Addressing Regional Differences in Diabetes Progression: Global Calibration for Diabetes Simulation Model

Adaptation of risk prediction equations for cardiovascular outcomes among patients with type 2 diabetes in real-world settings: a cross-institutional study using common data model approach

OBJECTIVE

To adapt risk prediction equations for myocardial infarction (MI), stroke, and heart failure (HF) among patients with type 2 diabetes in real-world settings using cross-institutional electronic health records (EHRs) in Taiwan.

METHODS

The EHRs from two medical centers, National Cheng Kung University Hospital (NCKUH; 11,740 patients) and National Taiwan University Hospital (NTUH; 20,313 patients), were analyzed using the common data model approach. Risk equations for MI, stroke, and HF from UKPDS-OM2, RECODe, and CHIME models were adapted for external validation and recalibration. External validation was assessed by (1) discrimination, evaluated by the area under the receiver operating characteristic curve (AUROC) and (2) calibration, evaluated by calibration slopes and intercepts and the Greenwood-Nam-D'Agostino (GND) test. Recalibration was conducted for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of original equations to address variations in patients' cardiovascular risks across institutions.

RESULTS

The CHIME risk equations had acceptable discrimination (AUROC: 0.71-0.79) and better calibration than that for UKPDS-OM2 and RECODe, although the calibration remained unsatisfactory. After recalibration, the calibration slopes/intercepts of the CHIME-MI, CHIME-stroke, and CHIME-HF risk equations were 0.9848/- 0.0008, 1.1003/- 0.0046, and 0.9436/0.0063 in the NCKUH population and 1.1060/- 0.0011, 0.8714/0.0030, and 1.0476/- 0.0016 in the NTUH population, respectively. All the recalibrated risk equations showed satisfactory calibration (p-values of GND tests ≥ 0.05).

CONCLUSIONS

We provide valid risk prediction equations for MI, stroke, and HF outcomes in Taiwanese type 2 diabetes populations. A framework for adapting risk equations across institutions is also proposed.

An exploratory analysis of the cost-effectiveness of insulin glargine 300 units/mL versus insulin glargine 100 units/mL over a lifetime horizon using the BRAVO diabetes model

BACKGROUND

This analysis assessed the cost-effectiveness of insulin glargine 300 units/mL (Gla-300) versus insulin glargine 100 units/mL (Gla-100) in insulin-naïve adults with type 2 diabetes (T2D) inadequately controlled with oral antidiabetic drugs (OADs).

METHODS

Costs and outcomes for Gla-300 versus Gla-100 from a US healthcare payer perspective were assessed using the BRAVO diabetes model. Baseline clinical data were derived from EDITION-3, a 12-month randomized controlled trial comparing Gla-300 with Gla-100 in insulin-naïve adults with inadequately controlled T2D on OADs. Treatment costs were calculated based on doses observed in EDITION-3 and 2020 US net prices, while costs for complications were based on published literature. Lifetime costs ($US) and quality-adjusted life-years (QALYs) were predicted and used to calculate incremental cost-effectiveness ratio (ICER) estimates; extensive scenario and sensitivity analyses were conducted.

RESULTS

Lifetime medical costs were estimated to be $353,441 and $352,858 for individuals receiving Gla-300 and Gla-100 respectively; insulin costs were $52,613 and $50,818. Gla-300 was associated with a gain of 8.97 QALYs and 21.12 life-years, while Gla-100 was associated with a gain of 8.89 QALYs and 21.07 life-years. This resulted in an ICER of $7522/QALY gained for Gla-300 versus Gla-100. Thus, Gla-300 was cost-effective versus Gla-100 based on a willingness-to-pay threshold of $50,000/QALY. Compared with Gla-100, Gla-300 provided a net monetary benefit of $3290. Scenario and sensitivity analyses confirmed the robustness of the base case.

CONCLUSION

Gla-300 may be a cost-effective treatment option versus Gla-100 over a lifetime horizon for insulin-naïve people in the United States with T2D inadequately controlled on OADs.

5-Year simulation of diabetes-related complications in people treated with tirzepatide or semaglutide versus insulin glargine

AIM

This study compared the 5-year incidence rate of macrovascular and microvascular complications for tirzepatide, semaglutide and insulin glargine in individuals with type 2 diabetes, using the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes simulation model.

