Study Results
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Basic Information
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UNKNOWN
1500 participants
OBSERVATIONAL
2020-04-01
2021-04-30
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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American Fork
CGM usage months 1 and 3
CGM
Dexcom CGM system
Central Orem
CGM usage for month 1
CGM
Dexcom CGM system
North Canyon, Saratoga Springs, and Lehi
CGM usage for 1-3 months
CGM
Dexcom CGM system
Interventions
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CGM
Dexcom CGM system
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
80 Years
ALL
Yes
Sponsors
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DexCom, Inc.
INDUSTRY
Intermountain Health Care, Inc.
OTHER
Responsible Party
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Principal Investigators
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Elizabeth Joy, MD, MPH
Role: PRINCIPAL_INVESTIGATOR
Intermountain Health Care, Inc.
Brad Isaacson, PhD, MBA, MSF, PMP
Role: STUDY_DIRECTOR
Intermountain Health Care, Inc.
Central Contacts
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References
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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.
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.
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.
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.
Other Identifiers
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IHC IRB#: 1051315
Identifier Type: -
Identifier Source: org_study_id
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