Adding a sodium-glucose co-transporter 2 (SGLT2) inhibitor dominated switching to a glucagon-like peptide 1 receptor agonist over the lifetimes of patients with type 2 diabetes not at glycated hemoglobin A1c target after treatment with metformin plus a dipeptidyl peptidase-4 inhibitor.
Objectives: Using a US payer perspective, this study aimed to compare the lifetime cost-effectiveness of adding sodium-glucose cotransporter 2 (SGLT2) inhibitors vs switching to glucagon-like peptide 1 receptor agonists (GLP-1 RAs) among patients with type 2 diabetes who were not at glycated hemoglobin A1c target after dual therapy with metformin and dipeptidyl peptidase-4 (DPP-4) inhibitors.
Study Design: The cost-effectiveness analysis was performed with the validated IQVIA Core Diabetes Model. Treatment effects were obtained from randomized clinical trials with economic data based on published literature.
Methods: Risk of treatment-emergent adverse events and complications were simulated using submodels informed by published risk equations adjusted for patient characteristics, physiological parameters, and history of complications. Outcomes included cumulative incidence of micro- and macrovascular complications, life-years (LYs), quality-adjusted life-years (QALYs), and total costs. Scenario analyses were performed to assess robustness of results to variations in clinical and cost inputs and assumptions.
Results: Over a lifetime time horizon, adding an SGLT2 inhibitor dominated the strategy of switching to a GLP-1 RA, improving survival by 0.049 LYs and 0.026 QALYs, and was associated with cost savings of $9511. The majority of the scenario analyses confirmed dominance of the DPP-4 inhibitor + SGLT2 inhibitor pathway vs the GLP-1 RA pathway. The probabilistic sensitivity analysis reinforced the base-case finding of cost savings while gaining QALYs.
Conclusions: Intensification with an SGLT2 inhibitor on top of a DPP-4 inhibitor demonstrated slightly better efficacy and cost savings compared with switching to a GLP-1 RA in patients not at glycemic goal with metformin and a DPP-4 inhibitor.
Am J Manag Care. 2021;27(8):e269-e277. https://doi.org/10.37765/ajmc.2021.88728
Treatment intensification with sodium-glucose co-transporter 2 (SGLT2) inhibitors demonstrated slightly better efficacy and cost savings compared with switching to glucagon-like peptide 1 receptor agonists (GLP-1 RAs):
Type 2 diabetes (T2D) is a chronic condition in which the body becomes resistant to insulin, causing glucose to build up in the blood.1 High blood glucose results in complications in a variety of organs, including the eyes, kidneys, nerves, and heart.1 T2D is among the most prevalent chronic diseases in the world today and accounts for 90% to 95% of all diabetes cases.2 Twenty-one million Americans (8.6%) received a diagnosis of T2D in 2016.3 T2D is a multifactorial condition characterized by increasing chronic disability and higher risk of related conditions that contribute to increased patient morbidity and risk of mortality, such as cardiovascular disease (CVD) and obesity.1 It was the seventh leading cause of death in the United States in 2015, listed as the underlying cause for 79,535 deaths (2.9% of 2015 total mortality) and mentioned as a cause for 252,806 deaths (9.3%).2,4
Beyond exacting a large burden on individual patients, T2D imposes a substantial economic burden on health systems and the US population. In 2017, the total estimated cost of diagnosed diabetes was $327 billion, including $237 billion in direct medical costs and $90 billion in reduced productivity.5 The high cost of diabetes is driven by the substantial health care resource use in managing acute events and long-term complications.5 More than 21% of the 7.2 million hospital discharges with diabetes in 2014 were for major CVD, with another 4% associated with diabetic ketoacidosis and lower-extremity amputation.2 Emergency department visits totaled 14.2 million for hypo- and hyperglycemic events as well as other conditions for which diabetes was listed among the diagnoses.2
The goal of diabetes treatment is to manage blood glucose, which subsequently prevents or slows the progress of complications and their impact on quality of life.6 The 2018 guidelines from the American Diabetes Association (ADA) recommend a glycated hemoglobin A1c (HbA1c) target of 6.5% to 8%, depending on patient characteristics and history of hypoglycemia and adverse events.7 A threshold of 8% may be appropriate for patients with a history of severe hypoglycemia or advanced CVD complications who have not had success with previous glucose-lowering agents.7
Patients with T2D are first treated with metformin monotherapy.7 Although the ADA guidelines do not denote a specific preference for add-on therapies, sulfonylurea has been frequently prescribed after metformin because of its low cost and availability of long-term safety data.8 However, a 2010 meta-analysis revealed significant risk of hypoglycemia with sulfonylurea compared with placebo.9 Dipeptidyl peptidase-4 (DPP-4) inhibitors have been demonstrated to be at least as effective as sulfonylurea in glycemic control without increased risk of hypoglycemia and have become a commonly used add-on therapy among patients with T2D whose HbA1c is not at goal levels with metformin monotherapy.7,10 Evidence has also indicated lower all-cause mortality and fewer CVD events with DPP-4 inhibitors.11
For patients who fail to reach target HbA1c on metformin plus a DPP-4 inhibitor, there are many treatment options for intensification, which could include adding an oral therapy such as a sodium-glucose co-transporter 2 (SGLT2) inhibitor or switching to other products such as a glucagon-like peptide 1 receptor agonist (GLP-1 RA).7 Whereas GLP-1 RAs are not recommended for concurrent use with DPP-4 inhibitors,7 recent clinical trials of third-line therapy have demonstrated a clinical benefit of combinations with DPP-4 inhibitors and SGLT2 inhibitors that is significantly greater than the use of each individual component with metformin.12-18 With emerging evidence on the clinical benefits and cardioprotective effects of SGLT2 inhibitors, it is expected that treatment pathways for patients who fail on metformin with DPP-4 inhibitors may increasingly consider triple-therapy combinations.19-21
With the variety of treatment options that could be recommended by physicians along the treatment pathway in real-world practice, it may be difficult for clinical trials to capture the complete picture of every possible combination and sequence of treatments. However, it is vital to understand the comparative performance and the associated economic outcomes for treatment options to ensure that key health care decision makers can support the use of products that bring the most value to patients and health care systems. This study aimed to compare from a US payer perspective the projected lifetime outcomes and costs of 2 treatment intensification pathways in treating patients with T2D who have failed on dual therapy. After failure to achieve goal on dual therapy with metformin and a DPP-4 inhibitor, we explored adding an SGLT2 inhibitor compared with switching from the DPP-4 inhibitor to a GLP-1 RA prior to initiation of insulin to examine the relative impact of triple therapy with metformin, DPP-4 inhibitor, and SGLT2 inhibitor vs dual therapy composed of metformin and GLP-1 RA.
