Ignite Creation Date:
2025-12-24 @ 3:00 PM
Ignite Modification Date:
2026-03-03 @ 6:57 PM
Study NCT ID:
NCT06728059
Status:
RECRUITING
Last Update Posted:
2025-05-15
First Post:
2024-11-14
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003922', 'term': 'Diabetes Mellitus, Type 1'}], 'ancestors': [{'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D001327', 'term': 'Autoimmune Diseases'}, {'id': 'D007154', 'term': 'Immune System Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'TREATMENT', 'interventionModel': 'CROSSOVER', 'interventionModelDescription': 'This is a research study about the UVA Automated Insulin Delivery System known as Adaptive NETwork (AIDANET). This system consists of a Reinforcement Learning trained Bolus Priming System (BPS\\_RL) added to the AIDANET algorithm and running on Diabetes Assistant (DiAs) phone wirelessly connected to Tandem t:AP insulin pump and Dexcom Continuous Glucose Monitor (CGM). One part of the algorithm, called the Bolus Priming System (BPS), gives insulin automatically to help keep blood sugar levels in a healthy range.\n\nIn this study, the Bolus Priming System is being tested in a new way. This system uses a type of smart learning called reinforcement learning (RL), which helps the algorithm make better choices about how much insulin to give. The new version of the system looks at blood sugar and insulin levels over the past 3 days to find patterns and give a better insulin dose before meals. This should provide an improvement over the old system, which only uses the last 30 minutes of data.'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 16}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-02-05', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-05', 'completionDateStruct': {'date': '2025-07-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-05-12', 'studyFirstSubmitDate': '2024-11-14', 'studyFirstSubmitQcDate': '2024-12-06', 'lastUpdatePostDateStruct': {'date': '2025-05-15', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-12-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-07-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'CGM-measured time in range (TIR, 70-180 mg/dL) during the 18-hour hotel sessions on AIDANET or AIDANET+BPS_RL.', 'timeFrame': '36 hours total (18 hours for Group A and 18 hours for Group B)', 'description': 'The time periods begin at 6 PM and end at noon on the next day, thereby covering two meals - dinner and breakfast.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isUnapprovedDevice': True, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'keywords': ['Type 1 Diabetes', 'Automated insulin delivery as Adaptive Network (AIDANET)', 'Diabetes Assistant (DiAs)', 'Fully Closed-Loop', 'Bolus Priming System (BPS)', 'Remote Learning (RL)', 'Continuous Glucose Monitor (CGM)'], 'conditions': ['Type 1 Diabetes']}, 'descriptionModule': {'briefSummary': 'A randomized crossover trial assessing glycemic control using Reinforcement Learning trained Bolus Priming System (BPS\\_RL) added to the the Automated Insulin Delivery as Adaptive NETwork (AIDANET algorithm) compared to the original AIDANET algorithm.', 'detailedDescription': 'After receiving training on the study equipment, participants will use the AIDANET system at home for 7 days/6 nights to establish a baseline and initialize the control algorithm. Participants will then be studied at a hotel session for 3 days/2 nights. Participants will transition to home use of AIDANET+ BPS\\_RL for 7 days/6 nights.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age ≥18.0 years old at time of consent\n2. Clinical diagnosis, based on investigator assessment, of Type 1 Diabetes for at least one year.\n3. Having used an AID system equipped with Dexcom G6 or G7 CGM within the last three months (does not need to be continuous use if CGM was unavailable for instance).\n4. Currently using insulin for at least six months.\n5. Willingness to switch to use a commercially approved personal insulin (e.g., lispro or aspart, or biosimilar approved products) within the study pump as directed by the study team.\n6. Has one or more supportive companions knowledgeable about emergency procedures for severe hypoglycemia and able to contact emergency services and study staff that either lives with participant or located within approximately 30 minutes of participant and able to locate participant in the event of an emergency.\n7. Participant not currently known to be pregnant or breastfeeding.\n8. If participant capable of becoming pregnant, must agree to use a form of contraception to prevent pregnancy while a participant in the study (e.g. hormonal contraception, abstinence from heterosexual intercourse). A negative serum or urine pregnancy test will be required for all females of childbearing potential. Participants who become pregnant will be discontinued from the study. Also, participants who during the study develop and express the intention to become pregnant within the timespan of the study will be discontinued.\n9. Willingness to use the study AIDANET system (CGM, pump, and phone) during the study period.\n10. Willingness not to start any new non-insulin glucose-lowering agent during the course of the trial.