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

Sponsor

University of Tennessee (Other)

Overall Status

Recruiting

CT.gov ID

NCT05214105

Collaborator

National Heart, Lung, and Blood Institute (NHLBI) (NIH), University of Illinois at Chicago (Other), University of Memphis (Other), University of North Carolina, Charlotte (Other), Wake Forest University (Other), University of North Carolina, Chapel Hill (Other), University of Tennessee Health Science Center (Other)

400

Enrollment

Locations

42.9

Anticipated Duration (Months)

133.3

Patients Per Site

3.1

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Sickle Cell Disease Kidney Diseases, Chronic	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 Design

Study Type:

Observational

Anticipated Enrollment :

400 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Actual Study Start Date :

Jul 5, 2022

Anticipated Primary Completion Date :

Jan 31, 2026

Anticipated Study Completion Date :

Jan 31, 2026

Arms and Interventions

Arm	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).

Arm

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).

Outcome Measures

Primary Outcome Measures

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. [12 months]
At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.

Secondary Outcome Measures

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. [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. [12 months]
At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 65 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

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.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	University of Illinois at Chicago	Chicago	Illinois	United States	60612
2	Wake Forest University	Winston-Salem	North Carolina	United States	27109
3	The University of Tennessee Health Science Center	Memphis	Tennessee	United States	38104