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Completed

NCT Number: NCT05307237

Continuous Glucose Monitoring for High-Risk Type 2 Diabetes in the Hospital (Cyber GEMS)

Given the known serious consequences of uncontrolled blood sugars during hospitalization, this research plans to study an alternative seamlessly integrated continuous glucose monitoring (CGM) system in the hospital to test a dynamic and digitized, team-based approach to glucose management in an underserved and understudied, yet high-risk population. A digital dashboard will facilitate real-time, remote monitoring of a large volume of patients simultaneously; automatically identify and prioritize patients for intervention; and will detect any and all potentially dangerous hypoglycemic episodes in a hospital environment. The study will focus on clinical metrics of glucose control and infection that are in-line with patient priorities and US hospital quality initiatives.

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Key information

Conditions

Type 2 Diabetes Diabetes Mellitus Diabetes Mellitus, Type 2 Endocrine System Diseases Glucose Metabolism Disorders Metabolic Diseases Nutritional and Metabolic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Scripps Mercy Hospital

Chula Vista, California, 91910, United States

About this study

There is strong evidence that poor glycemic control in the hospital is common. Given the known consequences of uncontrolled blood sugars during a hospitalization (e.g., infection, serious neurological and cardiac complications, mortality, longer lengths of stay, readmissions, higher healthcare costs), health systems devote significant resources to developing protocols for improving glucometrics. Despite the widespread use and demonstrated effectiveness of continuous glucose monitoring (CGM) for ambulatory glucose management, CGMs is not routinely used in US hospitals. Therefore, the long-term goal to develop Cloud-Based Real-Time Glucose Evaluation and Management System (Cyber GEMS) is to provide an effective, real-time solution to augment existing processes, to provide a valuable test of real-world effectiveness, while capitalizing on standardized algorithms to facilitate sustainability and scalability to other systems and at-risk populations. The intervention will enable hospital care teams to take immediate steps based on the wireless transmission of glucose data from the Dexcom G6 device, sent to a digital dashboard, where integration with existing real-world hospital processes can provide immediate prioritization to prevent or correct impending hypoglycemia and severe hyperglycemic events. This study is a randomized controlled trial, defined as a Phase II/III definitive clinical trial that in turn establishes efficacy and effectiveness of this intervention. Aim 1 will establish the effectiveness of Cyber GEMS versus Usual Care (UC) in increasing the % time patients are in-range and decreasing % time in hypoglycemia and severe hyperglycemia during hospitalization. Aim 2 will evaluate the effectiveness of Cyber GEMS versus UC in decreasing hospital-acquired infection risk. A digital dashboard will facilitate real-time, wireless transmission of glucose data of a large volume of patients simultaneously; automatically identify and prioritize patients for intervention; and detect potentially dangerous hypoglycemic episodes - all at a reduced burden than current methods of stratification and review. The uninterrupted coverage, and efficient and remote diabetes specialist oversight in Cyber GEMS is a scalable, novel, team-based approach to maximize the use of continuously streaming CGM data for optimal glucose management.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Documented previous or current Type 2 Diabetes (T2D) diagnosis as defined by either diagnosis in the chart or an HbA1c > or = to 6.5% in the last 90 days
  • Either on subcutaneous (SQ) insulin orders, or greater than two serum or Point of Care (POC) glucose > or = 200 mg/dL in most recent 24 hours of admission

Exclusion criteria

  • Anticipated length of stay < 24 hours;
  • Current or anticipated ICU placement;
  • Does not speak English or Spanish;
  • Known allergy to adhesives;
  • Current participation in any medication or device research study;
  • Pregnant;
  • Any other condition that Multiple Principal Investigator (MPI) Philis-Tsimikas or the attending physician deems contraindicated

Treatment and study plan

Dexcom G6 Continous Glucose Monitoring Management

Device

CGM data will be transmitted from the bedside iPhone to web-based platforms for: (1) Real-Time Management (via iPad-based FOLLOW app used by bedside RN and Digital Dashboard used by the remote monitoring team) and (2) Clinical Optimization (via CLARITY, by which a Diabetes RN Coordinator will conduct remote clinical management of patients from a central, Scripps Diabetes Hub.

Usual Care - Blinded Continuous Glucose Monitoring Management

Device

CGM data will be blinded and used for evaluation purposes only. Glucose will be monitored via the hospital's standard POC testing protocol (i.e., prior to meals and at bedtime for patients who are eating, and every 4-6 waking hours if not eating). Glucose management in UC is designed to minimize differences between groups, aside from CGM monitoring.

Primary outcomes

  1. Percent time in range

    Time frame: Immediately following intervention completion

    Participants will have their percent time in range calculated following a minimum CGM data collection period of 12 hours and expressed as a percentage where: Percent Time in Range= 100 (Number readings in range (70-200mg/dL)/Total number of readings from CGM). Number of readings will be used in calculation, which scale directly with time.

  2. Percent time spent in hypoglycemia and percent time in severe hyperglycemia

    Time frame: Immediately following intervention completion

    Our second outcome will be assessed by the same methods as the first, but instead looking at Percent Time in Severe Hyperglycemic Range (>300mg/dL) and Percent Time in Hypoglycemic Range (<70mg/dL).

