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

NCT05214105 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2023-12-14

No results posted yet for this study

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.

Conditions

Interventions

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).

Sponsors & Collaborators

  • National Heart, Lung, and Blood Institute (NHLBI)

    collaborator NIH

  • University of Illinois at Chicago

    collaborator OTHER

  • University of Memphis

    collaborator OTHER

  • University of North Carolina, Charlotte

    collaborator OTHER

  • Wake Forest University

    collaborator OTHER

  • University of North Carolina, Chapel Hill

    collaborator OTHER

  • University of Tennessee

    lead OTHER

Principal Investigators

  • Kenneth I Ataga, MD · The University of Tennessee Health Science Center

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-07-05
Primary Completion
2026-01-31
Completion
2026-01-31

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05214105 on ClinicalTrials.gov