The analysis was performed using the IQVIA Core Diabetes Model (CDM) version 9.0, which is a thoroughly validated internet-based computer simulation that uses annual cycles to predict long-term health outcomes and costs in patients with T2D.22-24 The CDM is product agnostic; it starts with a predefined patient cohort and follows these patients over time. Baseline patient characteristics, physiological parameters (eg, HbA1c, blood pressures, lipids, body weight), past history of complications, and other relevant clinical parameters feed into risk equations represented by a series of 17 submodels. Each submodel is a combination of semi-Markov structures and Monte Carlo simulations that predicts diabetes complications and treatment-emergent adverse events (AEs) that include, but are not limited to, congestive heart failure, myocardial infarction, stroke, end-stage renal disease, lower extremity amputation, and hypoglycemia. In addition to event prediction, the CDM also calculates life-years (LYs), quality-adjusted life-years (QALYs), and total costs. This analysis used the United Kingdom Prospective Diabetes Study (UKPDS) 82 risk equations and other physiological parameters progressed primarily based on the Framingham Heart Study.25,26
The analysis took a US third-party payer perspective over a 50-year (lifetime) time horizon. Direct medical costs (intervention, treatment of diabetes complications, and management of treatment-emergent AEs) were included. A 3% annual discount rate was applied to costs and outcomes based on US best practices.27 All analyses were run with 1000 patients for 1000 iterations.
The model cohort was patients who were not at HbA1c goal on metformin and who had intensified to dual therapy, to represent the appropriate target population for this type of therapy intensification in the United States. The baseline patient characteristics were derived from the GE Centricity electronic medical record (EMR) database (Merck & Co Inc; GE Centricity EMR: Januvia Diabetes Economic Program [JADE] cohort data on file), supplemented by the relevant clinical trials (Merck & Co Inc; ertugliflozin clinical trial program [VERTIS] data on file). This population matches the population entering the model on the metformin + DPP-4 inhibitor combination in both arms. Note that due to the use of a network meta-analysis to obtain class-level effects for the metformin + DPP-4 inhibitor combination,28-32 population characteristics for the specific clinical data were not available. Therefore, these general population data were considered most appropriate to reflect the US cohort eligible for these intensification pathways. Starting HbA1c was 8.37%, with a mean age of 57.93 years. Full details are available in eAppendix Table 1 (eAppendix available at ajmc.com).
Treatment Pathways, Effects, and Dosing
The model compared 2 intensification pathways for patients on DPP-4 inhibitors in combination with metformin who were not at HbA1c target (7.5%). Treatment pathway 1 added SGLT2 inhibitor to metformin + DPP-4 inhibitor (metformin + DPP-4 inhibitor + SGLT2 inhibitor), whereas treatment pathway 2 switched patients from DPP-4 inhibitor to GLP-1 RA (metformin + GLP-1 RA). Upon failure to achieve glycemic control, patients in pathway 1 dropped the SGLT2 inhibitor and added basal insulin, and patients in pathway 2 added basal insulin to their current treatments.33 Patients transitioned to metformin in combination with basal and bolus insulin upon further treatment failure. Failure to achieve glycemic control is defined equivalently as HbA1c of 7.5% or higher in both pathways, with HbA1c progressing according to UKPDS equations.25 Figure 1 depicts the full treatment pathways.
