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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Sickle Cell Disease, Kidney Diseases, Chronic

Intervention / Treatment
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* Other: Biospecimen/DNA collection and analysis


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: HbSS or HbSβ0 thalassemia, 18 - 65 years old; non-crisis, steady state with no acute pain episodes requiring medical contact in preceding 4 weeks; ability to understand the study requirements. Exclusion Criteria: pregnant at enrollment; poorly controlled hypertension; long-standing diabetes with suspicion for diabetic nephropathy; connective tissue disease such as systemic lupus erythematosus (SLE); polycystic kidney disease or glomerular disease unrelated to SCD; stem cell transplantation; 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.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patients with sickle cell anemia<br>Prospective longitudinal study of patients with sickle cell anemia | Other: Biospecimen/DNA collection and analysis<br>* Patients will be followed longitudinally with collection of CBC and chemistries as well as research biomarkers (urine, plasma, and genomic materials).<br>|

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Machine Learning Models, Sickle Cell Disease, Chronic Kidney Disease, eGFR, Anemia, Sickle Cell, Albuminuria, Renal Insufficiency, Chronic, Renal Insufficiency, APOL1