Assessing Patterns of Continuous Glucose Monitoring Use and Metrics of Glycemic Control in Type 1 Diabetes and Type 2 Diabetes Patients in the Veterans Health Care System: Integrating Continuous Glucose Monitoring Device Data with Electronic Health Records Data.

Objective: To integrate long-term daily continuous glucose monitoring (CGM) device data with electronic health records (EHR) for patients with type 1 and type 2 diabetes (T1D and T2D) in the national Veterans Affairs Healthcare System to assess real-world patterns of CGM use and the reliability of EHR-based CGM information. Research Design and Methods: This observational study used Dexcom CGM device data linked with EHR (from 2015 to 2020) for a large national cohort of patients with diabetes. We tracked the initiation and consistency of CGM use, assessed concordance of CGM use and measures of glucose control between CGM device data and EHR records, and examined results by age, ethnicity, and diabetes type. Results: The time from pharmacy release of CGM to patients to initiation of uploading CGM data to Dexcom servers averaged 3 weeks but demonstrated wide variation among individuals; importantly, this delay decreased markedly over the later years. The average daily wear time of CGM exceeded 22 h over nearly 3 years of follow-up. Patterns of CGM use were generally consistent across age, race/ethnicity groups, and diabetes type. There was strong concordance between EHR-based estimates of CGM use and Dexcom CGM wear time and between estimates of glucose control from both sources. Conclusions: The study demonstrates our ability to reliably integrate CGM devices and EHR data to provide valuable insights into CGM use patterns. The results indicate in the real-world environment that CGM is worn consistently over many years for both patients with T1D and T2D within the Veterans Affairs Healthcare System and is similar across major race/ethnic groups and age-groups.

Reduction in Diabetes-Related Hospitalizations and Medical Costs After Dexcom G6 Continuous Glucose Monitor Initiation in People with Type 2 Diabetes Using Intensive Insulin Therapy.

INTRODUCTION: Some people with type 2 diabetes (T2D) require intensive insulin therapy to manage their diabetes. This can increase the risk of diabetes-related hospitalizations. We hypothesize that initiation of real-time continuous glucose monitoring (RT-CGM), which continuously measures a user's glucose values and provides threshold- and trend-based alerts, will reduce diabetes-related emergency department (ED) and inpatient hospitalizations and concomitant costs. METHODS: A retrospective analysis of US healthcare claims data using Optum's de-identified Clinformatics® Data Mart database was performed. The cohort consisted of commercially insured, CGM-naïve individuals with T2D who initiated Dexcom G6 RT-CGM system between August 1, 2018, and March 31, 2021. Twelve months of continuous health plan enrollment before and after RT-CGM initiation was required to capture baseline and follow-up rates of diabetes-related hospitalizations and associated healthcare resource utilization (HCRU) costs. Analyses were performed for claims with a diabetes-related diagnosis code in either (1) any position or (2) first or second position on the claim. RESULTS: A total of 790 individuals met the inclusion criteria. The average age was 52.8 (10.5) [mean (SD)], 53.3% were male, and 76.3% were white. For claims with a diabetes-related diagnosis code in any position, the number of individuals with ≥ 1 ED visit decreased by 30.0% (p = 0.01) and with ≥ 1 inpatient visit decreased by 41.5% (p < 0.0001). The number of diabetes-related visits and average number of visits per person similarly decreased by at least 31.4%. Larger relative decreases were observed for claims with a diabetes-related diagnosis code in the first or second position on the claim. Total diabetes-related costs expressed as per-person-per-month (PPPM) decreased by $341 PPPM for any position and $330 PPPM for first or second position. CONCLUSION: Initiation of Dexcom G6 among people with T2D using intensive insulin therapy was associated with a significant reduction in diabetes-related ED and inpatient visits and related HCRU costs. Expanded use of RT-CGM could augment these benefits and result in further cost reductions.

Association Between Adherence, A1C Improvement, and Type of Continuous Glucose Monitoring System in People with Type 1 Diabetes or Type 2 Diabetes Treated with Intensive Insulin Therapy.

