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The objective of this study was to describe a predictive modeling approach to risk stratify people with type 2 diabetes for diabetes self-management education and support (DSMES) services. With data from a large health system, a predictive model including age, glycated hemoglobin (HbA1c), and insulin use among other factors, was developed to assess risk of future high HbA1c. The model was retrospectively applied to a cohort of people who received DSMES over a 2-year period to assess the impact of DSMES on glycemia by risk strata. Of 6934 eligible people, 4014 (58%) were in the composite low-risk group and 2604 (38%) were in the composite high-risk group. Mean HbA1c change after DSMES was -0.38% in the low-risk group and -0.84% in the high-risk group. This analysis demonstrates the potential application of predictive modeling as one approach to target DSMES resources to people who will benefit most.

Objectives To assess what patient, family supporter, and call characteristics predicted whether patients completed automated and coach-provided calls in a telehealth diabetes intervention. Study design A total of 123 adults with type 2 diabetes and high glycated hemoglobin A1c (HbA1c) or blood pressure, enrolled with a family supporter, received automated interactive voice response (IVR) and coach-provided visit preparation calls over 12 months. Methods Data from baseline surveys and diabetes-related clinical information from patient medical records were entered into multilevel, multivariate regression models of associations between participant and call characteristics with call completion. Results A total of 76.3% of 2784 IVR calls and 75.8% of 367 visit preparation calls were completed. For IVR calls, patients with recent call-triggered provider alerts had higher odds of call completion (adjusted odds ratio [AOR], 3.5; 95% CI, 2.2-5.5); those with depressive symptoms (AOR, 0.4; 95% CI, 0.2-0.9), higher HbA1c (AOR, 0.8; 95% CI, 0.6-0.99), and more months in the study (AOR, 0.9; 95% CI, 0.87-0.94 per month) had lower odds. For visit preparation calls, higher patient activation scores predicted higher call completion (AOR, 1.4; 95% CI, 1.1-1.9); patient college education predicted less call completion (AOR, 0.3; 95% CI, 0.2-0.6). Supporter help taking medications predicted less completion of both call types. Patient age did not predict call completion. Conclusions Patients of all ages completed telehealth calls at a high rate. Automated IVR calls were completed more often when urgent issues were identified to patients' providers, but less often if patients had high HbA1c or depression. Visit preparation call content should be tailored to patient education level. Family help with medications may identify patients needing additional support to engage with telehealth.

Aim: Perform a cost–effectiveness analysis of addition of subcutaneous semaglutide versus empagliflozin to usual treatment for patients with Type 2 diabetes and cardiovascular disease in US setting. Materials & methods: A Markov decision model estimated the impact of each strategy using cardiovascular complication rates based on EMPA-REG and SUSTAIN-6 trials. Modeled cohorts were followed for 3 years at 1-month intervals beginning at age 66. Results: Compared with empagliflozin, semaglutide resulted in cost of US$19,964 per quality-adjusted life-year gained. In one-way sensitivity analysis, only semaglutide cost >US$36.25/day (base case US$18.04) resulted in empagliflozin being preferred at a willingness-to-pay threshold of US$50,000/quality-adjusted life-year gained. Conclusion: For patients with Type 2 diabetes and cardiovascular disease, semaglutide is likely more cost-effective than empagliflozin added to usual treatment.

