Introduction: Cardiovascular (CV) disease is a major cause of morbidity and mortality for people with diabetes. This study examines the annual medical costs of newly identified CV complications for ...adults with type 2 diabetes (T2D).
Methods: Optum’s de-identified Clinformatics® Data Mart data from 1/1/2015 - 6/30/2021 was used to examine a cohort of adults with T2D identified with a CV complication, with first such date identified as the index date. Descriptive statistics examined the direct medical costs (inpatient, emergency, and outpatient) associated with each complication in the 1st and 2nd year post index date. Complications included heart failure (HF), myocardial infarction (MI), stroke (STRK), hospitalization with a diagnosis of unstable angina (HUA), coronary revascularization (CR), and hypertension (HTN).
Results: There were 167,046 patients with an average of 1.2 conditions of interest each. HTN (12.3%), STRK (12.1%), and HF (10.6%) were the most common complications. In the 1st year post index date, HUA, MI, and HF were the most expensive complications. In the 2nd year post index date, costs of each complication ranged from 1.7% (CR) to 65.0% (HTN) of 1st year costs (see Figure).
Conclusion: Results illustrate the costs associated with CV-related complications for T2D and helps to quantify the economic burden that T2D imposes on U.S. healthcare systems.
Disclosure
J.P.Bae: None. V.Thieu: None. M.J.Lage: Consultant; Eli Lilly and Company. K.Boye: Employee; Eli Lilly and Company.
Introduction: The estimated economic costs of diabetes was $327B in 2017 and is forecasted to increase to $494B by 2030. This study compares total health care costs for adults with T2D with glycemic ...control (HbA1c < 7%) compared to poor glycemic control (HbA1c ≥ 7%).
Methods: The Optum’s de-identified Clinformatics® Data Mart database from 1/1/2015 - 6/30/2021 was used to identify a cohort of adults with T2D with an index date of first recorded HbA1c test between 1/1/2016 - 7/1/2020 and continuous insurance from 1 year prior through 1 year post index date. Patients with glycemic control were propensity score matched to patients with poor glycemic control. Generalized linear models and two-part models examined all-cause total medical costs in 2021 dollars as well as outpatient, drug, and acute care costs over the 1- year post-period.
Results: Results from the matched cohort (N=59,830) indicate that glycemic control was associated with significantly lower annual total, acute, and drug costs but significantly higher outpatient costs (see Figure 1; all P<0.05). Sensitivity analyses using ≤ 6.5% and < 8% HbA1c thresholds showed that being below threshold was associated with significantly lower total medical costs.
Conclusion: Glycemic control was associated with significantly lower annual all-cause total medical costs. Results suggest economic benefits associated with having HbA1c at or below target.
Disclosure
J.P.Bae: None. V.Thieu: None. M.J.Lage: Consultant; Eli Lilly and Company. K.Boye: Employee; Eli Lilly and Company.
Background: Newer treatment options for type 2 diabetes (T2D) and emerging evidence of treatment-specific benefits for ASCVD, CKD, heart failure (HF), hypoglycemia, and obesity provide opportunities ...for individualizing treatment choices. We used claims data to examine how T2D treatment patterns have changed in recent years as clinical trial data and updated guidelines have emerged.
Method: A retrospective observational study of T2D patients in a health insurance claims database (IBM® MarketScan® 2007 to 2019 Commercial and Medicare Databases). Medication use by class was examined from Jan 2015 through Dec 2019. Data since 2007 were used to establish disease history and categorize individuals according to the ADA/EASD Consensus Statement (2019): ASCVD, CKD, HF, hypoglycemia, and obesity defined risk groups. Alignment of risk group with treatment recommendation was evaluated by comparing use rates in the groups with and without risk. A GEE model was fit with guideline alignment as the dependent variable. The slope parameter and the area under the curve were calculated on the probability scale.
Results: Use of guideline-recommended therapies in the risk groups increased in both cohorts from 2015 to 2019. The Commercial cohort showed better alignment vs. the Medicare cohort over all years. Guideline-recommended therapy use in 2019 was highest in the Commercial CKD and obesity groups (39.2%; 37.6 %) with 5 year growth rates of 74.5% and 54.9%; and lowest in Medicare HF and CKD groups (16.6%; 18.5% ), but showing rapid 5 year growth (145.5% and 132.2% respectively). Use of SU in the hypoglycemia risk group decreased over time, but at comparable rates to those without the risk. The HF risk group was less likely to receive guideline recommended therapies vs. no HF risk.