Clinical inputs for treatments within each pathway were aligned with published randomized clinical trials (RCTs) or meta-analyses of RCTs. Where available, information on class effects derived from large meta-analyses was used to produce more generalizable output. With regards to SGLT2 inhibitors, specifically, a recent meta-analysis found no differences between ertugliflozin and other SGLT2 inhibitors in this combination therapy setting, excepting only dapagliflozin 10 mg, which was less effective when added to sitagliptin and metformin.34 Treatment effects on CVD outcomes were not included in pivotal trials or existing meta-analyses; therefore, they were omitted from the base-case analysis and reserved for scenario analysis. This was considered appropriate because CVD benefits reflect targeted safety studies performed in populations with high CVD risk rather than the target population for this study20,35 and do not reflect the specific combination therapies in the pathway. However, scenario analysis permits exploration of outcomes when assuming a single dominant effect (eg, the CVD benefit of SGLT2 inhibitor only in the triple-therapy combination), as well as that the magnitude of benefit in a high-risk population applies to the general T2D population.
Dosing for each regimen reflected the prescribing information, except for insulin.36 Insulin glargine represented basal insulin, and a daily dose of 53.2 IU/day was applied when used in combination with DPP-4 inhibitor or GLP-1 RA in both treatment pathways.33 When glargine was used in combination with bolus insulin, a daily dose of 61.3 IU/day was applied.33 Dosage for bolus insulin (represented by insulin aspart) was 0.2 IU/kg based on a National Institute for Health and Care Excellence Assessment Group report of SGLT2 inhibitors.37 The total daily dose of bolus insulin (17.0 IU) was estimated using the body weight of patients in the ertugliflozin clinical trial program (Merck & Co Inc; ertugliflozin clinical trial program [VERTIS] data on file).
Utilities and Disutilities
Utilities and disutilities reflected previously published US default estimates for a T2D population.38-42 The utility value for a patient with T2D without any complications was 0.785.38 Acute events were associated with decrement in utilities (disutilities), following which the patient changed health state and was assigned a different utility. In the case of multiple events occurring in a single model cycle, the lowest adjusted utility was applied for the period of the event. The disutility for nonsevere hypoglycemic events was estimated using a diminishing approach derived from Lauridsen et al.43
Annual treatment costs were calculated based on the dosing of each treatment, with wholesale acquisition costs (in 2018 US$) derived from the Medi-Span PriceRx database.44 Costs of the fixed-dose combinations (FDCs) were used for DPP-4 inhibitor + SGLT2 inhibitor where applicable. Costs of individual drugs were weighted by the market distribution within each drug class to estimate the class costs. Similarly, the FDC options available for DPP-4 inhibitor + SGLT2 inhibitor were also weighted by the relative market share (Merck & Co Inc; market distribution data on file).
For insulin regimens, the costs of test strips, lancets, and needles were included. Frequency of use was based on published literature or assumptions about typical usage per clinical expert opinion.45 Patients on basal insulin were assumed to require 1 lancet, 0 needles, and 2.7 test strips per day. Patients on basal + bolus insulin were assumed to require 1 lancet, 2 needles, and 2.7 test strips per day. Unit costs of these resources were based on the ADW Diabetes website and the CMS durable medical equipment file.46,47
T2D management costs were included. For complications or procedures with a clear description of drug or resource use, PriceRx and Medicare fee schedules were used.44,48,49 For other complications (eg, CVD, renal, neuropathy, foot ulcer), costs per event from the published literature were used, inflated to 2018 US$.50
Scenario and Sensitivity Analyses
Scenario analyses were conducted to explore the impact of alternative inputs and assumptions.
The cost inputs were tested for the DPP-4 inhibitor + SGLT2 inhibitor treatment. Separate ingredients costs for DPP-4 inhibitor and SGLT2 inhibitor (weighted for drug classes) were used instead of weighted FDC, increasing the annual treatment cost to $10,739.23.
To account for treatment effects on CVD, literature-based risk reductions (RRs) for events (heart failure, myocardial infarction, and stroke) were used to adjust the CVD effects across lines of therapy.
The upper or lower CIs of the key treatment effects (HbA1c or body mass index [BMI], one at a time) across all lines of therapy were applied based on the clinical studies used in base case analysis.
The disutility for gastrointestinal (GI) distress (nausea) during metformin + GLP-1 RA treatment was incorporated as an incremental effect vs metformin + DPP-4 inhibitor. The GI-related disutility was –0.04 and affected 14% more patients with GLP-1 RA treatment compared with those on DPP-4 inhibitor, which produced a weighted disutility of –0.0066 per patient per event.51,52
Impact of adherence to GLP-1 RA and DPP-4 inhibitor was explored by assuming that nonadherent patients had a mean proportion of days covered (PDC) of 79% (ie, these patients only filled 79% of their prescriptions), which affected only medication costs and HbA1c (but not the other clinical parameters). For DPP-4 inhibitor, 66% of patients were nonadherent, with an HbA1c benefit of –0.51%. For GLP-1 RA, the nonadherence rate was 71%, with an HbA1c benefit of –0.52%.53
Probabilistic sensitivity analysis (PSA) was also conducted, accounting for intrinsic variation of input parameters. Sampling was performed according to published standard errors and designated parametric forms (see eAppendix Tables 1, 2, and 4 for included parameters and distribution information). Although there is no official value for the willingness-to-pay threshold for an intervention in the United States, historically cited values such as $50,000 and $100,000 per QALY are currently thought to be low.54 A 2008 review on cost-effectiveness of reimbursed interventions showed an implicit threshold range of $109,000 to $294,000 per QALY.55 The Institute for Clinical and Economic Review applies a range of $50,000 to $150,000 per QALY in US cost-effectiveness reviews.56 In addition, pharmacy benefit management organizations have made efforts to set and use thresholds when assessing value. For example, CVS Caremark published in 2018 that a $100,000/QALY threshold would be used to determine patient access to drugs.57
Detailed population characteristics, treatment costs, and utility inputs may be found in Table 114,33,37-39,41,43,44 and eAppendix Tables 1, 2, and 3. Clinical inputs may be viewed in Table 2,14,20,28-33,35,58-60 with additional detail in eAppendix Table 4.