INTRODUCTION: Use of continuous glucose monitoring (CGM) systems by people with diabetes is associated with improved glycemic outcomes, including lower glycated hemoglobin (A1C). Less is known about adherence to CGM systems, whether glycemic outcomes are impacted by levels of adherence, or whether adherence rates differ between types of CGM systems-intermittently scanned CGM (isCGM) or real-time CGM (rtCGM). METHODS: A retrospective analysis of de-identified US administrative health claims and linked laboratory data was conducted using the Merative™ MarketScan® Research Database. The cohort included CGM-naïve people with type 1 diabetes (T1D) or type 2 diabetes treated with intensive insulin therapy (T2D-IIT) who initiated rtCGM or isCGM between August 1, 2019 and March 31, 2021 (defined as the index date). Adherence was calculated over a 12-month period using the proportion of days covered (PDC) with PDC ≥ 0.8 defined as adherent. A1C values were obtained within 6 months of the index date. RESULTS: A total of 7669 individuals were identified. Subgroups included T1D using isCGM (n = 1578), T1D using rtCGM (n = 1244), T2D-IIT using isCGM (n = 3567), and T2D-IIT using rtCGM (n = 1280). After 12 months, PDC was 0.71 (0.30)-0.72 (0.31) (mean(SD)) for T1D and T2D-IIT rtCGM users and 0.55 (0.34)-0.56 (0.34) for T1D and T2D-IIT isCGM users. The proportion of adherent users (PDC ≥ 0.8) was 56.8-59.7% for rtCGM users and 36.3-37.6% for isCGM users. Overall, regardless of diabetes type, the odds of adherence were over two times higher for rtCGM users compared to isCGM users. For those with available A1C information (T1D n = 213; T2D-IIT n = 346), independent of CGM type, adherence to CGM was associated with a greater reduction in A1C and more people reaching A1C targets of < 7.0% or < 8.0%. CONCLUSION: For people with T1D or T2D-IIT, higher adherence to CGM is associated with greater reductions in A1C, and higher adherence rates were observed with rtCGM systems than with isCGM systems.

Cost-utility of real-time continuous glucose monitoring versus self-monitoring of blood glucose in people with insulin-treated Type II diabetes in France.

Aim: Clinical trials and real-world data for Type II diabetes both show that glycated hemoglobin (HbA1c) levels and hypoglycemia occurrence can be reduced by real-time continuous glucose monitoring (rt-CGM) versus self-monitoring of blood glucose (SMBG). The present cost-utility study investigated the long-term health economic outcomes associated with using rt-CGM versus SMBG in people with insulin-treated Type II diabetes in France. Materials & methods: Effectiveness data were obtained from a real-world study, which showed rt-CGM reduced HbA1c by 0.56% (6.1 mmol/mol) versus sustained SMBG. Analyses were conducted using the IQVIA Core Diabetes Model. A French payer perspective was adopted over a lifetime horizon for a cohort aged 64.5 years with baseline HbA1c of 8.3% (67 mmol/mol). A willingness-to-pay threshold of €147,093 was used, and future costs and outcomes were discounted at 4% annually. Results: The analysis projected quality-adjusted life expectancy was 8.50 quality-adjusted life years (QALYs) for rt-CGM versus 8.03 QALYs for SMBG (difference: 0.47 QALYs), while total mean lifetime costs were €93,978 for rt-CGM versus €82,834 for SMBG (difference: €11,144). This yielded an incremental cost-utility ratio (ICUR) of €23,772 per QALY gained for rt-CGM versus SMBG. Results were particularly sensitive to changes in the treatment effect (i.e., change in HbA1c), annual price and quality of life benefit associated with rt-CGM, SMBG frequency, baseline patient age and complication costs. Conclusion: The use of rt-CGM is likely to be cost-effective versus SMBG for people with insulin-treated Type II diabetes in France.

Cost-utility of real-time continuous glucose monitoring versus self-monitoring of blood glucose in people with insulin-treated Type 2 diabetes in Canada.