The ADA Standards of Care recommend that all patients with diabetes at risk of hypoglycemia have glucagon available for emergency use. However, glucagon prescriptions are filled by only a small minority of patients with type 1 and type 2 diabetes in the United States each year. While there are multiple barriers to glucagon use, the extent to which out-of-pocket medication costs contributes to low uptake is not clear. Using administrative claims from Optum’s de-identified Clinformatics® Data Mart Database (2007-2019) for adults with commercial or Medicaid insurance from 2010-2020, we sought to describe the trends in out-of-pocket costs for glucagon over time. Analyzed claims included all fills of glucagon, based on National Drug Codes, from 20through 9/30/2020. Our primary outcome was total out-of-pocket costs defined as the sum of copays and deductibles. Costs were adjusted for inflation based on 2020 dollars and scaled per single-use kit. Insurance type was based on the primary payor at the time of the glucagon fill. The median (IQR) out-of-pocket cost of unmixed glucagon was $27.42 (15.09-37.98) in 2010, $33.58 (5.60-41.97) in 2015, and $30.42 (15.21-40.56) in 2020 for commercially insured beneficiaries. For Medicare beneficiaries, cost for unmixed glucagon was $0 (0-25.35) in 2010, $0 (0-33.58) in 2015, and $0 (0-40.56) in 2020. After introduction in 2019, the 2020 out-of-pocket cost of intranasal glucagon was $15.21 (7.60-35.49) for commercially insured beneficiaries and $10.14 (0.99-23.83) for Medicare beneficiaries. Out-of-pocket costs for unmixed glucagon was stable for most commercially insured patients, and $0 for most Medicare beneficiaries from 20through 2020; therefore, cost should not serve as the primary barrier to accessing glucagon for most patients. However, for the subset of patients (4th quartile) with the highest out-of-pocket costs in both insurance groups, the cost of unmixed glucagon has increased over time. Novel intranasal glucagon had lower out-of-pocket cost compared to unmixed glucagon for most commercially insured patients immediately after introduction. Disclosure M.Zupa: None. R.Feldman: None. J.Luo: None. Funding NCATS KL2 (TR001856)

Background: Support from care partners (CPs) , family or friends who are involved in diabetes management, is associated with improved self-management and clinical outcomes for adults with type 2 diabetes (T2D) . Therefore, guidelines recommend active CP involvement in diabetes self-management education and support (DSMES) . Telemedicine is increasingly being used to deliver DSMES and may facilitate CP involvement. Objective: To examine the frequency and type of CP involvement in virtual DSMES sessions delivered to patients in their homes over 12 months as part of the Telemedicine for Reach, Education, Access, Treatment, and Ongoing Support study, designed to evaluate the impact of longitudinal DSMES conducted via videoconferencing. Methods: 43 patients with T2D and an HbA1c>9% were enrolled from rural federally qualified health centers. A diabetes care and education specialist (DCES) performed an initial in-person needs assessment, followed by individualized virtual DSMES sessions via videoconferencing over 12 months. Participants were surveyed on the presence and roles of CPs at baseline, and DCES recorded CP involvement at each DSMES session. Twenty participants identified a diabetes CP at baseline; 75% lived with their CP and 60% of CPs were the patient’s significant other. Patients reported that CPs regularly helped with self-management by encouraging healthy eating (80%) , medication adherence (70%) , and discussing home blood glucose results (70%) . Nine participants had a CP involved in at least one virtual DSMES session; CPs performed diverse functions, including assisting with technology, setting up continuous glucose monitors, reviewing nutrition recommendations, and facilitating information sharing between participants and DCES. Conclusions and Future Direction: For adults with T2D, virtual visits offer opportunities for CPs to support and enhance DSMES. Efforts to more actively engage existing CPs could better leverage this resource to support ongoing diabetes self-management. Disclosure M.Zupa: None. P.A.Johnson: None. J.S.Krall: Research Support; Becton, Dickinson and Company, Sanofi. J.Ng: Research Support; Sanofi. S.L.Reynolds: None. A.Rosland: None. L.M.Siminerio: Advisory Panel; Abbott, Bayer AG, Research Support; Becton, Dickinson and Company, Sanofi. Funding NIDDK (R34DK123370) NCATS (TR001856)