Base-case results are shown in Table 3. Health outcomes were similar between the 2 treatment pathways. Addition of an SGLT2 inhibitor while remaining on dual therapy with DPP-4 inhibitor was associated with lower rates of microvascular and macrovascular complications overall, longer survival (0.049 incremental LYs), and better quality of life (0.026 incremental QALYs) compared with switching to GLP-1 RA over a lifetime time horizon. Total lifetime medical costs were reduced by $9511 when keeping patients on DPP-4 inhibitor + SGLT2 inhibitor. The cost difference between pathways was predominantly driven by the difference in accrued medication costs ($9086), reflecting the less costly FDC price for DPP-4 inhibitor + SGLT2 inhibitor in the metformin + DPP-4 inhibitor + SGLT2 inhibitor combination ($6472/year) vs metformin + GLP-1 RA ($8369/year) (eAppendix Table 4). The incremental cost-effectiveness ratio (ICER) results indicated that remaining on DPP-4 inhibitor and adding SGLT2 inhibitor dominated the strategy of switching to GLP-1 RA.
In most scenarios, the DPP-4 inhibitor + SGLT2 inhibitor pathway remained dominant or was associated with an ICER below the thresholds accepted by decision makers (Table 3). The cost scenario using individual ingredient costs instead of FDC resulted in a higher cost to the DPP-4 inhibitor + SGLT2 inhibitor treatment pathway, which produced an ICER of $76,487/QALY. The upper CI of BMI scenario and the CVD scenario both resulted in fewer QALYs for the DPP-4 inhibitor + SGLT2 inhibitor pathway vs the GLP-1 RA pathway, driven by the relative effect for GLP-1 RA treatment after applying the adjustments; the QALY differences are very low (–0.067 and –0.044, respectively) and the DPP-4 inhibitor + SGLT2 inhibitor remained cost-saving. All remaining scenarios demonstrated greater QALY benefits and cost savings with the DPP-4 inhibitor + SGLT2 inhibitor pathway.
The PSA reinforced the base-case finding of cost savings across 97% of iterations, and 56% of these cost-saving simulations also showed slightly better QALY results (ie, the DPP-4 inhibitor + SGLT2 inhibitor pathway dominated the GLP-1 RA pathway). The incremental QALYs ranged across the axis, with the DPP-4 inhibitor + SGLT2 inhibitor pathway having better QALY results in 57.5% of the simulations (Figure 2).
The current analysis shows that for patients not at goal with metformin and DPP-4 inhibitor, intensification with SGLT2 inhibitor is likely a cost-effective alternative compared with switching to metformin + GLP-1 RA. PSA and scenario analyses confirmed that model results remained robust and consistent with the base-case findings of cost-effectiveness for triple therapy with DPP-4 inhibitor and SGLT2 inhibitor.
When considering separate ingredient costs, DPP-4 inhibitor + SGLT2 inhibitor costs increased, with the resulting ICERs below the accepted threshold. Note that because ingredient costs are fixed fees, these are not varied in PSA; therefore, uncertainty around the ICER for this scenario would appear similar to the base case (Figure 2) but shifted upward on the cost axis. Univariate scenario analyses on clinical parameters (HbA1c, BMI, CVD, disutilities of AEs) revealed that GLP-1 RA may lead to a very minor improvement in outcomes (range of 0.044 to 0.067 gain in QALYs compared with the DPP-4 inhibitor + SGLT2 inhibitor pathway) if BMI reduction or RRs for CVD effects were assumed to be the most favorable. However, the pathway with DPP-4 inhibitor and SGLT2 inhibitor remained cost-saving in all scenarios and showed incremental outcome improvements over GLP-1 RA in all other univariate scenarios explored. When considering adherence, total treatment costs of both pathways decreased because patients did not fill all their prescriptions. Survival was projected to be smaller for both pathways compared with the base case, with greater drop for the GLP-1 RA pathway given the higher proportion of nonadherent patients. The DPP-4 inhibitor + SGLT2 inhibitor pathway remained dominant.
To the authors’ knowledge, no published cost-effectiveness analyses have compared the treatment pathways studied in this analysis. A recently published analysis by Pawaskar et al compared DPP-4 inhibitor + SGLT2 inhibitor with direct intensification with insulin in the United States.42 This study’s results showed that the projected long-term benefits of a sequential pathway with branded oral medication including DPP-4 inhibitor and subsequent addition of SGLT2 inhibitor prior to insulin initiation was cost-effective.42 Our study took a similar approach to compare 2 treatment pathways with therapy intensification. It confirmed that intensification with SGLT2 inhibitor was projected to improve outcomes among patients on metformin + DPP-4 inhibitor dual therapy who did not meet the HbA1c target. Different from previous work, the current analysis compared pathways with branded products, examining triple therapy with DPP-4 inhibitor/SGLT2 inhibitor vs dual therapy of metformin + GLP-1 RA. We used a published, comprehensive meta-analysis for GLP-1 RA inputs, which represented class effects that made conclusions generalizable. In addition, outcomes with CVD effects were explored in a scenario analysis. However, the CVD literature for the specific population and combination regimens (eg, SGLT2 inhibitor as add-on to combination therapy with DPP-4 inhibitor) is currently scarce; formal evidence synthesis of these particular data is needed to understand the potential class effect.
