Ignite Creation Date:
2025-12-25 @ 3:24 AM
Ignite Modification Date:
2026-01-01 @ 12:14 AM
Study NCT ID:
NCT05214105
Status:
RECRUITING
Last Update Posted:
2023-12-14
First Post:
2021-12-17
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
The Predictive Capacity of Machine Learning Models for Progressive Kidney Disease in Individuals With Sickle Cell Anemia
Sponsor:
University of Tennessee
Organization:
Study Overview
Official Title:
Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models [PREMIER]
Status:
RECRUITING
Status Verified Date:
2023-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PREMIER
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.
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 Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
Secondary ID Infos
| Secondary ID | Type | Domain | Link | View |
|---|---|---|---|---|
| 1R01HL159376-01 | NIH | None | https://reporter.nih.gov/quic… | View |