Real-World Treatment Patterns of Glucose-Lowering Agents Among Patients with Type 2 Diabetes Mellitus and Cardiovascular Disease or At Risk for Cardiovascular Disease: An Observational, Cross-Sectional, Retrospective Study.

INTRODUCTION: There is limited published literature on longitudinal utilization of glucose-lowering agents (GLAs) among patients with type 2 diabetes (T2D) and cardiovascular disease (CVD or risk of CVD). This retrospective, observational study aimed to provide updated evidence on patient characteristics and utilization of GLAs among patients with T2D and CVD or risk of CVD in the United States. METHODS: This was a cross-sectional evaluation of patients with T2D aged 50-89 years with annual continuous enrolment in a Medicare Advantage and Prescription Drug plan, identified from administrative claims data (Humana Research Database). Patients with T2D and atherosclerotic cardiovascular disease (ASCVD) or heart failure (HF) (CVD cohort), or T2D and an additional CVD risk factor without pre-existing CVD (CVD risk cohort) were identified from 2015 to 2019. Patients were followed from their first observed ASCVD/HF diagnosis or CVD risk factor for each year they were continuously enrolled or until occurrence of a CVD diagnosis (CVD risk cohort only). Use of GLA classes were reported by year, cohort, and age groups (50-64 years and ≥ 65 years). RESULTS: The percentage of patients on sodium-glucose co-transporter-2 inhibitors (SGLT-2is), glucagon-like peptide-1 receptor agonists (GLP-1 RAs), and GLP-1 RAs with proven cardiovascular benefit, respectively, increased from 2015 to 2019 among ≥ 65 years (CVD cohort: 1.1-3.4%, 1.6-4.0%, and 1.2-3.8%; CVD risk cohort: 1.4-3.7%, 2.0-4.3%, and 1.5-4.1%); and among 50-64 years (CVD cohort: 2.6-7.3%, 4.3-10.1%, and 3.4-9.4%; CVD risk cohort: 3.3-6.8%, 4.6-9.6%, and 3.5-8.9%). CONCLUSIONS: Although use of SGLT-2is and GLP-1 RAs increased over time, overall utilization of these agents in patients with T2D and ASCVD/HF or at risk for ASCVD/HF remained low, especially for those aged ≥ 65 years.

Sodium-glucose co-transporter-2 inhibitors (SGLT-2is) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are types of glucose-lowering medications for patients with type 2 diabetes (T2D). The American Diabetes Association, the American Association of Clinical Endocrinologists, and American College of Endocrinology have recommended these medications for patients who have been diagnosed with T2D and atherosclerotic cardiovascular disease or heart failure (ASCVD/HF). The purpose of this study was to find out how many patients in a US-based health insurance population with T2D and ASCVD/HF were treated with SGLT-2is, GLP-1 RAs, and other glucose-lowering medications from 2015 to 2019. Using insurance claims data, we identified 50- to 89-year-old patients with T2D and either ASCVD/HF or at least one risk factor for ASCVD/HF. We tracked the number of patients with T2D and either ASCVD/HF or ASCVD/HF risk factors who were using different glucose-lowering medications. Glucose-lowering medications were used in most patients (60­78%), but fewer than 11% of patients aged 50­64 years, and fewer than 5% of patients over 65 years of age were prescribed SGLT-2i and GLP-1 RA medications, despite clinical guidelines recommending their use for the above-mentioned indications. Increasing awareness among healthcare providers may be required to ensure patients with T2D and ASCVD/HF or ASCVD/HF risk factors are prescribed the guideline-recommended cardioprotective glucose-lowering agents.

Cardiovascular risk prediction model and stratification in patients with type 2 diabetes enrolled in a Medicare Advantage plan.