As with any modeling study, this analysis has some limitations, primarily linked to data availability. First, there are no head-to-head data or common source for the comparison of interest. A large meta-analysis was used where feasible to provide class-level treatment effects, and PSA and scenario analyses were utilized to test the impact of modeling assumptions and uncertainty in model parameters. No RCT was available for the order of intensification with metformin + GLP-1 RA + glargine, and thus proxy data with the same ingredients were utilized. Additionally, assumptions were made regarding clinically appropriate hypoglycemic event rates to address concerns about equivalent event reporting (eg, for metformin + GLP-1 RA + glargine, this rate was assumed to not differ from the Roussel 2019 intervention arm33). Second, certain scenarios were exploratory in nature due to limited data. For the nonadherence scenario, mean PDC was not available in the literature, so assumptions were made that nonadherent patients had a PDC of 79%. Similarly, nonadherence data were not available for all lines of therapy, and therefore the current analysis should be considered directional only. When assessing CVD effect, due to limited information about combination therapies, the CVD effect for 1 therapy was applied to the treatment combination. For example, SGLT2 inhibitor effect was assumed for patients taking metformin + DPP-4 inhibitor + SGLT2 inhibitor, and CVD risk for patients on glargine was assumed to not vary in terms of concurrent treatment with DPP-4 inhibitor or GLP-1 RA. No independent reduction in CVD mortality was implemented in order to remain conservative and avoid any potential for double counting. Future analyses with CVD effects for the combination therapy of interest and relevant to the population would provide valuable insight into the true cost-effectiveness of these pathways. An ideal future analysis would also incorporate potential for renal protective effects. Recent studies, including CREDENCE,61 have also found renal protective effects for SGLT2 inhibitors that were omitted from the current analysis. The omission was due to 2 factors: Version 9.0 of the IQVIA CDM was not equipped to explore these effects, nor were the effects found in the specific triple combination therapy of interest. This remains conservative in limiting the potential benefits of the SGLT2 inhibitor combination therapy, and thus such an analysis would be expected to strengthen the current findings. Lastly, the costs per class reflected market weighted averages and did not necessarily align with each particular regimen. However, they may appropriately represent the overall market in the United States. In addition, branded list prices were used; although these rarely match the cost to a payer due to discounts, details of which are not publicly available, they provide insight into the relative costs. Moreover, a recent evidence report by the Institute for Clinical and Economic Review suggested that across payers, average discounts might increase the incremental difference between the metformin + DPP-4 inhibitor + SGLT2 inhibitor combination in comparison with the metformin + GLP-1 RA regimen,62 and therefore this analysis without ingredient cost discounts may be considered a reasonable initial examination of the cost-effectiveness between these 2 pathways.
Although this study is subject to limitations inherent in any modeling exercise, it provides new, important information on how alternative treatment intensification pathways in T2D can affect long-term patient health outcomes and direct medical costs. By combining high-quality RCT and network meta-analysis data with a well-validated multifactorial model in T2D, this study allows for a comparative view of treatment combinations that has not been directly studied in clinical research and considers the long-term cost and efficacy implications of treatment selection. In particular, this study suggests that overall health outcomes are similar between pathways, with the base-case analysis showing a small benefit with DPP-4 inhibitor + SGLT2 inhibitor at lower total cost. Cost savings are primarily driven by differences in medication costs. Considering these findings together, triple therapy with DPP-4 inhibitor and SGLT2 inhibitor is projected to bring additional benefit at a lower cost compared with intensification with GLP-1 RA in patients who fail to achieve glycemic control with metformin and DPP-4 inhibitor.
Author Affiliations: Merck & Co Inc (MP, TW, GD), Kenilworth, NJ; IQVIA Inc (SPB, SK, QL), San Francisco, CA.
Source of Funding: This research is supported by Merck & Co Inc.
Author Disclosures: Dr Pawaskar, Ms Weiss, and Dr Davies are employed by and own stock in Merck & Co Inc. Ms Bilir, Ms Kowal, and Ms Li are employed by IQVIA, which was hired by Merck to perform the analysis and prepare this manuscript.
Authorship Information: Concept and design (MP, SPB, SK, GD); acquisition of data (SK, GD); analysis and interpretation of data (MP, SPB, SK, QL, TW, GD); drafting of the manuscript (MP, SPB, QL, TW, GD); critical revision of the manuscript for important intellectual content (MP, SPB, SK, QL, TW, GD); obtaining funding (MP, GD); administrative, technical, or logistic support (QL, TW); and supervision (MP, GD).
Address Correspondence to: Tracey Weiss, MPH, Merck & Co Inc, 126 E Lincoln Ave, Rahway, NJ 07065. Email: email@example.com.