Conclusion: The data suggest there is considerable room for improvement in individualizing therapies according to ADA risk grouping. Improving alignment through more targeted prescribing could further enhance outcomes without increasing overall use.
Disclosure
J. P. Bae: Employee; Self; Eli Lilly and Company. D. Liu: Employee; Self; Eli Lilly and Company. C. Chinthammit: Employee; Self; Eli Lilly and Company. Z. A. Kadziola: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. K. Boye: Employee; Self; Eli Lilly and Company, Employee; Spouse/Partner; Eli Lilly and Company. K. J. Mather: Employee; Self; Eli Lilly and Company.
Background: The ADA Pharmacotherapy Guidelines for type 2 diabetes (T2D) recommend patient-centric prescribing based on individual characteristics such as prevalent ASCVD, CKD, heart failure (HF), ...hypoglycemia (hypo), and obesity. Method: Analysis of health care claims in patients with T2D (IBM® MarketScan® 2007 to 2019 Commercial com and Medicaremed Supplemental Databases). Prescription claims were evaluated for the year 2019 (N=580,741). Claims since 2007 characterized patient history. Patients were categorized into risk/no risk groups (2019 ADA/EASD Consensus Statement: ASCVD, CKD, HF, hypo, or obesity). The medication use rate in risk vs. no risk groups, the overall mean, or com vs. med cohorts was tested using bootstrapping. Results: A large proportion of patients with T2D fit into multiple risk groups; overlap was more common in Med data. Patients with history of hypo or HF had the greatest overlap. Metformin, GLP-1, and SGLT2 use were more common in the Com cohort. SU and DPP-4 use were higher in Med data. The guideline-recommended therapy use was more prevalent in the CKD, ASCVD groups in Com cohort, and in obesity groups in both cohorts vs. no risk group. SGLT2 use rate was lower in the HF group compared to cohort average in both cohorts. Conclusion: Prevalence of guideline-aligned treatment use in 2019 was low, particularly since many patients fit into multiple risk groups with established treatment benefits.
A trade-off exists in most diabetes therapies between the benefits of good glycemic control and the morbidity of hypoglycemia. Balancing these factors to achieve desired outcomes is a key ...consideration for personalized diabetes therapy. Hypoglycemia at night (nocturnal hypoglycemia NH) is a common but often under-reported problem in insulin-treated patients with type 2 diabetes. To better understand the risk for NH, we pooled data from multiple clinical trials of insulin treatment and specifically examined NH risk factors in relation to glycemic goals.
Of 53 randomized trials involving insulin treatment, 18 trials that collected NH data were included. Risk factors associated with NH were identified by using gradient-boosting methods. A proportional hazards model was used to quantify the hazard ratio (HR) for risk factors. By modeling with individual patient data, a patient-level NH risk score distribution was created. Finally, results of the model were used to quantify an adjustment to the glycemic goal that would fully offset each risk factor, all other factors being equal.
Data pooling resulted in the inclusion of 7341 patients with type 2 diabetes from 18 randomized clinical trials. In the mean 6-month treatment period, 43% of patients experienced at least 1 episode of NH (mean SD, 1.1 1.5 events/month). Reduction of glycosylated hemoglobin (HbA1c) levels during the trial was a risk factor for NH (HR, 1.40 95% CI, 1.38-1.43 per –1% of HbA1c). Higher baseline HbA1c level was a protective factor against NH (HR, 0.76 95% CI, 0.74–0.77 per +1% of HbA1c); and the adjustment to HbA1c goal required to offset 1% higher baseline HbA1c was –0.825%. Patient characteristics for risk of NH included older age (HR, 1.02 95% CI, 1.01–1.02) per 1-year increase), female sex (HR, 1.18 95% CI, 1.15–1.22), black or African-American race (HR, 1.41 95% CI, 1.33–1.50 vs white race), longer diabetes duration (HR, 1.02 95% CI, 1.01–1.02 per 1-year increase), diabetic nephropathy (HR, 1.40 95% CI, 1.27–1.54), and concomitant sulfonylurea use (HR, 1.10 95% CI, 1.05–1.15). Asian race was associated with a lower risk of NH (HR, 0.50 95% CI, 0.48–0.53 vs white race); this finding could be offset with a 2.03% adjustment to the HbA1c goal.
Data on NH are scarce. By pooling multiple clinical trials, this study was able to evaluate patient-level data. A quantitative understanding of the trade-off between individual risk factors for NH and glycemic reduction may help clinicians to personalize patients’ glycemic goals, while effectively managing NH risk. Limitations of the study include that patients were selected through inclusion/exclusion criteria and that patient compliance may be better in a trial setting. Validating the findings in the real world will be helpful.