BACKGROUND: Cardiovascular (CV) risk tools have been developed both nationally and internationally to identify patients at risk for developing CV disease or experiencing a CV event. However, these tools vary widely in the definitions of endpoints, the time at which the endpoints are measured, patient populations, and their validity. The primary limitation of some of the most commonly utilized tools is the lack of specificity for a type 2 diabetes (T2D) population and/or among older patients. OBJECTIVE: To develop a predictive model within an older population of patients with T2D to identify patients at risk for CV events. METHODS: This retrospective cohort study used claims, laboratory, and enrollment data during the 2011-2018 study period. Patients with T2D were identified based on diagnoses and/or medications from 2012-2013. The patient cohort was split into 3 different datasets. The holdout dataset included only those patients residing in the northeastern United States. The rest of the sample was then randomly split: 70% for the training dataset, which were used to fit the predictive model, and 30% for the test dataset to assess internal validity. The primary outcome was the first composite CV event defined as at least 1 of the following: inpatient hospitalization for myocardial infarction, ischemic stroke, unstable angina, or heart failure; or any evidence of revascularization. A survival model for the composite outcome was fitted with baseline demographic and clinical characteristics prognostic for the dependent variable utilizing augmented backwards elimination. For assessing model performance, accuracy, sensitivity, specificity, and the c-statistic were used. Patients were ranked as having a low, moderate, or high probability of a future CV event. RESULTS: A total of 362,791 patients were identified. The holdout dataset included only those patients residing in the northeastern United States (n = 8,303). There were 248,142 patients included in the training dataset and 106,346 patients in the test dataset. The proportion with at least 1 observed composite CV event was 20.9%. The final model included 42 variables. The c-statistic was 0.68, and the accuracy, sensitivity, and specificity were approximately 63%. Results were consistent across the training, test, and holdout samples. The optimal cut points minimizing the difference in sensitivity and specificity for low-, moderate-, and high-risk future CV events were determined to be less than 0.18, 0.18-0.63, and greater than 0.63, respectively, in the training dataset at 5 years. The 5-year observed event risk was 11%, 27%, and 51% for patients classified as low, moderate, and high risk of a future CV event, respectively. CONCLUSIONS: A model predicting CV events among older patients with T2D using administrative claims to identify those at risk may be used for focusing interventions to prevent future events. DISCLOSURES: This study was funded by Boehringer Ingelheim (BI) and conducted as part of the BI-Humana Research Collaboration. Caplan is employed by Humana Healthcare Research, Inc., a wholly owned subsidiary of Humana Inc., which received fees to conduct the study from the sponsor BI. At the time of the study, Hayden and Harvey were employees of Humana Healthcare Research, Inc. Additionally, Prewitt, who owns stock in Humana Inc, and Chiguluri are employees of Humana Inc. Kattan, associated with the Cleveland Clinic in Ohio, served as a consultant to BI, and Pimple and Goss are employees of BI. Luthra was employed by BI for the duration of the study. Portions of this work were accepted as an abstract and presented as a poster at the American Diabetes Association 2020 virtual meeting, June 12-16, 2020.

Health care resource utilization among patients with T2D and cardiovascular-, heart failure-, or renal-related hospitalizations.

OBJECTIVES: In patients with type 2 diabetes (T2D), comorbidity-related hospitalizations can have significant impact on longitudinal care. This study aimed to estimate incremental all-cause health care resource utilization (HCRU) and costs between patients with T2D who experienced cardiovascular (CV)-, heart failure (HF)-, or renal-related hospitalizations vs those who did not. STUDY DESIGN: This was a retrospective cohort study using data from a large national health plan. METHODS: Patients with T2D aged 18 to 90 years with CV, HF, or renal hospitalizations were identified from the Humana claims database from October 1, 2009, to September 30, 2015, and separated into CV, HF, and renal cohorts. Patients had 12 months of continuous enrollment prior to the date of first hospitalization (index) and were followed for up to 12 months. Per-patient per-month (PPPM) all-cause HCRU and costs for hospitalized patients were compared with those of no-CV, no-HF, and no-renal cohorts. Differences in baseline characteristics between cohorts were controlled for using generalized linear models. RESULTS: A total of 221,229, 68,126, and 120,105 patients were included in the CV, HF, and renal cohorts, respectively; these patients were older and had higher Deyo-Charlson Comorbidity Index scores than patients in the no-CV, no-HF, and no-renal cohorts. Adjusted for baseline covariates, they had higher mean PPPM inpatient stays, outpatient visits, emergency department visits, and total health care costs. CONCLUSIONS: Among patients with T2D, concurrent CV, HF, or renal events present significant disease burden leading to poor quality of life. This information can be used to guide disease management strategies and interventions aimed at reducing comorbidity-related hospitalizations and health care costs, thus providing improved quality of life for these patients.

