Clinical trial NCT05214105: 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]
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Sickle Cell Disease
Kidney Diseases, Chronic
Keywords: Machine Learning Models
Sickle Cell Disease
Chronic Kidney Disease
eGFR
Anemia, Sickle Cell
Albuminuria
Renal Insufficiency, Chronic
Renal Insufficiency
APOL1
Lead sponsor: University of Tennessee
Sponsor class: OTHER
Collaborators: name: National Heart, Lung, and Blood Institute (NHLBI)
name: University of Illinois at Chicago
name: University of Memphis
name: University of North Carolina, Charlotte
name: Wake Forest University
name: University of North Carolina, Chapel Hill
First posted: 2022-01-28
Enrollment: 400
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

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.

Primary outcomes:
measure: 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.
timeFrame: 12 months
Secondary outcomes:
measure: 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.
timeFrame: 12 months
measure: 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.
timeFrame: 12 months
Other outcomes:
Locations:
city: Chicago
country: United States
facility: University of Illinois at Chicago
state: Illinois
city: Winston-Salem
country: United States
facility: Wake Forest University
state: North Carolina
city: Memphis
country: United States
facility: The University of Tennessee Health Science Center
state: Tennessee
Study officials:
affiliation: The University of Tennessee Health Science Center
name: Kenneth I Ataga, MD
role: PRINCIPAL_INVESTIGATOR
References: