Clinical trial NCT04313803: CGM in Utah Valley
Official title: Expanding Continuous CGM Usage to Improve Outcomes in a Shorter Duration in Utah Valley Clinics
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Diabetes Mellitus
Diabetes Mellitus, Type 2
Diabetes
Keywords: Continuous glucose monitor (CGM)
diabetes
glucose
Lead sponsor: Intermountain Health Care, Inc.
Sponsor class: OTHER
Collaborators: name: DexCom, Inc.
First posted: 2020-03-18
Enrollment: 1500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with type II diabetes treated at Intermountain Healthcare

Brief summary: The purpose of this study is to replicate the positive impact observed in IRB #1050955, but conduct this over a shorter period to potentially maximize patient outcomes and make care more affordable. Intermountain intends to build a diabetes program with CGM based on the findings. Senior stakeholders, clinicians and operators are aligned on this vision including the Community Based Care triad, Executive Leadership Team, and our Diabetes Prevention Program.

Detailed description: Overview

Approximately 30 million Americans, or 9% of the population has diabetes, a condition in which a person does not make enough insulin, or the body cannot use its own to effectively manage blood glucose levels. Improper diabetes management is associated with severe comorbidities which include: heart disease, stroke, kidney disease, ocular problems, dental disease, nerve damage, and vascularity issues. The epidemic continues to challenge systems like Intermountain Healthcare, an accountable care organization (ACO), since diabetes cost $327 billion per year (representing $1 in every $7 dollars spent) on healthcare in the United States. Furthermore, people with diagnosed diabetes incur average medical expenditures of $16,752 per year, of which about $9,601 is directly attributed to diabetes. New treatment options are needed to manage population health, especially with 84 million adults having been diagnosed with prediabetes diabetes.

In an effort to reduce the physical, economic and social burden of diabetes, several healthcare systems have evaluated the use of telehealth to monitor glucose levels. In a previous metanalysis, the authors demonstrated that telehealth interventions produced a small, but significant improvement in hemoglobin A1c (HbA1c) levels compared with usual care (mean difference: -0.55, 95% CI: -0.73 to - 0.36). The Ontario Health Technology Advisory Committee also showed that the blood glucose home telemonitoring technologies they used yielded a statistically significant reduction in HbA1c of \~0.50% in comparison to usual care when used adjunctively to a broader telemedicine initiative for adults with type 2 diabetes.

1.2 Previous Work

Intermountain Healthcare conducted a pilot study in the Reimagine Primary Care (RPC) clinics to evaluate if six months of CGM could improve patient outcomes (IRB #1050955). A total of 99 patients remained enrolled for the full time period (n=50 CGM, n=49 standard of care (SOC)), and data showed a improvement in glucose levels, less primary care and specialty appointments, a reduction in emergency department (ED) encounters, less labs ordered, and a cumulative body mass index (BMI) improvement. Furthermore, nearly all participants reported being willing to engage in another future pilot, and the vast improvements were attributed to subjects use of real-time data.

Primary analyses

Cost of care for fee-for-value patients (specifically PMPM savings)

Secondary analyses

Frequency of hypoglycemic events, healthcare utilization per count of inpatient/outpatient visits, cost of care, current HEDIS performance on diabetes and behavioral health measures, coding specificity for diabetes, emergency department visit per 1000 rate, overall and for patients with diabetes.

Power analyses

Data from IRB #1050955 has shown significant changes in cost, care and utilization with only a sample of 50 CGM users. The effect size is currently being calculated by the study statistician, but most of the outcome variables comparing CGM to standard of care device were p\<0.05. Given that this will now include a much larger population, and 30x participant increase, the investigators will have sufficient power to deduce differences should they occur.

Eligibility criteria: Inclusion Criteria:

* Patients (18-80 years of age) with type ll diabetes, having an HbA1c ≥6.5%, treated within five Intermountain Healthcare Utah Valley clinics. Patients must have access to a smart phone to download applications, have Bluetooth capabilities for data sharing, and log/view their continuous glucose monitor (CGM) data.

Exclusion Criteria:

* Patients who are pregnant, not classified as having diabetes based on A1c levels, and age ≤ 17 or ≥ 81 years, or diagnosis of dementia.

Primary outcomes:
measure: Cost of care for fee-for-value patients (specifically PMPM savings)
timeFrame: 1 April 2020 - 30 April 2021
Secondary outcomes:
measure: The count of primary care, specialist, and emergency department visits or hospital stays for those using CGM compared to historical utilization and the broader T2DM cohort within Intermountain Healthcare.
timeFrame: 1 April 2020 - 30 April 2021
measure: Evaluation of HEDIS performance for those using CGM compared to historical utilization and the broader T2DM cohort within Intermountain Healthcare.
timeFrame: 1 April 2020 - 30 April 2021
Other outcomes:
Locations:
Study officials:
affiliation: Intermountain Health Care, Inc.
name: Elizabeth Joy, MD, MPH
role: PRINCIPAL_INVESTIGATOR
affiliation: Intermountain Health Care, Inc.
name: Brad Isaacson, PhD, MBA, MSF, PMP
role: STUDY_DIRECTOR
References:
citation: Lee PA, Greenfield G, Pappas Y. The impact of telehealth remote patient monitoring on glycemic control in type 2 diabetes: a systematic review and meta-analysis of systematic reviews of randomised controlled trials. BMC Health Serv Res. 2018 Jun 26;18(1):495. doi: 10.1186/s12913-018-3274-8.
citation: Ekhlaspour L, Mondesir D, Lautsch N, Balliro C, Hillard M, Magyar K, Radocchia LG, Esmaeili A, Sinha M, Russell SJ. Comparative Accuracy of 17 Point-of-Care Glucose Meters. J Diabetes Sci Technol. 2017 May;11(3):558-566. doi: 10.1177/1932296816672237. Epub 2016 Oct 3.
citation: Verkuilen J. Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach by P. de Boeck and M. Wilson and Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models by A. Skrondal and S. Rabe-Hesketh. Psychometrika. 2006 Jun;71(2):415-418. doi: 10.1007/s11336-005-1333-7. No abstract available.
citation: Fong Y, Rue H, Wakefield J. Bayesian inference for generalized linear mixed models. Biostatistics. 2010 Jul;11(3):397-412. doi: 10.1093/biostatistics/kxp053. Epub 2009 Dec 4.