Aim: Clinical trials and real-world data for Type 2 diabetes have shown that real-time continuous glucose monitoring (rt-CGM) lowers glycated hemoglobin (A1c) and reduces hypoglycemia relative to self-monitoring of blood glucose (SMBG). This analysis examined the long-term health and economic outcomes associated with using rt-CGM versus SMBG in people with insulin-treated Type 2 diabetes in Canada. Materials & methods: Clinical data were sourced from a real-world study, in which rt-CGM reduced A1C by 0.56% versus continued SMBG. The analysis was performed using the IQVIA Core Diabetes Model, from a Canadian payer perspective over a lifetime horizon for a cohort aged 65 years with an A1C of 8.3% at baseline. Future costs and clinical outcomes were discounted at 1.5% annually. Results: Projected total mean lifetime costs were CAD 207,466 for rt-CGM versus CAD 189,863 for SMBG (difference: CAD 17,602) and projected mean quality-adjusted life expectancy was 9.97 quality-adjusted life years (QALYs) for rt-CGM versus 9.02 QALYs for SMBG (difference: 0.95 QALYs), resulting in an incremental cost-utility ratio (ICUR) of CAD 18,523 per QALY gained for rt-CGM versus SMBG. Findings were sensitive to changes in the A1C treatment effect, annual cost and quality of life benefit associated with using rt-CGM, SMBG frequency, and baseline age, but ICURs remained below CAD 50,000 per QALY in all analyses. Conclusion: For people in Canada with insulin-treated Type 2 diabetes and poor glycemic control, use of rt-CGM is likely to be cost-effective relative to SMBG.

Association Between Change in A1C and Use of Professional Continuous Glucose Monitoring in Adults With Type 2 Diabetes on Noninsulin Therapies: A Real-World Evidence Study.

This retrospective analysis examined the association between change in A1C and professional continuous glucose monitoring (p-CGM) use in patients with type 2 diabetes and poor glycemic control who were not using insulin. Data from 15,481 eligible patients (p-CGM users n = 707 and p-CGM nonusers n = 14,774) showed a greater decrease in A1C from baseline to the end of follow-up for p-CGM users, and differences favored p-CGM users regardless of whether they started insulin therapy during the follow-up period. These findings suggest that people with type 2 diabetes who have poor glycemic control using multiple noninsulin therapies may benefit from p-CGM, which can reduce A1C over a 6-month period compared with usual care.

An Observational Crossover Study of People Using Real-Time Continuous Glucose Monitors Versus Self-Monitoring of Blood Glucose: Real-World Evidence Using EMR Data From More Than 12,000 People With Type 1 Diabetes.

BACKGROUND: We used real-world electronic health record (EHR) data to examine HbA1c levels among children and adults with type 1 diabetes (T1D) who are classified as continuous glucose monitor (CGM) users after T1D diagnosis and switch to self-monitoring of blood glucose (SMBG) during follow-up, versus people who opt for SMBG after T1D diagnosis and switch to CGM during follow-up visits. METHODS: We conducted an observational, case-crossover study using electronic medical record (EMR) data from the T1D Exchange Quality Improvement Collaborative. The primary outcome in this study was HbA1c. Baseline HbA1c levels were taken at the index date, corresponding to initial device classification, and compared with HbA1c value recorded at the clinic visit following device switch. RESULTS: Of all patients classified in the SMBG group, 7,706 switched to CGM use within the 5-year study time frame, and 5,123 of all initial CGM users switched to SMBG within the study time frame and were included in this analysis. At baseline, median (interquartile range [IQR]) HbA1c for SMBG use was 8.1 (2.4), whereas postcrossover to CGM use, there was a decline in median (IQR) levels to 7.7 (1.9) (P < .001). For baseline CGM users, median (IQR) HbA1c levels were 7.9 (2.0), and postcrossover to SMBG, median (IQR) HbA1c levels increased to 8.0 (2.9) (P < .001). CONCLUSION: We found that people who switched to CGM use had significantly improved HbA1c levels compared to those who switched to glucose monitoring with SMBG.