  3. Infection Rate

    Time frame: Immediately following intervention completion

    Rates of hospital-acquired infection are defined as skin wound or surgical site, central line-associated bloodstream infection, urinary tract infection, bacteremia, clostridium difficile infection, or pneumonia not present at admission. Unadjusted incidence rates among study participants will be compared between intervention and control groups via Chi-Square test of two proportions.

Secondary outcomes

  1. Glucose Variability

    Time frame: Immediately following intervention completion

    Using CGM data, glucose variability will be determined by first calculating the coefficient of variation for each participant, dividing the standard deviation of the glucose readings of that participant, by the mean of those readings and multiplying by 100 to get a percentage. Mean coefficients of variation will be compared between intervention and control groups by a students t test.

  2. Electronic Medical Record (EMR) - Derived Outcomes: HbA1C

    Time frame: Immediately following intervention completion

    Additional metrics of glycemic control will be captured for each study participant from the EMR including: HbA1C. Like primary outcome analyses, group mean differences of each variable will be assessed unadjusted with a students t-test utilized to detect between-group differences.

  3. Electronic Medical Record (EMR) - Derived Outcome: fasting POC blood glucose

    Time frame: Immediately following intervention completion

    Additional metrics of glycemic control will be captured for each study participant from the EMR including fasting point-of-care (POC) blood glucose measurements (mg/dL). Like primary outcome analyses, group mean differences of each variable will be assessed unadjusted with a students t-test utilized to detect between-group differences.

Other outcomes

  1. Process Indicators (Reach): Enrollment Characteristics

    Time frame: Immediately following intervention completion

    To examine enrollment rate, demographic characteristics of eligible patients will be compared between those who enroll versus decline; where continuous measures will be compared between groups will be compared between groups by Chi-Square tests.

  2. Process Indicators (Reach): Representative Characteristics

    Time frame: Immediately following intervention completion

    To examine generalizability of our sample, distribution of demographics in our sample will be compared to expected distributions of our target population through Chi-square tests.

  3. Process Indicators (Reach): CGM wear time

    Time frame: Immediately following intervention completion

    Median time on CGM will also be compared between Cyber GEMs and UC groups using a Mann-Whitney test.

  4. Process Indicators (Reach): Withdrawal rate

    Time frame: Immediately following intervention completion

    We do not plan to statistically assess reasons for withdrawal due to an anticipated low number of withdrawals, but all reasons will be recorded and descriptively quantified where applicable.

  5. Process Indicators (Efficacy): Impact of time on CGM

    Time frame: Immediately following intervention completion

    A generalized linear model will be used to assess whether time on CGM relates to changes in the percent time in primary and secondary outcome ranges over time.

  6. Process Indicators (Efficacy): Negative outcomes

    Time frame: Immediately following intervention completion

    Unintended negative outcomes will be recorded and descriptively analyzed.

  7. Process Indicators (Adoption): Perceptions of CGM

    Time frame: Immediately following intervention completion

    Results of semi-structured interviews will be qualitatively, descriptively analyzed to reveal perceptions of CGM implementation efficacy, challenges, satisfaction, and benefits.

  8. Process Indicators (Adoption): Clinical perceptions of glucose management

    Time frame: Immediately following intervention completion

    Descriptively assess physicians pre- and post study perceptions and knowledge of and identified barriers to successful inpatient glucose control via the Inpatient Glucose Management Questionnaire (IGCQ).

  9. Process Indicators (Implementation): Alarm actions

    Time frame: Immediately following intervention completion

    # of alarms for glucose managed by the Clinical Transfer Center (Cyber GEMS only) will be quantified for percent adherence to protocol by: # of times Clinical Transfer Center notified bedside Registered Nurse (RN) / # of qualifying alarms. Rates of follow-up POC testing at bedside will be analyzed/statistically tested by fitting a linear model with # of alarms.

  10. Process Indicators (Implementation): CGM satisfaction

    Time frame: Immediately following intervention completion

    CGM satisfaction will be determined using a modified CGM Satisfaction Scale. Questions regarding comfort/interruption, given in both arms and mean overall scores compared by unpaired students t-tests.

  11. Process Indicators (Maintenance): Enrollment progress

    Time frame: Immediately following intervention completion

    Number of participants will be continuously monitored by the Data Analyst throughout the study period and tracked against projected numbers for enrollment.

  12. Process Indicators (Maintenance): Stakeholder and advisory board feedback

    Time frame: Immediately following intervention completion

    Feedback from Stakeholders and Community Advisory Board members will be descriptively analyzed.

Sponsors and collaborators

Lead sponsor

Scripps Whittier Diabetes Institute

Other

Collaborators

  • National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Registry information

Official study title

Continuous Glucose Monitoring for High-Risk Type 2 Diabetes in the Hospital: Cloud-Based Real-Time Glucose Evaluation and Management System (Cyber GEMS)

Acronym: Cyber GEMS

Important dates

Study start
2022
Primary completion
2025
Study completion
2025
First posted
Apr 1, 2022
Registry last updated
Mar 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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