1. IDF Diabetes Atlas. 9th ed. International Diabetes Federation; 2019. Accessed February 15, 2020. https://diabetesatlas.org/en/resources/
2. CDC. National diabetes statistics report, 2017: estimates of diabetes and its burden in the United States. American Diabetes Association. 2017. Accessed February 15, 2020. https://dev.diabetes.org/sites/default/files/2019-06/cdc-statistics-report-2017.pdf
3. Bullard KM, Cowie CC, Lessem SE, et al. Prevalence of diagnosed diabetes in adults by diabetes type — United States, 2016. MMWR Morb Mortal Wkly Rep. 2018;67(12):359-361. doi:10.15585/mmwr.mm6712a2
4. Xu J, Murphy SL, Kochanek KD, Arias E. Mortality in the United States, 2015. National Center for Health Statistics data brief No. 267. December 2016. Accessed February 15, 2020. https://www.cdc.gov/nchs/products/databriefs/db267.htm
5. American Diabetes Association. Economic costs of diabetes in the U.S. in 2017. Diabetes Care. 2018;41(5):917-928. doi:10.2337/dci18-0007
6. Tight diabetes control. American Diabetes Association. Accessed July 28, 2021. http://archives.diabetes.org/living-with-diabetes/treatment-and-care/blood-glucose-control/tight-diabetes-control.html
7. American Diabetes Association. Standards of medical care in diabetes—2020. Diabetes Care. 2020;43(suppl 1):S1-S212.
8. Wyncott D, Lyon C, Mounsey A. PURLs: need an add-on to metformin? consider this. J Fam Pract. 2017;66(1):42-44.
9. Phung OJ, Scholle JM, Talwar M, Coleman CI. Effect of noninsulin antidiabetic drugs added to metformin therapy on glycemic control, weight gain, and hypoglycemia in type 2 diabetes. JAMA. 2010;303(14):1410-1418. doi:10.1001/jama.2010.405
10. Fadini GP, Bottigliengo D, D’Angelo F, et al; DARWIN-T2D Network. Comparative effectiveness of DPP-4 inhibitors versus sulfonylurea for the treatment of type 2 diabetes in routine clinical practice: a retrospective multicenter real-world study. Diabetes Ther. 2018;9(4):1477-1490. doi:10.1007/s13300-018-0452-y
11. Ou SM, Shih CJ, Chao PW, et al. Effects on clinical outcomes of adding dipeptidyl peptidase-4 inhibitors versus sulfonylureas to metformin therapy in patients with type 2 diabetes mellitus. Ann Intern Med. 2015;163(9):663-672. doi:10.7326/M15-0308
12. DeFronzo RA, Lewin A, Patel S, et al. Combination of empagliflozin and linagliptin as second-line therapy in subjects with type 2 diabetes inadequately controlled on metformin. Diabetes Care. 2015;38(3):384-393. doi:10.2337/dc14-2364
13. Pratley RE, Eldor R, Raji A, et al. Ertugliflozin plus sitagliptin versus either individual agent over 52 weeks in patients with type 2 diabetes mellitus inadequately controlled with metformin: the VERTIS FACTORIAL randomized trial. Diabetes Obes Metab. 2018;20(5):1111-1120. doi:10.1111/dom.13194
14. Dagogo-Jack S, Liu J, Eldor R, et al. Efficacy and safety of the addition of ertugliflozin in patients with type 2 diabetes mellitus inadequately controlled with metformin and sitagliptin: the VERTIS SITA2 placebo-controlled randomized study. Diabetes Obes Metab. 2018;20(3):530-540. doi:10.1111/dom.13116
15. Jabbour SA, Hardy E, Sugg J, Parikh S; Study 10 Group. Dapagliflozin is effective as add-on therapy to sitagliptin with or without metformin: a 24-week, multicenter, randomized, double-blind, placebo-controlled study. Diabetes Care. 2014;37(3):740-750. doi:10.2337/dc13-0467
16. Mathieu C, Ranetti AE, Li D, et al. Randomized, double-blind, phase 3 trial of triple therapy with dapagliflozin add-on to saxagliptin plus metformin in type 2 diabetes. Diabetes Care. 2015;38(11):2009-2017. doi:10.2337/dc15-0779
17. Rodbard HW, Seufert J, Aggarwal N, et al. Efficacy and safety of titrated canagliflozin in patients with type 2 diabetes mellitus inadequately controlled on metformin and sitagliptin. Diabetes Obes Metab. 2016;18(8):812-819. doi:10.1111/dom.12684
18. Søfteland E, Meier JJ, Vangen B, Toorawa R, Maldonado-Lutomirsky M, Broedl UC. Empagliflozin as add-on therapy in patients with type 2 diabetes inadequately controlled with linagliptin and metformin: a 24-week randomized, double-blind, parallel-group trial. Diabetes Care. 2017;40(2):201-209. doi:10.2337/dc16-1347
19. Kosiborod M, Cavender MA, Fu AZ, et al. Lower risk of heart failure and death in patients initiated on sodium-glucose cotransporter-2 inhibitors versus other glucose-lowering drugs: the CVD-REAL study (comparative effectiveness of cardiovascular outcomes in new users of sodium-glucose cotransporter-2 inhibitors). Circulation. 2017;136(3):249-259. doi:10.1161/CIRCULATIONAHA.117.029190
20. Neal B, Perkovic V, Mahaffey KW, et al; CANVAS Program Collaborative Group. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644-657. doi:10.1056/NEJMoa1611925
21. Zinman B, Wanner C, Lachin JM, et al; EMPA-REG OUTCOME Investigators. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117-2128. doi:10.1056/NEJMoa1504720