G6 continuous glucose monitoring system feature use and its associations with glycaemia in Europe.

AIMS: Current continuous glucose monitoring (CGM) devices provide features that alert individuals with diabetes about their current and impending adverse glycaemic events. The use of these features has been associated with glycaemic improvements. However, how these features are utilised under real-world conditions has not been well studied. We queried a large database to quantify utilisation of the Dexcom G6 system features and how utilisation impacted glycaemic outcomes within a cohort of European users. METHODS: This 6-month retrospective, observational, large database analysis utilised anonymised data from a sample of 47,784 Europe-based G6 users. Primary outcome measures were associations between utilisation and customisation of High/Low threshold alerts, 'urgent low soon' (ULS) alert, and established CGM metrics. RESULTS: Users in the Germany, Austria, Switzerland region (n = 20,257), the Nordic countries (n = 10,314), United Kingdom (n = 9006), Italy (n = 4747), France (n = 2130) and Spain (1330) were included. All alert features were utilised by >75% of the cohort across all regions/countries and age groups. Enabling the Low alert and ULS alert was associated with lower percentage of time below range compared to disabling the Low alert (p < 0.001). Enabling the High alert was associated with higher percentage of time in range (%TIR) and lower percentage of time above range (%TAR) %TAR compared to disabling the High alert (p < 0.001). Paediatric patients and older adults tended to set a higher threshold for High/Low alerts, while younger adults tended to use lower threshold values for High/Low alerts. CONCLUSIONS: Individuals who utilised the Dexcom G6 features showed better glycaemic control, particularly among those who utilised more sensitive High alert and Low alert settings, than users who did not utilise the system features.

Initiation of Continuous Glucose Monitoring Is Linked to Improved Glycemic Control and Fewer Clinical Events in Type 1 and Type 2 Diabetes in the Veterans Health Administration.

OBJECTIVE: To determine the benefit of starting continuous glucose monitoring (CGM) in adult-onset type 1 diabetes (T1D) and type 2 diabetes (T2D) with regard to longer-term glucose control and serious clinical events. RESEARCH DESIGN AND METHODS: A retrospective observational cohort study within the Veterans Affairs Health Care System was used to compare glucose control and hypoglycemia- or hyperglycemia-related admission to an emergency room or hospital and all-cause hospitalization between propensity score overlap weighted initiators of CGM and nonusers over 12 months. RESULTS: CGM users receiving insulin (n = 5,015 with T1D and n = 15,706 with T2D) and similar numbers of nonusers were identified from 1 January 2015 to 31 December 2020. Declines in HbA1c were significantly greater in CGM users with T1D (-0.26%; 95% CI -0.33, -0.19%) and T2D (-0.35%; 95% CI -0.40, -0.31%) than in nonusers at 12 months. Percentages of patients achieving HbA1c <8 and <9% after 12 months were greater in CGM users. In T1D, CGM initiation was associated with significantly reduced risk of hypoglycemia (hazard ratio [HR] 0.69; 95% CI 0.48, 0.98) and all-cause hospitalization (HR 0.75; 95% CI 0.63, 0.90). In patients with T2D, there was a reduction in risk of hyperglycemia in CGM users (HR 0.87; 95% CI 0.77, 0.99) and all-cause hospitalization (HR 0.89; 95% CI 0.83, 0.97). Several subgroups (based on baseline age, HbA1c, hypoglycemic risk, or follow-up CGM use) had even greater responses. CONCLUSIONS: In a large national cohort, initiation of CGM was associated with sustained improvement in HbA1c in patients with later-onset T1D and patients with T2D using insulin. This was accompanied by a clear pattern of reduced risk of admission to an emergency room or hospital for hypoglycemia or hyperglycemia and of all-cause hospitalization.

Glycemic Outcomes and Feature Set Engagement Among Real-Time Continuous Glucose Monitoring Users With Type 1 or Non-Insulin-Treated Type 2 Diabetes: Retrospective Analysis of Real-World Data.

