Closed Loop Glucose Control in Patients With Type 2 Diabetes

NCT ID: NCT05386849

Last Updated: 2022-07-20

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

2 participants

Study Classification

INTERVENTIONAL

Study Start Date

2022-05-09

Study Completion Date

2022-07-19

Brief Summary

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This is a proof of concept safety study of an artificial intelligence based closed loop glucose control system designed for use in the intensive care unit setting.

The type 2 diabetic subjects in this study will have their glucose controlled to a range of 100-140 mg/dL by a novel artificial intelligence based closed loop glucose control system for a period of 24 hours. The subjects will consume three standardized meals during the 24 hour study period.

Detailed Description

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Tight glucose control in the intensive care unit (ICU) setting is difficult to achieve. The investigators hypothesize that a closed loop glucose control system based on artificial intelligence (AI) will improve upon the glucose control currently achieved by the current open loop manual methods, and that this improved glucose control may improve the outcomes of critically ill patients, including those with COVID-19.

This Earl Feasibility Study will test the ability of a prototype artificial intelligence based closed loop glucose control system named FUSION, to provide safe and effective glucose control in subjects with type 2 diabetes in a clinical research center (CRC) setting. Subjects with type 2 diabetes have been chosen as they are insulin resistant, which makes their insulin resistance profile similar to that of ICU patients. As this is the first in human study of a new medical device, the controlled environment of the CRC is preferable to the less controlled environment of an ICU setting.

The prototype FUSION system to be used in this study will consist to two Dexcom G6 continuous glucose monitors (CGM), the AI-based glucose control software run on an all-in-one medical computer, and two syringe pumps. The prototype system is housed on a medical cart. Based on the average glucose value of the two Dexcom G6 CGM's, and the rules of the FUSION systems AI-based glucose control software, the FUSION system will make rate adjustments every 5-10 minutes to the intravenous infusion rates of short acting insulin (NovoLog) and dextrose (D10NS) under its control, in an attempt to keep the subjects glucose in the range of 100-140 mg/dL. The FUSION system only requires entry of the subjects study identification number and weight in kilograms to initiate the system.

For safety reasons, the subjects will have their blood glucose independently measured every 10-60 minutes on a YSI 2900 glucose analyzer and the point of care Nova StatStrip system, throughout the 24 hour study period.

The study has halting criteria to avoid recurrent instances of severe hypoglycemia (\< 54 mg/dL).

The average of the two CGM's, that is used by the FUSION system for glucose control, will be used for statistical analysis. In addition, both the average glucose value used by the FUSION system and each individual CGM system will be compared to the YSI glucose analyzer for correlation between systems using the Surveillance Error grid.

Conditions

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Diabetes Mellitus, Type 2

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Early Feasibility Safety Study
Primary Study Purpose

DEVICE_FEASIBILITY

Blinding Strategy

NONE

Study Groups

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FUSION closed loop glucose control system

All subjects will be treated with the FUSION closed loop glucose control system for up to 24 hours

Group Type EXPERIMENTAL

FUSION closed loop glucose control system

Intervention Type DEVICE

The FUSION system will be used to control the subjects glucose to a range of 100-140 mg/dL. Data will be collected for up to 24 hours, or upon early termination of the study session.

Interventions

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FUSION closed loop glucose control system

The FUSION system will be used to control the subjects glucose to a range of 100-140 mg/dL. Data will be collected for up to 24 hours, or upon early termination of the study session.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

1. Are 18-70 years of age, inclusive.
2. Can understand and sign an informed consent, communicate with the investigator, and understand and comply with the protocol requirements.
3. Have had a diagnosis of type 2 diabetes for a period of at least 3 months.
4. Use insulin injections at home for glucose control.
5. Have a hemoglobin A1c (HbA1c) in the range of 7.0 - 10.0%.
6. Have a hemoglobin in the normal range for sex:

1. Females: 12-15.5 grams/dL.
2. Males: 13.5-17.5 grams/dl.
7. Have adequate venous access sites in upper extremities.
8. Body weight between 40 - 150 kg.

