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:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000755', 'term': 'Anemia, Sickle Cell'}, {'id': 'D051436', 'term': 'Renal Insufficiency, Chronic'}, {'id': 'D000419', 'term': 'Albuminuria'}, {'id': 'D051437', 'term': 'Renal Insufficiency'}], 'ancestors': [{'id': 'D000745', 'term': 'Anemia, Hemolytic, Congenital'}, {'id': 'D000743', 'term': 'Anemia, Hemolytic'}, {'id': 'D000740', 'term': 'Anemia'}, {'id': 'D006402', 'term': 'Hematologic Diseases'}, {'id': 'D006425', 'term': 'Hemic and Lymphatic Diseases'}, {'id': 'D006453', 'term': 'Hemoglobinopathies'}, {'id': 'D030342', 'term': 'Genetic Diseases, Inborn'}, {'id': 'D009358', 'term': 'Congenital, Hereditary, and Neonatal Diseases and Abnormalities'}, {'id': 'D007674', 'term': 'Kidney Diseases'}, {'id': 'D014570', 'term': 'Urologic Diseases'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}, {'id': 'D002908', 'term': 'Chronic Disease'}, {'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D011507', 'term': 'Proteinuria'}, {'id': 'D014555', 'term': 'Urination Disorders'}, {'id': 'D020924', 'term': 'Urological Manifestations'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Routine laboratory tests (CBC and chemistries), cystatin C, pregnancy tests (if female and of child-bearing capacity), urinalysis, spot urine albumin-creatinine ratio, and select plasma and urine biomarkers (ET-1, VEGF and soluble VCAM-1) and kidney function (urinary nephrin, KIM-1) and genomic DNA analyses for APOL1 G1/G2 alleles.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 400}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2022-07-05', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-12', 'completionDateStruct': {'date': '2026-01-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-12-13', 'studyFirstSubmitDate': '2021-12-17', 'studyFirstSubmitQcDate': '2022-01-27', 'lastUpdatePostDateStruct': {'date': '2023-12-14', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-01-28', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-01-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'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.', 'timeFrame': '12 months', 'description': 'At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.'}], 'secondaryOutcomes': [{'measure': '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.', 'timeFrame': '12 months', 'description': 'At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits.'}, {'measure': '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.', 'timeFrame': '12 months', 'description': 'At each visit following the first 12 months, rate of eGFR change will be calculated using data from current and earlier visits'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Machine Learning Models', 'Sickle Cell Disease', 'Chronic Kidney Disease', 'eGFR', 'Anemia, Sickle Cell', 'Albuminuria', 'Renal Insufficiency, Chronic', 'Renal Insufficiency', 'APOL1'], 'conditions': ['Sickle Cell Disease', 'Kidney Diseases, Chronic']}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.\n\nThe 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.\n\nThe 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '65 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. HbSS or HbSβ0 thalassemia, 18 - 65 years old;\n2. non-crisis, "steady state" with no acute pain episodes requiring medical contact in preceding 4 weeks;\n3. ability to understand the study requirements.\n\nExclusion Criteria:\n\n1. pregnant at enrollment;\n2. poorly controlled hypertension;\n3. long-standing diabetes with suspicion for diabetic nephropathy;\n4. connective tissue disease such as systemic lupus erythematosus (SLE);\n5. polycystic kidney disease or glomerular disease unrelated to SCD;\n6. stem cell transplantation;\n7. 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.'}, 'identificationModule': {'nctId': 'NCT05214105', 'acronym': 'PREMIER', 'briefTitle': 'The Predictive Capacity of Machine Learning Models for Progressive Kidney Disease in Individuals With Sickle Cell Anemia', 'organization': {'class': 'OTHER', 'fullName': 'University of Tennessee'}, 'officialTitle': 'Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models [PREMIER]', 'orgStudyIdInfo': {'id': '2021-0746'}, 'secondaryIdInfos': [{'id': '1R01HL159376-01', 'link': 'https://reporter.nih.gov/quickSearch/1R01HL159376-01', 'type': 'NIH'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Patients with sickle cell anemia', 'description': 'Prospective longitudinal study of patients with sickle cell anemia', 'interventionNames': ['Other: Biospecimen/DNA collection and analysis']}], 'interventions': [{'name': 'Biospecimen/DNA collection and analysis', 'type': 'OTHER', 'description': 'Patients will be followed longitudinally with collection of CBC and chemistries as well as research biomarkers (urine, plasma, and genomic materials).', 'armGroupLabels': ['Patients with sickle cell anemia']}]}, 'contactsLocationsModule': {'locations': [{'zip': '60612', 'city': 'Chicago', 'state': 'Illinois', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Santosh Saraf, MD', 'role': 'CONTACT', 'email': 'ssaraf@uic.edu'}, {'name': 'Santosh Saraf, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'University of Illinois at Chicago', 'geoPoint': {'lat': 41.85003, 'lon': -87.65005}}, {'zip': '27109', 'city': 'Winston-Salem', 'state': 'North Carolina', 'status': 'NOT_YET_RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Payal Desai, MD', 'role': 'CONTACT', 'email': 'Payal.desai@osumc.edu'}, {'name': 'Payal Desai, MD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Wake Forest University', 'geoPoint': {'lat': 36.09986, 'lon': -80.24422}}, {'zip': '38104', 'city': 'Memphis', 'state': 'Tennessee', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Kenneth Ataga, MD', 'role': 'CONTACT', 'email': 'kataga@uthsc.edu', 'phone': '901-448-2813'}, {'name': 'Kenneth Ataga, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Robert Davis, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Laila Elsherif, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Ugochi Ogu, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Marquita Nelson, MD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'The University of Tennessee Health Science Center', 'geoPoint': {'lat': 35.14953, 'lon': -90.04898}}], 'centralContacts': [{'name': 'Kenneth I Ataga, MD', 'role': 'CONTACT', 'email': 'kataga@uthsc.edu', 'phone': '901-448-2813'}, {'name': 'Santosh Saraf, MD', 'role': 'CONTACT', 'email': 'ssaraf@uic.edu', 'phone': '312-996-5680'}], 'overallOfficials': [{'name': 'Kenneth I Ataga, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The University of Tennessee Health Science Center'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'CSR', 'ANALYTIC_CODE'], 'timeFrame': 'From time of first patient enrollment to up to 7 years after completion of study.', 'ipdSharing': 'YES', 'description': '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).', 'accessCriteria': 'Requests for data from academic investigators will be approved by the Executive Committee of the PREMIER Study. Following approval, de-identified data will be shared in a secure manner.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Tennessee', 'class': 'OTHER'}, 'collaborators': [{'name': 'National Heart, Lung, and Blood Institute (NHLBI)', 'class': 'NIH'}, {'name': 'University of Illinois at Chicago', 'class': 'OTHER'}, {'name': 'University of Memphis', 'class': 'OTHER'}, {'name': 'University of North Carolina, Charlotte', 'class': 'OTHER'}, {'name': 'Wake Forest University', 'class': 'OTHER'}, {'name': 'University of North Carolina, Chapel Hill', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Kenneth Ataga MD', 'investigatorAffiliation': 'University of Tennessee'}}}}