BACKGROUND: The benefits of real-time continuous glucose monitoring (RT-CGM) are well established for patients with type 1 diabetes (T1D) and patients with insulin-treated type 2 diabetes (T2D). However, the usage and effectiveness of RT-CGM in the context of non-insulin-treated T2D has not been well studied. OBJECTIVE: We aimed to assess glycemic metrics and rates of RT-CGM feature utilization in users with T1D and non-insulin-treated T2D. METHODS: We retrospectively analyzed data from 33,685 US-based users of an RT-CGM system (Dexcom G6; Dexcom, Inc) who self-identified as having either T1D (n=26,706) or T2D and not using insulin (n=6979). Data included glucose concentrations, alarm settings, feature usage, and event logs. RESULTS: The T1D cohort had lower proportions of glucose values in the 70 mg/dl to 180 mg/dl range than the T2D cohort (52.1% vs 70.8%, respectively), with more values indicating hypoglycemia or hyperglycemia and higher glycemic variability. Discretionary alarms were enabled by a large majority in both cohorts. The data sharing feature was used by 38.7% (10,327/26,706) of those with T1D and 10.4% (727/6979) of those with T2D, and the mean number of followers was higher in the T1D cohort. Large proportions of patients with T1D or T2D enabled and customized their glucose alerts. Retrospective analysis features were used by the majority in both cohorts (T1D: 15,783/26,706, 59.1%; T2D: 3751/6979, 53.8%). CONCLUSIONS: Similar to patients with T1D, patients with non-insulin-treated T2D used RT-CGM system features, suggesting beneficial, routine engagement with data by patients and others involved in their care. Motivated patients with diabetes could benefit from RT-CGM coverage.

Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study.

BACKGROUND: Physical inactivity is associated with numerous health risks, including cancer, cardiovascular disease, type 2 diabetes, increased health care expenditure, and preventable, premature deaths. The majority of Americans fall short of clinical guideline goals (ie, 8000-10,000 steps per day). Behavior prediction algorithms could enable efficacious interventions to promote physical activity by facilitating delivery of nudges at appropriate times. OBJECTIVE: The aim of this paper is to develop and validate algorithms that predict walking (ie, >5 min) within the next 3 hours, predicted from the participants' previous 5 weeks' steps-per-minute data. METHODS: We conducted a retrospective, closed cohort, secondary analysis of a 6-week microrandomized trial of the HeartSteps mobile health physical-activity intervention conducted in 2015. The prediction performance of 6 algorithms was evaluated, as follows: logistic regression, radial-basis function support vector machine, eXtreme Gradient Boosting (XGBoost), multilayered perceptron (MLP), decision tree, and random forest. For the MLP, 90 random layer architectures were tested for optimization. Prior 5-week hourly walking data, including missingness, were used for predictors. Whether the participant walked during the next 3 hours was used as the outcome. K-fold cross-validation (K=10) was used for the internal validation. The primary outcome measures are classification accuracy, the Mathew correlation coefficient, sensitivity, and specificity. RESULTS: The total sample size included 6 weeks of data among 44 participants. Of the 44 participants, 31 (71%) were female, 26 (59%) were White, 36 (82%) had a college degree or more, and 15 (34%) were married. The mean age was 35.9 (SD 14.7) years. Participants (n=3, 7%) who did not have enough data (number of days <10) were excluded, resulting in 41 (93%) participants. MLP with optimized layer architecture showed the best performance in accuracy (82.0%, SD 1.1), whereas XGBoost (76.3%, SD 1.5), random forest (69.5%, SD 1.0), support vector machine (69.3%, SD 1.0), and decision tree (63.6%, SD 1.5) algorithms showed lower performance than logistic regression (77.2%, SD 1.2). MLP also showed superior overall performance to all other tried algorithms in Mathew correlation coefficient (0.643, SD 0.021), sensitivity (86.1%, SD 3.0), and specificity (77.8%, SD 3.3). CONCLUSIONS: Walking behavior prediction models were developed and validated. MLP showed the highest overall performance of all attempted algorithms. A random search for optimal layer structure is a promising approach for prediction engine development. Future studies can test the real-world application of this algorithm in a "smart" intervention for promoting physical activity.

