Sickle Cell DiseasePREMIERNCT05214105
The Predictive Capacity of Machine Learning Models for Progressive Kidney Disease in Individuals With Sickle Cell Anemia
An observational study of Sickle Cell Disease and Kidney Diseases, Chronic, run by University of Tennessee at 3 sites in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 65 years
- Conditions the study is for
- Sickle Cell Disease, Kidney Diseases, Chronic
- What is being tested?
- Biospecimen/DNA collection and analysis
- How many people
- 400 participants
- Where
- 3 sitesUnited States
- Study dates
- 5 Jul 2022to 31 Jan 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Four hundred patients with SCD (HbSS or HbSB0 thalassemia) between the ages of 18 and 65 who meet the eligibility criteria and provide consent to participate in the study, will be enrolled in this prospective longitudinal trial.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
3 sites in 1 country.
United States3 sites, 3 recruiting
- Recruiting
University of Illinois at Chicago
Chicago, Illinois, 60612
Santosh Saraf, MD, ssaraf@uic.edu
Santosh Saraf, MD
- Recruiting
The University of Tennessee Health Science Center
Memphis, Tennessee, 38104
Kenneth Ataga, MD, 901-448-2813, kataga@uthsc.edu
Kenneth Ataga, MD
- Not yet recruiting
Wake Forest University
Winston-Salem, North Carolina, 27109
Payal Desai, MD, Payal.desai@osumc.edu
Payal Desai, MD
About this study
Official title: Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models [PREMIER]
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.
Read the 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.
- Conditions
- Sickle Cell Disease, Kidney Diseases, Chronic
- Being tested
- Biospecimen/DNA collection and analysis
- Keywords
- Machine Learning Models, Sickle Cell Disease, Chronic Kidney Disease, eGFR, Anemia, Sickle Cell, Albuminuria, Renal Insufficiency, Chronic, Renal Insufficiency, APOL1
- Medical subject headings
- Anemia, Sickle Cell, Renal Insufficiency, Chronic, Albuminuria, Renal Insufficiency
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Biospecimen/DNA collection and analysisOther
Patients will be followed longitudinally with collection of CBC and chemistries as well as research biomarkers (urine, plasma, and genomic materials).
| Study group | Role | What they receive |
|---|---|---|
| Patients with sickle cell anemia | Prospective longitudinal study of patients with sickle cell anemia Receives: Other: Biospecimen/DNA collection and analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.
Measured: 12 months
Secondary outcomes (2)
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.
At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.
Measured: 12 months
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.
At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jan 2022
- Study start5 Jul 2022
- Primary completion31 Jan 2026 (estimated)
- Study completion31 Jan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Sickle Cell DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Tennessee
- Sponsor type
- University, hospital or other
- Collaborators
- National Heart, Lung, and Blood Institute (NHLBI), University of Illinois Chicago, University of Memphis, University of North Carolina, Charlotte, Wake Forest University, University of North Carolina, Chapel Hill
- Responsible party
- Principal investigator, Kenneth Ataga MD, University of Tennessee
- Investigators
- Kenneth I Ataga, MD, Principal investigator, The University of Tennessee Health Science Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05214105
- Sponsor's protocol ID
- 2021-0746
- Other IDs
- 1R01HL159376-01
- Acronym
- PREMIER
- Status last verified
- Dec 2023
- First submitted
- 17 Dec 2021
- Last updated
- 14 Dec 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified data will be provided to other academic investigators, upon request, for the purposes of non-commercial research, utilizing institutional Material Transfer Agreement (MTA).
Study at a glance
From the registry record, last updated by the sponsor on 14 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models [PREMIER]
- ClinicalTrials.gov ID
- NCT05214105
- Acronym / sponsor ID
- PREMIER / 2021-0746
- Conditions
- Sickle Cell Disease, Kidney Diseases, Chronic
- Keywords
- Machine Learning Models, Sickle Cell Disease, Chronic Kidney Disease, eGFR, Anemia, Sickle Cell, Albuminuria, Renal Insufficiency, Chronic, Renal Insufficiency
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- University of TennesseeUniversity, hospital or other
- Collaborators
- National Heart, Lung, and Blood Institute (NHLBI), University of Illinois Chicago, University of Memphis, University of North Carolina, Charlotte, Wake Forest University, University of North Carolina, Chapel Hill
- Study start
- 5 Jul 2022 (actual)
- Primary completion
- 31 Jan 2026 (estimated)
- Study completion
- 31 Jan 2026 (estimated)
- First posted
- 28 Jan 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with sickle cell anemia | – | Other: Biospecimen/DNA collection and analysisProspective longitudinal study of patients with sickle cell anemia |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Four hundred patients with SCD (HbSS or HbSB0 thalassemia) between the ages of 18 and 65 who meet the eligibility criteria and provide consent to participate in the study, will be enrolled in this prospective longitudinal trial.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (7 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Biospecimen/DNA collection and analysisPatients will be followed longitudinally with collection of CBC and chemistries as well as research biomarkers (urine, plasma, and genomic materials). | Other | Patients with sickle cell anemia |
Primary outcome
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.
2 secondary outcomes
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
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
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Dec 2023.
- Individual participant dataSharing planned: Yes. De-identified data will be provided to other academic investigators, upon request, for the purposes of non-commercial research, utilizing institutional Material Transfer Agreement (MTA).
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Kenneth I Ataga, MD principal investigator
The University of Tennessee Health Science Center
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sickle Cell Disease and Kidney Diseases, Chronic, run by University of Tennessee at 3 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 14 Dec 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Kenneth I Ataga, MD (901-448-2813, kataga@uthsc.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Dec 2023.