease; Albuminuria; eGFR; Anemia, Sickle Cell; Machine Learning Models; APOL1
• Additional Relevant MeSH Terms: Pathologic Processes; Urologic Diseases; Disease Attributes; Hematologic Diseases; Renal Insufficiency; Genetic Diseases, Inborn; Anemia, Hemolytic, Congenital; Anemia, Hemolytic; Hemoglobinopathies; Chronic Disease; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Kidney Diseases; Renal Insufficiency, Chronic; Anemia; Anemia, Sickle Cell
• Other Study ID Numbers: 2021-0746; 1R01HL159376-01 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD 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).
• IPD Sharing Time Frame: From time of first patient enrollment to up to 7 years after completion of study.
• IPD Sharing 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.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No