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

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

Primary Outcome

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.

Secondary Outcome

 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.

Condition

Sickle Cell Disease

Intervention

Biospecimen/DNA collection and analysis

Study Arms / Comparison Groups

 Patients with sickle cell anemia

Description:  Prospective longitudinal study of patients with sickle cell anemia

Publications

* Includes publications given by the data provider as well as publications identified by National Clinical Trials Identifier (NCT ID) in Medline.

Recruitment Information

Recruitment Status

Other

Estimated Enrollment

Start Date

June 2, 2022

Completion Date

January 31, 2026

Primary Completion Date

January 31, 2026

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.

Gender

All

Ages

18 Years - 65 Years

Accepts Healthy Volunteers

No

Contacts

Kenneth I Ataga, MD, 901-448-2813, [email protected]

Location Countries

United States

Location Countries

United States

Administrative Informations

NCT ID

NCT05214105

Organization ID

2021-0746

Secondary IDs

1R01HL159376-01

Responsible Party

Principal Investigator

Study Sponsor

University of Tennessee

Collaborators

 National Heart, Lung, and Blood Institute (NHLBI)

Study Sponsor

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

Verification Date

May 2022