Assessment of Glucose Monitoring Adherence in Medicare Beneficiaries with Insulin-Treated Diabetes.

Background: We investigated the potential associations between race/ethnicity and adherence to prescribed glucose monitoring in a sample of Medicare beneficiaries with diabetes and how adherence to the method used impacted diabetes-related inpatient hospitalizations and associated costs among beneficiaries with intensive insulin-treated diabetes. Methods: This 12-month retrospective analysis utilized Centers for Medicare & Medicaid Services data to identify Medicare beneficiaries who used intensive insulin therapy from January through December 2018 and classified them into four groups: (1) persons using real-time continuous glucose monitoring (rtCGM), (2) persons using any method of blood glucose monitoring (BGM) who followed prescribed use patterns (adherent), (3) persons who were prescribed BGM but were nonadherent in its use, and (4) no record of any form of BGM. Analyses compared these groups and the role that comorbidities (Charlson Comorbidity Index [CCI]), and race/ethnicity played on group assignment, diabetes-related inpatient hospitalizations, and costs. Results: Among the 1,329,061 persons assessed, 38.14% had no record of glucose monitoring and 35.42% were BGM nonadherent. Similarly, among the 629,514 beneficiaries with a CCI risk score of ≥2, 466,646 (74.13%) were either nonadherent to BGM or had no monitoring record. The percentage of White (3.65%) rtCGM adherent beneficiaries was significantly larger than Black (1.58%) and Hispanic (1.28%) beneficiaries, both P < 0.0001. Hospitalizations and costs were higher for Black and Hispanic beneficiaries versus Whites within the risk score ≥ 2 group regardless of glucose monitoring method. Conclusions: Race is associated with increased hospitalizations and costs associated with diabetes care and absence of any form of BGM was associated with higher rates of comorbidities. Persons of color were less likely to use rtCGM despite Medicare coverage. New initiatives that promote diabetes self-management education and support services are needed to improve utilization of glucose monitoring within the Medicare diabetes population.

Cost-Effectiveness of a Real-Time Continuous Glucose Monitoring System Versus Self-Monitoring of Blood Glucose in People with Type 2 Diabetes on Insulin Therapy in the UK.

INTRODUCTION: Real-time continuous glucose monitoring (rt-CGM) involves the measurement and display of glucose concentrations, potentially improving glucose control among insulin-treated patients with type 2 diabetes (T2D). The present analysis aimed to conduct a cost-effectiveness analysis of rt-CGM versus self-monitoring of blood glucose (SMBG) based on a USA retrospective cohort study in insulin-treated people with T2D adapted to the UK. METHODS: Long-term costs and clinical outcomes were estimated using the CORE Diabetes Model, with clinical input data sourced from a retrospective cohort study. Patients were assumed to have a baseline glycated hemoglobin (HbA1c) of 8.3%. Patients using rt-CGM were assumed to have a 0.56% reduction in HbA1c based on the mean difference between groups after 12 months of follow-up. Reduced fingerstick testing when using rt-CGM was associated with a quality of life (QoL) benefit. The analysis was performed over a lifetime time horizon from a National Health Service (NHS) perspective, including only direct costs from published data. Future costs and clinical outcomes were discounted at 3.5% per annum. Extensive sensitivity analyses were performed. RESULTS: Projections showed that rt-CGM was associated with increased quality-adjusted life expectancy of 0.731 quality-adjusted life years (QALYs) and increased mean total lifetime costs of Great British pounds (GBP) 2694, and an incremental cost-effectiveness ratio of GBP 3684 per QALY compared with SMBG. Key drivers of outcomes included HbA1c reduction and reduced fingerstick testing QoL benefit. CONCLUSIONS: Over patient lifetimes, rt-CGM was associated with improved clinical outcomes and is highly likely to be cost effective versus SMBG in people with T2D on insulin therapy in the UK.