Exclusion Criteria

1. Have participated in an interventional medical, surgical, or pharmaceutical study within 30 days of screening.
2. Have a known hypersensitivity to any of the components of study treatment.
3. Have skin disease/injury at Dexcom G6 CGM insertion site(s) that would prevent insertion of the CGM.
4. Currently abuses drugs or alcohol or has a history of abuse that in the investigator's opinion would cause the individual to be noncompliant.
5. Have a medical condition that in the opinion of the investigator could affect study participation and/or personal well-being.
6. Have a clinically significant history or presence of any of the following conditions:

1. Hepatic failure or has alanine aminotransferase (ALT) greater than 3 times the upper limit of normal.
2. Has an estimated GFR \<30 ml/min/1.73 m2 or End Stage Kidney Disease on renal replacement therapy.
3. Have congestive heart failure greater than class 1 on the NYHA classification system.
4. Have a history of seizures.
5. Have a history of cerebrovascular accident.
6. Have a history of ischemic heart disease.
7. For female subjects of potential childbearing age (age 18 to 55) they will be excluded if:

1. Pregnant.
2. Refuse to agree to a pregnancy test at the time of enrollment.
3. Have a positive urine pregnancy test at the time of enrollment.
8. Have a positive COVID-19 test within 14 days of visit 3.
9. Have any COVID-19 related symptoms in the 14-day period prior to visit 3.
10. Have a known unprotected COVID-19 exposure in the 14-day period prior to visit 3.
Minimum Eligible Age

18 Years

Maximum Eligible Age

70 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Emory University

OTHER

Sponsor Role collaborator

Ideal Medical Technologies

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Leon DeJournett, MD

Role: STUDY_DIRECTOR

Ideal Medical Technologies

Francisco Pasquel, MD

Role: PRINCIPAL_INVESTIGATOR

Emory University

Locations

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Emory University

Atlanta, Georgia, United States

Site Status

Countries

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United States

References

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DeJournett L, DeJournett J. In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting. J Diabetes Sci Technol. 2016 Nov 1;10(6):1360-1371. doi: 10.1177/1932296816653967. Print 2016 Nov.

Reference Type BACKGROUND
PMID: 27301982 (View on PubMed)

DeJournett J, DeJournett L. Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric. J Diabetes Sci Technol. 2017 Nov;11(6):1207-1217. doi: 10.1177/1932296817711297. Epub 2017 Jun 22.

Reference Type BACKGROUND
PMID: 28637358 (View on PubMed)

DeJournett J, Nekludov M, DeJournett L, Wallin M. Performance of a closed-loop glucose control system, comprising a continuous glucose monitoring system and an AI-based controller in swine during severe hypo- and hyperglycemic provocations. J Clin Monit Comput. 2021 Apr;35(2):317-325. doi: 10.1007/s10877-020-00474-2. Epub 2020 Jan 31.

Reference Type BACKGROUND
PMID: 32006145 (View on PubMed)

DeJournett L. Essential elements of the native glucoregulatory system, which, if appreciated, may help improve the function of glucose controllers in the intensive care unit setting. J Diabetes Sci Technol. 2010 Jan 1;4(1):190-8. doi: 10.1177/193229681000400124.

Reference Type BACKGROUND
PMID: 20167184 (View on PubMed)

Sardu C, D'Onofrio N, Balestrieri ML, Barbieri M, Rizzo MR, Messina V, Maggi P, Coppola N, Paolisso G, Marfella R. Outcomes in Patients With Hyperglycemia Affected by COVID-19: Can We Do More on Glycemic Control? Diabetes Care. 2020 Jul;43(7):1408-1415. doi: 10.2337/dc20-0723. Epub 2020 May 19.

Reference Type BACKGROUND
PMID: 32430456 (View on PubMed)

Other Identifiers

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IMT 2022-1

Identifier Type: -

Identifier Source: org_study_id

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