Predicting progression of chronic kidney disease in sickle cell anemia using machine learning models (premier)

Ended Project Grant Awarded Last action

Award ID
R01HL159376
Recipient
Awarding agency
Obligated
$5,954,605

Award facts

Assistance listing
93.837 Cardiovascular Diseases Research
Completion date
Funding
  • $5,249,318.00 Federal
  • $705,287.00 Non-federal
Place of performance
Memphis, TN 38163, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Sickle cell disease (SCD) is characterized by a vasculopathy affecting multiple end organs, with complications including chronic kidney disease (CKD). Albuminuria, an early measure of glomerular injury, 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 in SCD is 3-fold higher than in the general population. Furthermore, high-risk APOL1 variants are associated with an increased risk of albuminuria and progression of CKD in SCD. Kidney disease, regardless of severity, and rapid egfr decline are associated with increased mortality in SCD. As such, early identification of patients at risk for progression of CKD is important to address potentially modifiable risk factors, slow egfr decline and reduce mortality. Despite the high prevalence of CKD and its contribution to increased morbidity and mortality, available treatments for SCD-related kidney disease remain limited. Although angiotensin converting enzyme inhibitors (ACE-i), angiotensin receptor blockers (arbs), and hydroxyurea decrease albuminuria in short-term studies, their benefits in preventing or slowing progressive loss of kidney function in SCD remain undefined. We have recently reported that machine learning (ML) models can identify patients at high risk for rapid decline in kidney function. Further, higher hemoglobin concentration is also an independent predictor of decreased odds of rapid kidney function decline. With the contribution of intravascular hemolysis to the pathophysiology of SCD-related glomerulopathy, voxelotor, a small molecule which modifies sickle hemoglobin oxygen affinity and improves sickle RBC survival, may decrease glomerular injury and slow the progression of CKD in individuals with SCD. In this application, we propose the conduct of a prospective, multicenter study to build a ML-based predictive model for progression of CKD in adults with SCD. Furthermore, in individuals predicted to be at risk for rapid decline in kidney function, based on the presence of persistent albuminuria (urine ACR = 100 MG/g), we will evaluate the effect of voxelotor on albuminuria, rapid decline in kidney function and progression of CKD. With advances in the understanding of the pathophysiology of SCD and its complications, combined with an increasing number of approved drug therapies, early identification of patients at risk for progressive kidney disease and subsequent increased risk of death is necessary to modify known risk factors, initiate targeted therapies and possibly increase life expectancy. Further, with the known contribution of hemolytic anemia to the pathogenesis of SCD-related glomerulopathy and progressive kidney disease, drugs that decrease hemolysis are likely to be beneficial in preventing and/or slowing the progression of kidney disease in this patient population.

Transactions

Action Federal Non-federal Date
Amendment $610,088 $0
Amendment $606,572 $0
Amendment $626,209 $0
Amendment $645,294 $0
Amendment $645,294 $0
Amendment $705,287 $0
Amendment $705,287 $0
Original award $705,287 $705,287

Subawards

Subrecipient Amount Awarded
University Of North Carolina At Charlotte Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models (PREMIER) $26,348
Wake Forest University Health Sciences $25,585
University Of North Carolina At Chapel Hill $21,017
University Of Illinois $160,320
The University Of Memphis $25,728
University Of Illinois $154,687
The Charlotte-Mecklenburg Hospital Authority $78,425
University Of Memphis In the proposed project, the study team will conduct a prospective observational study to evaluate the predictive capacity of machine learning models for… $20,095
Wake Forest University Health Sciences Dr. Akbilgic is an Associate Professor in the Section of Cardiovascular Medicine, as well as Associate Professor in the Biomedical Informatics Center at Wake… $19,954
The Charlotte-Mecklenburg Hospital Authority For this work, Atrium Health Levine Cancer Institute (LCI) Clinical Trials Office will perform the following specific tasks: 1. Establish and maintain IRB for… $81,890
University Of Memphis In the proposed project, the study team will conduct a prospective observational study to evaluate the predictive capacity of machine learning models for… $22,930
University Of North Carolina At Chapel Hill Vimal Derebail, MD, a faculty member of the University of North Carolina at Chapel Hill, will serve as a Co-Investigator for this study. He is a nephrologist… $15,384
University Of North Carolina At Chapel Hill $15,899
University Of North Carolina At Charlotte Sickle cell disease is characterized by multiple complications, including progressive kidney disease. Kidney disease is common in these patients and is… $20,990
University Of Illinois SCOPE OF WORK UIC will be a participating site for this study developing machine learning models to identify patients with sickle cell disease at high risk… $155,560
Wake Forest University Health Sciences Dr. Akbilgic has already been in close collaboration with the PI and the other study team members in developing preliminary results in prediction rapid… $20,021
Wake Forest University Health Sciences Scope of Work Oguz Akbilgic, PhD Dr. Akbilgic is an Associate Professor in the Section of Cardiovascular Medicine, as well as Associate Professor in the… $20,020
The University Of Memphis Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models (PREMIER) $34,356
University Of North Carolina At Chapel Hill Vimal Derebail, MD, a faculty member of the University of North Carolina at Chapel Hill, will serve as a Co-Investigator for this study. He is a nephrologist… $23,916
University Of Illinois UIC will be a participating site for this study developing machine learning models to identify patients with sickle cell disease at high risk for chronic kidney… -$7,989
Show all 20

Quick answers

Who received award R01HL159376?

R01HL159376 was awarded to University Of Tennessee by National Heart Lung and Blood Institute (National Institutes of Health) on September 10, 2021.

How much is award R01HL159376 worth?

$5,954,605 has been obligated on R01HL159376: $5,249,318 in federal funding and $705,287 in non-federal funding.

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