Real-world glycaemic outcomes in adult persons with type 1 diabetes using a real-time continuous glucose monitor compared to an intermittently scanned glucose monitor: A retrospective observational study from the Canadian LMC diabetes registry (REAL-CGM-T1D).

Real-time continuous glucose monitoring (rtCGM) and intermittently scanned CGM (isCGM) have both been shown to improve glycaemic outcomes in people with T1D. The aim of this study was to compare real-world glycaemic outcomes at 6-12 months in a propensity score matched cohort of CGM naïve adults with T1D who initiated a rtCGM or an isCGM. Among the matched rtCGM and isCGM cohorts (n = 143/cohort), rtCGM users had a significantly greater HbA1c benefit compared to isCGM users (adjusted difference, -3 mmol/mol [95% CI, -5 to -1]; -0.3% [95% CI, -0.5 to -0.1]; p = 0.01). There was a significantly greater lowering of HbA1c for rtCGM compared to isCGM when baseline HbA1c was <69 mmol/mol (8.5%) (adjusted difference, -4 mmol/mol [95% CI, -7 mmol/mol to -2 mmol/mol]; -0.4% [95% CI, -0.6% to -0.2%]; p < 0.001), and in MDI users (adjusted difference, -3 mmol/mol [95% CI, -6 mmol/mol to -0 mmol/mol]; -0.3% [95% CI -0.5% to 0.0%], p = 0.04). The rtCGM cohort had significantly greater time in range (58.3 ± 16.1% vs. 54.5 ± 17.1%, p = 0.03), lower time below range (2.1 ± 2.7% vs. 6.1 ± 5.0%, p < 0.001) and lower glycaemic variability compared to the isCGM cohort. In this real-world analysis of adults with T1D, rtCGM users had a significantly greater reduction in HbA1c at 6-12 months compared to isCGM, and significantly greater time in range, lower time below range and lower glycaemic variability, compared to a matched cohort of isCGM users.

Association Between Real-Time Continuous Glucose Monitor Use and Diabetes-Related Medical Costs for Patients with Type 2 Diabetes.

Little is known about the impact of real-time continuous glucose monitoring (rtCGM) on diabetes-related medical costs within the type 2 diabetes (T2D) population. A retrospective analysis of administrative claims data from the Optum Research Database was conducted. Changes in diabetes-related health care resource utilization costs were expressed as per-patient-per-month (PPPM) costs. A total of 571 T2D patients (90% insulin treated) met study inclusion criteria. Average PPPM for diabetes-related medical costs decreased by -$424 (95% confidence interval [CI] -$816 to -$31, P = 0.035) after initiating rtCGM. These reductions were driven, in part, by reductions in diabetes-related inpatient medical costs: -$358 (95% CI -$706 to -$10, P = 0.044). Inpatient hospital admissions were reduced on average -0.006 PPPM (P = 0.057) and total hospital days were reduced an average of -0.042 PPPM (P = 0.139). These findings provide real-world evidence that rtCGM use was associated with diabetes-related health care resource utilization cost reductions in patients with T2D.

A clinical trial comparing trauma-informed guilt reduction therapy (TrIGR), a brief intervention for trauma-related guilt, to supportive care therapy.