Objective
Nonalcoholic steatohepatitis (NASH) is a form of chronic liver disease (CLD): patients have an increased risk of developing cirrhosis, liver failure, and complications (e.g. hepatocellular ...carcinoma). NASH has a high clinical burden, and likely impairs patients’ health-related quality of life (HRQoL), but there are currently no licensed therapies. The objective of this robust pragmatic literature review was to identify and describe recent studies on the HRQoL burden of NASH from the patient perspective.
Methods
English-language primary research studies were identified that measured HRQoL in adults with NASH (population-based studies or clinical trials of pharmacological therapy). Searches were conducted in the following bibliographical databases: MEDLINE, EMBASE, Cochrane Database of Systematic Reviews (CDSR), Cochrane Central Register of Controlled Trials (CENTRAL), Database of Abstracts of Reviews of Effects (DARE), and Health Technology Assessment Database (HTA). Abstracts from selected congresses (2015/2016) were hand searched. Articles were assessed for relevance by two independent reviewers, and HRQoL data were extracted.
Results
A total of 567 de-duplicated abstracts were identified, and 20 full-text articles were reviewed. Eight studies were included: five quantitative, two interventional, and one qualitative. The quantitative and interventional studies measured HRQoL using the Short-Form 36 (SF-36) and the Chronic Liver Disease Questionnaire (CLDQ), and the qualitative study involved focus groups and individual interviews. Overall, the studies showed that NASH affects HRQoL, especially physical functioning, with many patients reporting being fatigued. In quantitative studies, overall, patients with NASH had a reduced HRQoL versus normative populations and nonalcoholic fatty liver disease (NAFLD) patients, but not versus chronic liver diseases. A longitudinal study showed that when weight loss was achieved, HRQoL improvement over 6 months was greater in patients with NASH versus NAFLD. Qualitative research suggested that, in addition to fatigue, other symptoms are also burdensome, having a broad negative impact on patients’ lives. The impact of pharmacological treatment on HRQoL was explored in only two included studies.
Conclusions
HRQoL is impaired in patients with NASH. Patients experience a range of symptoms, especially fatigue, and the impact on their lives is broad. Further research is needed to understand the HRQoL burden of NASH (e.g. assessing NASH-specific impacts not captured by SF-36 and CLDQ) and the impact of future NASH therapies on HRQoL.
Objective: Obesity and prediabetes are widespread in the U.S., with an estimated 42% of adults living with obesity and 35% living with prediabetes. This research examines the prevalence of ...comorbidities among people with obesity with or without prediabetes over a 5-year period.
Methods: This observational study uses Optum® Market Clarity deidentified data from 2007-2020. For adults with obesity and prediabetes, the index date was defined as the first date where the person was identified with both obesity (BMI ≥ 30 kg/m2) and prediabetes (5.7% ≤ A1c < 6.5%) while for adults with obesity without prediabetes (obesity only) , the index date was defined as the first date with BMI ≥ 30 kg/m2. All patients were required to be continuously insured from 1 year prior through 5 years post index date. For both cohorts, patients were excluded if there was any prior evidence of diabetes and in the obesity only cohort individuals were excluded if identified with prediabetes in the pre-period. Prevalence rates and 95% CIs were calculated for comorbidities for each 365-day period post index date and over the entire 5-year post-period.
Results: There were 22,423 individuals with obesity and prediabetes (mean age = 58.5 years; 42.2% male) and 902,975 individuals with obesity only (mean age = 51.7 years; 46.1% male) . The mortality rate after 5 years was 16.7% for adults with obesity and prediabetes and 9.9% for adults with obesity only (p<0.05) . Five years post index date, the prevalence of T2D was 28.8% for people with obesity and prediabetes and 11.9% for people with obesity only (p<0.05) . Prevalence rates for heart failure (4.3% v 2.2%) , atherosclerotic cardiovascular disease (16.2% v 6.9%) , and obstructive sleep apnea (2.2% v 1.0%) were significantly higher for adults with obesity and prediabetes compared to adults with obesity without prediabetes (all p<0.05) .
Conclusion: Findings indicate that the presence of prediabetes contributes to the development of additional morbidity and mortality in adults with obesity over time.
Disclosure
J.Ford: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. M.J.Lage: Consultant; Eli Lilly and Company. K.Boye: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. J.P.Bae: None. K.A.Terrell: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. M.C.Bunck: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company.
The proportion of heart failure patients with preserved ejection fraction has been rising over the past decades and has coincided with increases in the prevalence of obesity and metabolic syndrome. ...The relationship between these interconnected comorbidities and heart failure with preserved ejection fraction (HFpEF) is still poorly understood. This study characterized obesity and metabolic syndrome among real-world patients with HFpEF.