22. Palmer AJ, Roze S, Valentine WJ, et al. The CORE Diabetes Model: projecting long-term clinical outcomes, costs and cost-effectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decision-making. Curr Med Res Opin. 2004;20(suppl 1):S5-S26. doi:10.1185/030079904X1980
23. Palmer AJ, Roze S, Valentine WJ, et al. Validation of the CORE Diabetes Model against epidemiological and clinical studies. Curr Med Res Opin. 2004;20(suppl 1):S27-S40. doi:10.1185/030079904X2006
24. McEwan P, Foos V, Palmer JL, Lamotte M, Lloyd A, Grant D. Validation of the IMS CORE Diabetes Model. Value Health. 2014;17(6):714-724. doi:10.1016/j.jval.2014.07.007
25. Hayes AJ, Leal J, Gray AM, Holman RR, Clarke PM. UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia. 2013;56(9):1925-1933. doi:10.1007/s00125-013-2940-y
26. Wilson PW, Evans JC. Coronary artery disease prediction. Am J Hypertens. 1993;6(11, pt 2):309S-313S. doi:10.1093/ajh/6.11.309s
27. Sanders GD, Neumann PJ, Basu A, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA. 2016;316(10):1093-1103. doi:10.1001/jama.2016.12195
28. Kongwatcharapong J, Dilokthornsakul P, Nathisuwan S, Phrommintikul A, Chaiyakunapruk N. Effect of dipeptidyl peptidase-4 inhibitors on heart failure: a meta-analysis of randomized clinical trials. Int J Cardiol. 2016;211:88-95. doi:10.1016/j.ijcard.2016.02.146
29. Krittanawong C, Kitai T, Aydar M, Sun T. Sitagliptin and risk of heart failure in patients with type 2 diabetes: a meta-analysis. JACC Heart Fail. 2016;4(11):910. doi:10.1016/j.jchf.2016.07.005
30. Lehrke M, Marx N, Patel S, et al. Safety and tolerability of linagliptin in patients with type 2 diabetes: a comprehensive pooled analysis of 22 placebo-controlled studies. Clin Ther. 2014;36(8):1130-1146. doi:10.1016/j.clinthera.2014.06.008
31. Maruthur NM, Tseng E, Hutfless S, et al. Diabetes medications as monotherapy or metformin-based combination therapy for type 2 diabetes: a systematic review and meta-analysis. Ann Intern Med. 2016;164(11):740-751. doi:10.7326/M15-2650
32. Zannad F, Cannon CP, Cushman WC, et al; EXAMINE Investigators. Heart failure and mortality outcomes in patients with type 2 diabetes taking alogliptin versus placebo in EXAMINE: a multicentre, randomised, double-blind trial. Lancet. 2015;385(9982):2067-2076. doi:10.1016/S0140-6736(14)62225-X
33. Roussel R, Duran-García S, Zhang Y, et al. Double-blind, randomized clinical trial comparing the efficacy and safety of continuing or discontinuing the dipeptidyl peptidase-4 inhibitor sitagliptin when initiating insulin glargine therapy in patients with type 2 diabetes: the CompoSIT-I study. Diabetes Obes Metab. 2019;21(4):781-790. doi:10.1111/dom.13574
34. McNeill AM, Davies G, Kruger E, et al. Ertugliflozin compared to other anti-hyperglycemic agents as monotherapy and add-on therapy in type 2 diabetes: a systematic literature review and network meta-analysis. Diabetes Ther. 2019;10(2):473-491. doi:10.1007/s13300-019-0566-x
35. Marso SP, Daniels GH, Brown-Frandsen K, et al; LEADER Steering Committee; LEADER Trial Investigators. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2016;375(4):311-322. doi:10.1056/NEJMoa1603827
36. FDA online label repository. FDA. Accessed February 20, 2020. https://labels.fda.gov/
37. Johnston R, Uthman O, Cummins E, et al. Canagliflozin, dapagliflozin, and empagliflozin monotherapy for treating type 2 diabetes. National Institute for Health and Care Excellence. October 4, 2015. Accessed July 28, 2021. https://www.nice.org.uk/guidance/ta390/documents/assessment-report
38. Beaudet A, Clegg J, Thuresson PO, Lloyd A, McEwan P. Review of utility values for economic modeling in type 2 diabetes. Value Health. 2014;17(4):462-470. doi:10.1016/j.jval.2014.03.003
39. Evans M, Khunti K, Mamdani M, et al. Health-related quality of life associated with daytime and nocturnal hypoglycaemic events: a time trade-off survey in five countries. Health Qual Life Outcomes. 2013;11:90. doi:10.1186/1477-7525-11-90
40. Foos V, McEwan P. Conversion of hypoglycemia utility decrements from categorical units reflecting event history into event specific disutility scores applicable to diabetes decision models. Value in Health. 2018;21(suppl 1):S223. doi:10.1016/j.jval.2018.04.1506
41. Marrett E, Radican L, Davies MJ, Zhang Q. Assessment of severity and frequency of self-reported hypoglycemia on quality of life in patients with type 2 diabetes treated with oral antihyperglycemic agents: a survey study. BMC Res Notes. 2011;4:251. doi:10.1186/1756-0500-4-251
42. Pawaskar M, Bilir SP, Kowal S, Gonzalez C, Rajpathak S, Davies G. Cost-effectiveness of DPP-4 inhibitor and SGLT2 inhibitor combination therapy for type 2 diabetes. Am J Manag Care. 2019;25(5):231-238.