Health Care Utilization and Medical Cost Outcomes from a Digital Diabetes Prevention Program in a Medicare Advantage Population.

This study examined the effects of a digital diabetes prevention program (DPP) on health care costs and utilization among Medicare Advantage participants. Patients (n = 501) received access to a plan-sponsored, digitally-delivered DPP accessible through computer, tablet, or smartphone. Prior research demonstrated a 7.5% reduction in body weight at 12 months. A comparison group who did not participate in the DPP was constructed by matching on demographic, health plan, health status, and health care costs and utilization. The authors assessed effects on cost and utilization outcomes using difference-in-differences regressions, controlling for propensities to participate and engage in the DPP, in the 12 months prior to DPP enrollment and 24 months after. Though post-enrollment data showed trends in decreased drug spending and emergency department use, increased inpatient utilization, and no change in total nondrug costs or outpatient utilization, the findings did not reach statistical significance, potentially because of sample size. The population had low costs and utilization at baseline, which may be responsible for the lack of observed effects in the short time frame. This study demonstrates the challenges of studying the effectiveness of preventive programs in a population with low baseline costs and the importance of using a large enough sample and follow-up period, but remains an important contribution to exploring the effects of digital DPPs in a real-world sample of individuals who were eligible and willing to participate.

Understanding Prediabetes in a Medicare Advantage Population Using Data Adaptive Techniques.

The objective was to identify individuals with undiagnosed prediabetes from administrative data using adaptive techniques. The data source was a national Medicare Advantage Prescription Drug (MAPD) plan administrative data set. A retrospective, cross-sectional study developed and evaluated data adaptive logistic regression, decision tree, neural network, and ensemble predictive models for metabolic syndrome and prediabetes using 3 mutually exclusive cohorts (N = 279,903). The misclassification rate (MCR), average squared error (ASE), c-statistics, sensitivity (SN), and false positive (FP) rates were compared to select the final predictive models. MAPD individuals with continuous enrollment from 2013 to 2014 were included. Metabolic syndrome and prediabetes were defined using clinical guidelines, diagnosis, and laboratory data. A total of 512 variables identified through subject matter expertise in addition to utilizing all data available were evaluated for the modeling. The ensemble model demonstrated better discrimination (c-statistics, MCR, and ASE of 0.83, 0.24, and 0.16, respectively), high SN, and low FP rate in predicting metabolic syndrome than the individual data adaptive modeling techniques. Logistic regression demonstrated better discrimination (c-statistics, MCR, and ASE of 0.67, 0.13, and 0.11 respectively), high SN, and low FP rate in predicting prediabetes than the other adaptive modeling techniques or ensemble methods. The scored data predicted prediabetes in 44% of the MAPD population, which is comparable to 2005-2006 National Health and Nutrition Examination Survey prediabetes rates of 41%. The logistic regression model demonstrated good performance in predicting undiagnosed prediabetes in MAPD individuals.

Outcomes of a Digital Health Program With Human Coaching for Diabetes Risk Reduction in a Medicare Population.

OBJECTIVE: To examine the outcomes of a Medicare population who participated in a program combining digital health with human coaching for diabetes risk reduction. METHOD: People at risk for diabetes enrolled in a program combining digital health with human coaching. Participation and health outcomes were examined at 16 weeks and 6 and 12 months. RESULTS: A total of 501 participants enrolled; 92% completed at least nine of 16 core lessons. Participants averaged 19 of 31 possible opportunities for weekly program engagement. At 12 months, participants lost 7.5% ( SD = 7.8%) of initial body weight; among participants with clinical data, glucose control improved (glycosylated hemoglobin [HbA1c] change = -0.14%, p = .001) and total cholesterol decreased (-7.08 mg/dL, p = .008). Self-reported well-being, depression, and self-care improved ( p < .0001). DISCUSSION: This Medicare population demonstrated sustained program engagement and improved weight, health, and well-being. The findings support digital programs with human coaching for reducing chronic disease risk among older adults.

Comorbid Arthritis Is Associated With Lower Health-Related Quality of Life in Older Adults With Other Chronic Conditions, United States, 2013-2014.