Background: Efforts to reduce type 2 diabetes morbidity and mortality focus on reducing microvascular and macrovascular complications. Development of GLP-1 receptor agonists and SGLT2 inhibitors has furthered progress toward this goal. As these medication classes have distinct effects on complication risk, the optimal approach is not clear. Methods: We evaluated the cost-effectiveness of 2 strategies to intensify treatment of patients with type 2 diabetes and cardiovascular disease: addition of 1 mg subcutaneous semaglutide weekly or 25 mg oral empagliflozin daily to usual diabetes treatment. A Markov decision model estimated the impact of each strategy; complications included nonfatal myocardial infarction, hospitalization for congestive heart failure, nonfatal stroke, new or worsening nephropathy, and death. The empagliflozin strategy also included possible genital mycotic infection. Complication rates were based on EMPA-REG and SUSTAIN-6 trials. Modeled cohorts began at age 66 and were followed for 3 years at 1 month intervals. Analyses took a healthcare perspective, discounting 3%/year. For the base case, all patients entered the model with no complications. Results: Compared to empagliflozin, semaglutide gained 0.053 quality-adjusted life year (QALY) at a cost of $1,006, or $18,897 per QALY gained. In one-way sensitivity analysis, only a semaglutide cost >$38/day (base case $18.04) resulted in empagliflozin being preferred at a willingness-to-pay threshold of $50,000/QALY. In probabilistic sensitivity analysis, semaglutide was preferred in 55% of model iterations at a $50,000/QALY threshold and in 57% at $100,000/QALY. When patients entered the model in complication states based on trial cohorts, semaglutide remained the preferred strategy at $23,518 per QALY gained. Conclusion: For patients with type 2 diabetes mellitus with cardiovascular disease, semaglutide is likely more cost-effective than empagliflozin as an addition to usual treatment. Disclosure M. Zupa: None. R. Codario: None. K. J. Smith: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (5T32DK007052-45)

Diabetes self-management education and support (DSMES) is known to improve diabetes treatment outcomes, but this limited resource is not always targeted toward the highest risk patients in current models of care. Predictive modeling, which relies on large data sets to predict outcomes, is one tool which can be used to direct resources to patients with the greatest need for these services. University of Pittsburgh Medical Center (UPMC) diabetes care providers along with members of the clinical analytics team created a predictive model, including patient age, HbA1c, and insulin use among other factors, to assess individual patient risk of future poor glycemic control. The model was applied to patients who received DSMES via diabetes care and education specialists (DCES) from December 2018 to December 2020 to evaluate the impact of DSMES according to risk strata. Patients were categorized into high, medium and low risk groups based on clinical data prior their initial DSMES encounter. HbA1c change was calculated using the HbA1c prior to DSMES and most recent HbA1c. Of 1,247 patients eligible for retrospective assessment, 50% were categorized as low risk (n=621), 4% (n=55) medium, and 46% high risk (n=571). Mean HbA1c change after DSMES was -0.54% for low risk, -0.72% for medium risk, and -1.19% for the high risk group. Mean HbA1c after DSMES was 7.6% for low risk, 9.7% for medium risk and 9.6% for high risk patients. 57.5% of low, 59% of medium and 46.6% of high risk patients were seen by an endocrinologist. In this analysis, high risk patients who received DSMES had larger improvement in HbA1c and were less likely to be seen by an endocrinologist. To optimize utilization of diabetes care regarding DCES, predictive modeling can be used to target resources to the highest risk patients. Disclosure M. Zupa: None. J. S. Krall: Research Support; Self; Becton, Dickinson and Company, Sanofi. K. Collins: None. J. Ng: Research Support; Self; Sanofi-Aventis. L. M. Siminerio: Advisory Panel; Self; Abbott Diabetes, Bayer U. S., Research Support; Self; Becton, Dickinson and Company. Funding National Institute of Diabetes and Digestive and Kidney Diseases (5T32DK007052-45)