\n11. Willingness to participate in all study procedures including the house/hotel sessions.\n12. Access to internet at home and willingness to upload data during the study as needed.\n13. Investigator has confidence that the participant can successfully operate all study devices and is capable of adhering to the protocol.\n14. Participant is proficient in reading and writing English.\n\nExclusion Criteria:\n\n1. Plans to start a new non-insulin glucose-lowering agent (e.g., GLP-1 receptor agonists, Symlin, DPP-4 inhibitors, sulfonylureas). Participants may be on a stable dose of such an agent for at least the past month.\n2. Current use of an SGLT-2 or SGLT-1/2 inhibitor due to risk of euglycemic DKA.\n3. Hemophilia or any other bleeding disorder.\n4. History of severe hypoglycemic events with seizure or loss of consciousness in the last 12 months.\n5. History of DKA event in the last 12 months.\n6. Stage 4 chronic renal disease or currently on peritoneal or hemodialysis.\n7. Currently being treated for adrenal insufficiency.\n8. Currently being treated for a seizure disorder.\n9. Hypothyroidism or hyperthyroidism that is not adequately treated.\n10. Use of oral or injectable steroids at the time of enrollment or within the last 4 weeks.\n11. Planned surgery during the study period.\n12. Known ongoing adhesive intolerance that is not well managed.\n13. A condition, which in the opinion of the investigator or designee, would put the participant or study at risk.\n14. Participation in another interventional trial at the time of enrollment.\n15. Participant with a direct supervisor involved in the conduct of the trial.'}, 'identificationModule': {'nctId': 'NCT06728059', 'acronym': 'AIDANET+BPS_RL', 'briefTitle': 'Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm', 'organization': {'class': 'OTHER', 'fullName': 'University of Virginia'}, 'officialTitle': 'Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm', 'orgStudyIdInfo': {'id': '301989'}, 'secondaryIdInfos': [{'id': 'R01DK133148', 'link': 'https://reporter.nih.gov/quickSearch/R01DK133148', 'type': 'NIH'}]}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': 'AIDANET→AIDANET+ BPS_RL', 'description': 'Group A: AIDANET followed by AIDANET+ BPS\\_RL during the hotel session', 'interventionNames': ['Device: Automated Insulin Delivery Adaptive NETwork (AIDANET)']}, {'type': 'ACTIVE_COMPARATOR', 'label': 'AIDANET+ BPS_RL→AIDANET', 'description': 'Group B: AIDANET+BPS\\_RL followed by AIDANET during the hotel session', 'interventionNames': ['Device: AIDANET+ BPS_RL→AIDANET']}], 'interventions': [{'name': 'Automated Insulin Delivery Adaptive NETwork (AIDANET)', 'type': 'DEVICE', 'otherNames': ['AIDANET→AIDANET+ BPS_RL'], 'description': 'Group A participants will use the AIDANET system at home for 7 days/6 nights. They will continue use of AIDANET system for 18 hours during the hotel session and then use AIDANET+BPS\\_RL for 18 hours during the hotel session.', 'armGroupLabels': ['AIDANET→AIDANET+ BPS_RL']}, {'name': 'AIDANET+ BPS_RL→AIDANET', 'type': 'DEVICE', 'otherNames': ['Group B'], 'description': 'Group B participant will use the AIDANET+BPS\\_RL system for 18 hours during the hotel session and will then use AIDANET system for 18 hours during the hotel session. They will continue to use AIDANET+BPS\\_RL system at home for 7 days/6 night and then use the AIDANET system at home for 7 days/6 nights.', 'armGroupLabels': ['AIDANET+ BPS_RL→AIDANET']}]}, 'contactsLocationsModule': {'locations': [{'zip': '22903', 'city': 'Charlottesville', 'state': 'Virginia', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Sue Brown, MD', 'role': 'CONTACT', 'email': 'sab2f@virginia.edu', 'phone': '434-982-0602'}, {'name': 'Boris P. Kovatchev, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Anas El Fathi, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Mark D DeBoer, MD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'University of Virginia Center for Diabetes Technology', 'geoPoint': {'lat': 38.02931, 'lon': -78.47668}}], 'centralContacts': [{'name': 'Sara Prince, RN', 'role': 'CONTACT', 'email': 'SP4SA@uvahealth.org', 'phone': '(434) 320-5599'}, {'name': 'Carlene Alix', 'role': 'CONTACT', 'email': 'uax8yx@uvahealth.org', 'phone': '434-249-8961'}], 'overallOfficials': [{'name': 'Sue Brown, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University of Virginia'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ICF'], 'timeFrame': 'Data will be made available after the publication of the manuscript.', 'ipdSharing': 'YES', 'description': 'Will follow the NIH Data Sharing Policy and Implementation Guidance on sharing research resources for research purposes to qualified individuals in the scientific community.', 'accessCriteria': 'The Data Sharing Agreements will be formulated by the study team'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sue Brown', 'class': 'OTHER'}, 'collaborators': [{'name': 'National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)', 'class': 'NIH'}, {'name': 'DexCom, Inc.', 'class': 'INDUSTRY'}], 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Sue Brown', 'investigatorAffiliation': 'University of Virginia'}}}}