INTRODUCTION: Arthritis is related to poor health-related quality of life (HRQoL) in adults aged 18 years or older. We sought to determine whether this relationship persisted in an older population using claims-based arthritis diagnoses and whether people who also had arthritis and at least 1 of 5 other chronic conditions had lower HRQoL. METHODS: We identified adults aged 65 years or older with Medicare Advantage coverage in November or December 2014 who responded to an HRQoL survey (Healthy Days). For respondents with and without arthritis, we used linear regression to compare mean physically, mentally, and total unhealthy days, overall and in 5 comorbidity subgroups (coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, diabetes, and hypertension), accounting for age, sex, dual Medicaid/Medicare eligibility, rural/urban commuting area, and Charlson Comorbidity Index. RESULTS: Of the 58,975 survey respondents, 44% had arthritis diagnosed through claims. Respondents with arthritis reported significantly more adjusted mean physically, mentally, and total unhealthy days than those without arthritis (P < .001). Older adults with arthritis and either congestive heart failure, chronic obstructive pulmonary disease, diabetes, or hypertension reported significantly more adjusted physically, mentally, and total unhealthy days than older adults without arthritis but with the same chronic conditions. CONCLUSIONS: In older adults, having arthritis is associated with lower HRQoL and even lower HRQoL among those with at least 1 of 5 other common chronic conditions. Because arthritis is so common among older adults, improving HRQoL depends on managing both underlying chronic conditions and any accompanying arthritis.

Predictors of all-cause 30 day readmission among Medicare patients with type 2 diabetes.

OBJECTIVE: Readmission is costly among patients with type 2 diabetes (T2DM) in Medicare Advantage Prescription Drug Plans; identifying high-risk patients is necessary for targeting reduction programs. The objective of this study was to develop a claims-based algorithm to predict all-cause 30 day readmission among patients with T2DM. METHODS: This study used administrative data from 1 January 2012 through 31 January 2014. The cohort included hospitalized T2DM patients, aged 18-90 with ≥12 months' continuous enrollment before an unplanned hospital admission and ≥1 month of enrollment post-discharge, excluding patients in long-term care >30 days pre-index. Multivariate logistic regression predicted the likelihood of readmission following hospitalization in 2013. The analytic file was randomly split into training and test datasets to build and validate the model. Candidate variables included physician and patient demographics, baseline clinical conditions, and healthcare utilization metrics. Clinical conditions were classified using the Healthcare Cost and Utilization Project clinical classification system for ICD-9-CM. RESULTS: Of 63,237 individuals, 17.1% experienced a readmission. Of nearly 200 candidate variables, 14 were predictors of readmission, including total cumulative number of days for inpatient stays and the number of emergency department visits in the baseline period. Male gender, older age, and certain comorbidities were associated with higher likelihood of readmission. The final model demonstrated good discriminant ability (c-statistic = 0.82). CONCLUSIONS: This study provided evidence that certain patient characteristics and healthcare utilization are predictive of readmission. An algorithm with good discriminant ability was developed which could be used to target readmission reduction programs. Physician gender, specialty, and ownership status did not appear to influence the likelihood of readmission.

Leveraging Health-Related Quality of Life in Population Health Management: The Case for Healthy Days.

Measuring population health with morbidity and mortality data, often collected at the site of care, fails to capture the individual's perspective on health and well-being. Because health happens outside the walls of medical facilities, a holistic and singular measure of health that can easily be captured for an entire population could aid in understanding the well-being of communities. This paper postulates that Healthy Days, a health-related quality of life measure developed and validated by the Centers for Disease Control and Prevention, is an ideal survey instrument to advance population health. A systematic literature review was conducted and revealed a strong evidence base using Healthy Days with significant correlations to chronic disease conditions. Building on the literature base and experience, methods for analyzing Healthy Days data are discussed, including stratified sampling techniques, statistical measures to account for variance, and modeling techniques for skewed distributions. Using such analytic techniques, Healthy Days has been used extensively in national health surveillance. As the health care system faces increasing costs and constrained resources, the Healthy Days survey instrument can be used to inform public policies and allocate health service resources. Because Healthy Days captures broad dimensions of health from the individual's perspective, it is a simple way to holistically measure the health and well-being of a population and its trend over time. Expanded use of Healthy Days can aid population health managers and contribute to the understanding of the broader determinants of the nation's and individual community's health and aid in evaluating progress toward health goals.