Stay at home orders, changes in business and clinic operations, and financial concerns related to the COVID-19 pandemic may have affected how adults with diabetes obtained food, medications, medical care, and performed diabetes self-management. These impacts may be most pronounced among people in low-resource urban communities. We assessed the experiences of adults with type 2 diabetes participating in a federally-qualified health center diabetes management program. Of 72 patients called, 61 completed telephone surveys from July to October 2020 in Spanish and English. 74% of respondents were Latino with Spanish as their preferred language. Respondents’ mean age was 54 years, 62% were female, and 57% had less than a high school degree. Five patients reported testing positive for COVID-19, and 7 lived with someone who tested positive. Of 33 respondents who worked outside the home before COVID-19, 61% had lost their jobs or now worked fewer hours. When asked how diabetes management behaviors changed during the pandemic, 53% of respondents reported less physical activity, while 25% reported more. 18% reported eating less healthfully, while 38% reported eating more healthfully. 43% found it more difficult to obtain healthy food. 15% reported taking their medications more regularly, while 5% reported less. 61% reported increased difficulty accessing medical care. 41% felt more worried about being able to manage their diabetes during the pandemic and 49% felt more socially isolated. Changes in diabetes self-management behaviors related to COVID-19 were both positive and negative for patients in this low-resource urban setting. Programs supporting patients with diabetes could help patients adapt to pandemic changes by facilitating access to medications and health care, providing virtual support for self-management and other health habits, connecting patients with financial and food security concerns to community resources, and addressing stress and social isolation through virtual diabetes support groups. Disclosure M. Zupa: None. A. Rosland: None. S. Perez: None. G. Palmisano: None. E. C. Kieffer: None. G. Piatt: None. F. Valbuena: None. D. J. Deverts: None. J. Yabes: None. M. Heisler: None. Funding National Institutes of Health (R01DK116733)

Background Family support for adults’ diabetes care is associated with improved self-management and outcomes, but healthcare providers lack structured ways to engage those supporters. Objective Assess the impact of a patient-supporter diabetes management intervention on supporters’ engagement in patients’ diabetes care, support techniques, and caregiving experience. Design Multivariate regression models examined between-group differences in support-related measures observed as part of a larger trial randomizing participants to a dyadic intervention versus usual care. Participants A total of 239 adults with type 2 diabetes and either A1c >8% or systolic blood pressure >160mmHg enrolled with a family supporter. Intervention Health coaches provided training on positive support techniques and facilitated self-management information sharing and goal-setting. Main Measures Patient and supporter reports at baseline and 12 months of supporter roles in diabetes care and caregiving experience. Results At 12 months, intervention-assigned patients had higher odds of reporting increased supporter involvement in remembering medical appointments (AOR 2.74, 95% CI 1.44, 5.21), performing home testing (AOR 2.40, 95% CI 1.29, 4.46), accessing online portals (AOR 2.34, 95% CI 1.29, 4.30), deciding when to contact healthcare providers (AOR 2.12, 95% CI 1.15, 3.91), and refilling medications (AOR 2.10, 95% CI 1.14, 3.89), but not with attending medical appointments or with healthy eating and exercise. Intervention-assigned patients reported increased supporter use of autonomy supportive communication (+0.27 points on a 7-point scale, p=0.02) and goal-setting techniques (+0.30 points on a 5-point scale, p=0.01). There were no differences at 12 months in change scores measuring supporter distress about patients’ diabetes or caregiving burden. Intervention-assigned supporters had significantly larger increases in satisfaction with health system support for their role (+0.88 points on a 10-point scale, p=0.01). Conclusions A dyadic patient-supporter intervention led to increased family supporter involvement in diabetes self-management and increased use of positive support techniques, without increasing caregiver stress. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-06946-8.