INTRODUCTION: Trauma-related guilt is common, associated with posttraumatic mental health problems, and can persist after posttraumatic stress disorder (PTSD) treatment. We compared the efficacy of two six-session psychotherapies, Trauma-Informed Guilt Reduction (TrIGR) and Supportive Care Therapy (SCT), for reducing trauma-related guilt. TrIGR helps patients accurately appraise their role in the trauma and re-engage in values. In SCT, patients guide session content. METHODS: A total of 184 veterans seeking VA mental health services were enrolled across two sites; 145 veterans (mean age: 39.2 [8.1]; 92.4% male; 84.8% with PTSD) who endorsed guilt related to a traumatic event that occurred during a post 9/11 Iraq or Afghanistan deployment were randomized and assessed at baseline, posttreatment, 3- and 6-month follow-up. RESULTS: Linear mixed models using intent-to-treat analyses showed guilt decreased in both conditions with a greater decrease for TrIGR (treatment × time, -0.22; F 1, 455.2 = 18.49, p = .001; d = 0.92) than supportive therapy. PTSD and depressive symptoms showed the same pattern. TrIGR had significantly higher likelihood of PTSD treatment response (67% vs. 40%), loss of PTSD diagnosis (50% vs. 14%), and meaningful change in depression (54% vs. 27%) than supportive therapy. Psychological distress and trait shame improved in both conditions. Quality of life did not change. CONCLUSIONS: Targeting guilt appears to be an effective means for reducing posttraumatic symptoms and distress.

Subgroups of comorbid PTSD and AUD in U.S. military veterans predict differential responsiveness to two integrated treatments: A latent class analysis.

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) frequently co-occur. Integrated treatments are effective, but not all patients respond and predicting outcome remains difficult. In this study, latent class analysis (LCA) identified symptom-based subgroups of comorbid PTSD/AUD among 119 veterans with PTSD/AUD from a randomized controlled trial of integrated exposure therapy (I-PE) versus integrated coping skills therapy (I-CS). Multilevel models compared subgroups on PTSD severity and percentage of heavy drinking days at post-treatment and 3- and 6-month follow-up. LCA revealed three subgroups best fit the data: Moderate PTSD/Low AUD Impairment (21%), High PTSD/High AUD Impairment (48%), and Low PTSD/High AUD Impairment (31%). There was a three-way interaction between time, treatment condition, and subgroup in predicting PTSD outcomes (p < .05). For the Moderate PTSD/Low AUD Impairment class, outcomes at post-treatment and 3-months were similar (ds = 0.17, 0.55), however I-PE showed greater reductions at 6-months (d = 1.36). For the High PTSD/High AUD Impairment class, I-PE demonstrated better post-treatment (d = 0.83) but comparable follow-up (ds = -0.18, 0.49) outcomes. For the Low PTSD/High AUD Impairment class, I-PE demonstrated stronger outcomes at every timepoint (ds = 0.82-1.15). Heavy drinking days declined significantly through follow-up, with an effect of subgroup, but not treatment, on timing of response. This was the first study modeling how PTSD and AUD symptoms might cluster together in a treatment sample of veterans with PTSD/AUD. Symptom-based subgroups show promise in helping understand variability in treatment response among patients with PTSD/AUD and deserve further study.

Impact of COVID-19 on Health Economics and Technology of Diabetes Care: Use Cases of Real-Time Continuous Glucose Monitoring to Transform Health Care During a Global Pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has exposed vulnerabilities and placed tremendous financial pressure on nearly all aspects of the U.S. health care system. Diabetes care is an example of the confluence of the pandemic and heightened importance of technology in changing care delivery. It has been estimated the added total direct U.S. medical cost burden due to COVID-19 to range between $160B (20% of the population infected) and $650B (80% of the population infected) over the course of the pandemic. The corresponding range for the population with diabetes is between $16B and $65B, representing between 5% and 20% of overall diabetes expenditure in the United States. We examine the evidence to support allocating part of this added spend to infrastructure capabilities to accelerate remote monitoring and management of diabetes. Methods and Results: We reviewed recent topical literature and COVID-19-related analyses in the public health, health technology, and health economics fields in addition to databases and surveys from government sources and the private sector. We summarized findings on use cases for real-time continuous glucose monitoring in the community, for telehealth, and in the hospital setting to highlight the successes and challenges of accelerating the adoption of a digital technology out of necessity during the pandemic and beyond. Conclusions: One critical and lasting consequence of the pandemic will be the accelerated adoption of digital technology in health care delivery. We conclude by discussing ways in which the changes wrought by COVID-19 from a health care, policy, and economics perspective can add value and are likely to endure postpandemic.