The Predictive Capacity of Machine Learning Models for Progressive Kidney Disease in Individuals With Sickle Cell Anemia (PREMIER)
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|ClinicalTrials.gov Identifier: NCT05214105|
Recruitment Status : Recruiting
First Posted : January 28, 2022
Last Update Posted : July 18, 2022
|Condition or disease||Intervention/treatment|
|Sickle Cell Disease Kidney Diseases, Chronic||Other: Biospecimen/DNA collection and analysis|
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|
|Estimated Enrollment :||400 participants|
|Official Title:||Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models [PREMIER]|
|Actual Study Start Date :||July 5, 2022|
|Estimated Primary Completion Date :||January 31, 2026|
|Estimated Study Completion Date :||January 31, 2026|
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).
- 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. [ Time Frame: 12 months ]At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.
- 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. [ Time Frame: 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. [ Time Frame: 12 months ]At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits
Biospecimen Retention: Samples With DNA
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05214105
|Contact: Kenneth I Ataga, MDfirstname.lastname@example.org|
|Contact: Santosh Saraf, MDemail@example.com|
|United States, Illinois|
|University of Illinois at Chicago||Recruiting|
|Chicago, Illinois, United States, 60612|
|Contact: Santosh Saraf, MD firstname.lastname@example.org|
|Principal Investigator: Santosh Saraf, MD|
|United States, North Carolina|
|Wake Forest University||Not yet recruiting|
|Winston-Salem, North Carolina, United States, 27109|
|Contact: Payal Desai, MD Payal.email@example.com|
|Sub-Investigator: Payal Desai, MD|
|United States, Tennessee|
|The University of Tennessee Health Science Center||Recruiting|
|Memphis, Tennessee, United States, 38104|
|Contact: Kenneth Ataga, MD 901-448-2813 firstname.lastname@example.org|
|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|
|Principal Investigator:||Kenneth I Ataga, MD||The University of Tennessee Health Science Center|