43. Lauridsen JT, Lønborg J, Gundgaard J, Jensen HH. Diminishing marginal disutility of hypoglycaemic events: results from a time trade-off survey in five countries. Qual Life Res. 2014;23(9):2645-2650. doi:10.1007/s11136-014-0712-x
44. Medi-Span Price Rx. Accessed August 15, 2018. https://pricerx.medispan.com/IngredientName.aspx
45. Schütt M, Kern W, Krause U, et al; DPV Initiative. Is the frequency of self-monitoring of blood glucose related to long-term metabolic control? multicenter analysis including 24,500 patients from 191 centers in Germany and Austria. Exp Clin Endocrinol Diabetes. 2006;114(7):384-388. doi:10.1055/s-2006-924152
46. ADW Diabetes. Accessed September 12, 2018. https://www.adwdiabetes.com/
47. DMEPOS fee schedule. CMS. Accessed September 15, 2018. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/DMEPOSFeeSched/DMEPOS-Fee-Schedule.html
48. Clinical laboratory fee schedule. CMS. Accessed September 15, 2018. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ClinicalLabFeeSched/Clinical-Laboratory-Fee-Schedule-Files
49. Physician fee schedule. CMS. Accessed September 15, 2018. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/PhysicianFeeSched
50. Yeaw J, Halinan S, Hines D, et al. Direct medical costs for complications among children and adults with diabetes in the US commercial payer setting. Appl Health Econ Health Policy. 2014;12(2):219-230. doi:10.1007/s40258-014-0086-9
51. Bailey TS, Takács R, Tinahones FJ, et al. Efficacy and safety of switching from sitagliptin to liraglutide in subjects with type 2 diabetes (LIRA-SWITCH): a randomized, double-blind, double-dummy, active-controlled 26-week trial. Diabetes Obes Metab. 2016;18(12):1191-1198. doi:10.1111/dom.12736
52. Matza LS, Boye KS, Yurgin N, et al. Utilities and disutilities for type 2 diabetes treatment-related attributes. Qual Life Res. 2007;16(7):1251-1265. doi:10.1007/s11136-007-9226-0
53. Carls GS, Tuttle E, Tan RD, et al. Understanding the gap between efficacy in randomized controlled trials and effectiveness in real-world use of GLP-1 RA and DPP-4 therapies in patients with type 2 diabetes. Diabetes Care. 2017;40(11):1469-1478. doi:10.2337/dc16-2725
54. Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness—the curious resilience of the $50,000-per-QALY threshold. N Engl J Med. 2014;371(9):796-797. doi:10.1056/NEJMp1405158
55. Braithwaite RS, Meltzer DO, King JT Jr, Leslie D, Roberts MS. What does the value of modern medicine say about the $50,000 per quality-adjusted life-year decision rule? Med Care. 2008;46(4):349-356. doi:10.1097/MLR.0b013e31815c31a7
56. Final value assessment framework: updates for 2017-2019. Institute for Clinical and Economic Review.2017. Accessed July 28, 2021. https://icer.org/wp-content/uploads/2020/10/ICER-value-assessment-framework-Updated-050818.pdf
57. Current and new approaches to making drugs more affordable. CVS Health. August 2018. Accessed July 27, 2021. https://cvshealth.com/sites/default/files/cvs-health-current-and-new-approaches-to-making-drugs-more-affordable.pdf
58. Billings LK, Doshi A, Gouet D, et al. Efficacy and safety of IDegLira versus basal-bolus insulin therapy in patients with type 2 diabetes uncontrolled on metformin and basal insulin: the DUAL VII randomized clinical trial. Diabetes Care. 2018;41(5):1009-1016. doi:10.2337/dc17-1114
59. Fritsche A, Larbig M, Owens D, Häring HU; GINGER Study Group. Comparison between a basal-bolus and a premixed insulin regimen in individuals with type 2 diabetes—results of the GINGER study. Diabetes Obes Metab. 2010;12(2):115-123. doi:10.1111/j.1463-1326.2009.01165.x
60. Maiorino MI, Chiodini P, Bellastella G, Capuano A, Esposito K, Giugliano D. Insulin and glucagon-like peptide 1 receptor agonist combination therapy in type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials. Diabetes Care. 2017;40(4):614-624. doi:10.2337/dc16-1957
61. Perkovic V, Jardine MJ, Neal B, et al; CREDENCE Trial Investigators. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med. 2019;380(24):2295-2306. doi:10.1056/NEJMoa1811744
62. Oral semaglutide for type 2 diabetes: effectiveness and value. evidence report. Institute for Clinical and Economic Review. November 1, 2019. Accessed July 27, 2021. https://icer.org/wp-content/uploads/2020/10/ICER_Diabetes_Final-Evidence-Report_120919-1.pdf