We identified adults with heart failure in the Veradigm Cardiology Registry, previously the PINNACLE Registry, with a left ventricular ejection fraction measurement ≥ 50% between 01/01/2016 and 12/31/2019. Patients were stratified by obesity diagnosis and presence of metabolic syndrome (≥ 3 of the following: diabetes, hypertension, hyperlipidemia, and obesity). We captured baseline demographic and clinical characteristics and used multivariable logistic regression to examine the odds of having cardiac (atrial fibrillation, coronary artery disease, coronary artery bypass surgery, myocardial infarction, and stroke/transient ischemic attack) and non-cardiac (chronic kidney disease, chronic liver disease, and peripheral artery disease) comorbidities of interest. The models adjusted for age and sex, and the main covariates of interest were obesity and metabolic burden score (0-3 based on the presence of diabetes, hypertension, and hyperlipidemia). The models were run with and without an obesity*metabolic burden score interaction term.
This study included 264,571 patients with HFpEF, of whom 55.7% had obesity, 52.5% had metabolic syndrome, 42.5% had both, and 34.3% had neither. After adjusting for age, sex, and burden of other metabolic syndrome-associated diagnoses, patients with HFpEF with obesity had lower odds of a diagnosis of other evaluated comorbidities relative to patients without obesity. The presence of metabolic syndrome in HFpEF appears to increase comorbidity burden as each additional metabolic syndrome-associated diagnosis was associated with higher odds of assessed comorbidities except atrial fibrillation.
Obesity was common among patients with HFpEF and not always co-occurring with metabolic syndrome. Multivariable analysis suggested that patients with obesity may develop HFpEF in the absence of other driving factors such as cardiovascular disease or metabolic syndrome.
This research examines the prevalence of morbidity and mortality among people with obesity with or without prediabetes.
This observational study uses Optum® Market Clarity deidentified data from 2007 ...to 2020. Individuals with obesity without prediabetes (obesity only) were matched 1:1 to adults with prediabetes plus obesity based upon age, sex, race, ethnicity, and region. Age and sex adjusted prevalence rates and 95 % CIs were calculated for morbidity and mortality for each 365-day period post index date and over the entire 5-year post-period.
After 5-years, the adjusted mortality rate was 10.1 % for adults with obesity plus prediabetes and 6.9 % for adults with obesity only (p < 0.05). Five years post index date, the prevalence of type 2 diabetes was 25.3 % for people with obesity plus prediabetes and 9.2 % for people with obesity only (p < 0.05). Prevalence rates after 5 years for atherosclerotic cardiovascular disease (13.1 % v 8.1 %), composite cardiovascular outcome (7.0 % v 4.4 %) and composite cardio-renal outcome (8.9 % v 5.0 %) were significantly higher for adults with obesity plus prediabetes compared to adults with obesity only (all p < 0.05).
Results of this study indicate that the presence of prediabetes contributes to the development of additional morbidity and mortality in adults with obesity.
•Obesity and prediabetes are both widespread in the United States.•The study examines prevalence for those with obesity with and without prediabetes.•Both cohorts had a high morbidity and mortality burden over 5 years.•Prediabetes and obesity had significantly higher morbidity and mortality prevalence.•Prediabetes contributes to added morbidity and mortality in adults with obesity.
Economic models in type 1 diabetes have relied on a change in haemoglobin A1c as the link between the blood glucose trajectory and long-term clinical outcomes, including microvascular and ...macrovascular disease. The landscape has changed in the past decade with the availability of regulatory approved, accurate and convenient continuous glucose monitoring devices and their ability to track patients' glucose levels over time. The data emerging from continuous glucose monitoring have enriched the clinical understanding of the disease and indirectly of patients' behaviour. This has triggered the development of new measures proposed to better define the quality of glycaemic control, beyond haemoglobin A1c. The objective of this paper is to review recent developments in clinical knowledge brought into focus with the application of continuous glucose monitoring devices, and to discuss potential approaches to incorporate the concepts into economic models in type 1 diabetes. Based on a targeted review and a series of multidisciplinary workshops, an influence diagram was developed that captures newer concepts (e.g. continuous glucose monitoring metrics) that can be integrated into economic models and illustrates their association with more established concepts. How the additional continuous glucose monitoring-based indicators of glycaemic control may contribute to economic modelling beyond haemoglobin A1c, and more accurately reflect the economic value of novel type 1 diabetes treatments, is discussed.
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