Many adults with DM have a family supporter (FS) involved in their care. Informal support has been linked with better DM management and outcomes. We evaluated the effect of a patient and FS engagement intervention on FS roles in DM care, and the impact of those roles on HbA1c. Two-hundred and thirty-nine adult Veterans with type 2 DM and a FS were randomized to intervention vs. usual care for 12 months. Intervention dyads received one coaching session, biweekly automated calls to prompt action planning on new DM concerns, coaching calls to prepare for clinic visits, and summaries of primary care visits and DM complication risk. All models were controlled for baseline HbA1c, age, sex, insulin use, and FS cohabitation with patient. Over 12 months, participants in the intervention group were more likely to have increased involvement in several FS roles, including: deciding when to contact healthcare providers about concerns (AOR 2.080, 95% CI 1.137,3.804), remembering to go to appointments (AOR 2.852, 95% CI 1.503, 5.412), take (1.853, 95% CI 1.001, 3.428) and refill (AOR 2.193, 1.196, 4.019) medications, and perform (AOR 2.615, 95% CI 1.405, 4.867) and review (AOR 2.822, 95% CI 1.506, 5.290) home testing. Changes in help navigating the healthcare system, and encouraging healthy eating and exercise were not different by intervention arm. Help remembering to take medications at baseline was associated with 0.457% increase in HbA1c over 12 months (95% CI 0.107, 0.807, p=0.011), and no support roles were significantly associated with HbA1c decrease. In conclusion, this intervention increased FS involvement in diabetes-specific self-management, with less impact on general lifestyle support. Patients with support for taking medication prior to the intervention had significant subsequent increases in HbA1c. This may indicate family are recognizing which patients have challenges with medication taking, but need more training to effectively help improve subsequent patient outcomes. Disclosure M. Zupa: None. M. Heisler: None. J. Piette: None. R.B. Trivedi: None. S.C. Stoll: None. D. Obrosky: None. E.Z. Lovelace: None. M.K. Mor: None. A. Rosland: None. Funding VA Health Services Research and Development (IIR 14–074-1); Michigan Center for Diabetes Translational Research (5P60-DK09292); Michigan Claude D. Pepper Older Americans Independence Center (AG-024824); VA Ann Arbor Center for Clinical Management Research; University of Pittsburgh (5T32DK007052-45)

Purpose The purpose of this feasibility study was to determine the effectiveness of an insurer-based diabetes educator (DE)–driven intervention that relies on systematic restructuring of primary care (PC) linking DE services through population health, practice redesign, and coordinated care for patients with diabetes mellitus (DM) identified as high risk. Methods Two DEs were introduced as members of PC teams and worked with practice-based care managers (PBCMs) to identify and refer DM patients considered at high risk, A1C >9%, DM-related emergency room visit or hospitalization, or reported barriers to care. Elements shown to ensure quality, including population management, diabetes self-management education and support (DSMES), and coordinated patient-centered team-based PC, were central to intervention. A1C, low-density lipoprotein (LDL), and body mass index (BMI) were collected at baseline and outcomes were followed at 3, 6, 9, and 12 months after intervention. Results For patients who received intervention, A1C decreased on average 1.2% (95% confidence interval [CI], 0.8-1.5) from 9.6% (81 mmol/mol) to 8.4% (68 mmol/mol) over 6 months and by 1.1% (95% CI, 0.7-1.5) from 9.2% (77 mmol/mol) to 8.1% (65 mmol/mol) over 12 months, indicating durable improvement in glycemic control. There was no significant change in BMI, and LDL improvement observed at 9 months was lost by 12-month follow-up. Conclusion Findings support the feasibility of a DE-driven intervention for patients with DM at high risk through a coordinated PC approach that improves glycemic control. The feasibility and clinical outcome of this model warrant consideration of a fresh role for DEs in the complex environment of value-based care.

Factors linked to mortality after coronary artery bypass graft (CABG) include post-operative hypoglycemia and graft source. However, it is unknown if there is an interaction between these two factors, namely whether arterial grafts withstand hypoglycemia better than veins after CABG. In a cohort of 1323 patients undergoing CABG with intensive insulin therapy (IIT) in the ICU, we identified graft sources, ICU glucometrics, diabetes (DM) status and one-year mortality. Glucometrics included mean BG in ICU, and hypoglycemic excursions (Incidence, Severity [nadir 152mg/dl 4/17, 24% vs. ≤152 9/173, 5%, p=0.039) Preexisting DM was protective in patients with repetitive hypoglycemia (DM 2/43, 5% vs. 15/53 28%, p=0.003). Hypoglycemia in the ICU has a durable impact on one-year mortality post-CABG, superseding graft source, which is only significant without hypoglycemia. Severe hypoglycemia poses the greatest risk, but even mild repetitive BG In conclusion, hypoglycemia erases the survival benefit from arterial grafts, suggesting that it may be equally damaging, regardless of graft source, and far more durable than hitherto envisioned. Disclosure L. Heller: None. M. Zupa: None. P.L. Perreiah: None. R. Rao: None.