Type 2 Diabetes
Conditions
Keywords
Type 2 Diabetes, Hospital, Continous Glucose Monitoring
Brief summary
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.
Detailed description
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.
Interventions
DEVICEDexcom G6 Continous Glucose Monitoring Management
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.
DEVICEUsual Care - Blinded Continuous Glucose Monitoring Management
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.
Sponsors
Scripps Whittier Diabetes Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
SINGLE (Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Percent time in range | 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. |
| Percent time spent in hypoglycemia and percent time in severe hyperglycemia | 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). |
| Infection Rate | 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
| Measure | Time frame | Description |
|---|---|---|
| Glucose Variability | 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. |
| Electronic Medical Record (EMR) - Derived Outcomes: HbA1C | 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. |
| Electronic Medical Record (EMR) - Derived Outcome: fasting POC blood glucose | 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. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORAthena Philis-Tsimikas, MD
Scripps Whittier Diabetes Institute
PRINCIPAL_INVESTIGATORAddie Fortmann, PhD
Scripps Whittier Diabetes Institute
Outcome results
None listed