RESEARCH DESIGN AND METHODS

This study was a 5-year SURPASS-2 trial extrapolation, with an insulin glargine arm added as an additional comparator. The 1-year treatment effects of tirzepatide (5, 10 or 15 mg), semaglutide (1 mg) and insulin glargine on glycated haemoglobin, systolic blood pressure, low-density lipoprotein and body weights were obtained from the SUSTAIN-4 and SURPASS-2 trials. We used the BRAVO model to predict 5-year complications for each study arm under two scenarios: the 1-year treatment effects persisted (optimistic) or diminished to none in 5 years (conservative).

RESULTS

When compared with insulin glargine, we projected a 5-year risk reduction in cardiovascular adverse events [rate ratio (RR) 0.64, 95% confidence interval (CI) 0.61-0.67] and microvascular composite (RR 0.67, 95% CI 0.64-0.70) with 15 mg tirzepatide, and 5-year risk reduction in cardiovascular adverse events (RR 0.75, 95% CI 0.72-0.79) and microvascular composite (RR 0.79, 95% CI 0.76-0.82) with semaglutide (1 mg) under an optimistic scenario. Lower doses of tirzepatide also had similar, albeit smaller benefits. Treatment effects for tirzepatide and semaglutide were smaller but still significantly higher than insulin glargine under a conservative scenario. The 5-year risk reduction in diabetes-related complication events and mortality for the 15 mg tirzepatide compared with insulin glargine ranged from 49% to 10% under an optimistic scenario, which was reduced by 17%-33% when a conservative scenario was assumed.

CONCLUSION

With the use of the BRAVO diabetes model, tirzepatide and semaglutide exhibited potential to reduce the risk of macrovascular and microvascular complications among individuals with type 2 diabetes, compared with insulin glargine in a 5-year window. Based on the current modelling assumptions, tirzepatide (15 mg) may potentially outperform semaglutide (1 mg). While the BRAVO model offered insights, the long-term cardiovascular benefit of tirzepatide should be further validated in a prospective clinical trial.

Cost-effectiveness analysis of once-daily insulin glargine 300 U/mL versus insulin degludec 100 U/mL using the BRAVO diabetes model

AIMS

A cost-effectiveness analysis was conducted to compare insulin glargine 300 U/mL (Gla-300) versus insulin degludec 100 U/mL (IDeg-100) in insulin-naïve adults with type 2 diabetes (T2D) sub-optimally controlled with oral anti-diabetic drugs (OADs).

METHODS

The BRAVO diabetes model was used to assess costs and outcomes for once-daily Gla-300 versus once-daily IDeg-100 from a US healthcare sector perspective. Baseline clinical data were based on BRIGHT, a 24-week, non-inferiority, randomised control trial comparing Gla-300 and IDeg-100 in adults with T2D sub-optimally controlled with OADs (with or without glucagon-like peptide-1 receptor agonists). Treatment costs were based on doses observed in BRIGHT as well as net prices. Costs associated with complications were based on published literature. Lifetime costs (US$) and quality-adjusted life-years (QALYs) were predicted and used to calculate incremental cost-effectiveness ratio estimates; extensive scenario and sensitivity analyses were conducted.

RESULTS

Overall lifetime medical costs were estimated to be $327,904 and $330,154 for people receiving Gla-300 and IDeg-100, respectively; insulin costs were $43,477 and $44,367, respectively. People receiving Gla-300 gained 8.024 QALYs and 18.55 life-years, while people receiving IDeg-100 gained 7.997 QALYs and 18.52 life-years. Because Gla-300 was associated with a cost-saving of $2250 and 0.027 additional QALYs, it was considered to be dominant compared with IDeg-100. Results of the scenario and sensitivity analyses confirmed the robustness of the base case results.

CONCLUSION

Gla-300 was the dominant treatment option compared with IDeg-100 based on the willingness-to-pay threshold of $50,000/QALY. Results remained robust against a wide range of alternative assumptions on key parameters.

Cardiorenal diseases in type 2 diabetes mellitus: clinical trials and real-world practice

Patients with type 2 diabetes mellitus (T2DM) can have multiple comorbidities and premature mortality due to atherosclerotic cardiovascular disease, hospitalization with heart failure and/or chronic kidney disease. Traditional drugs that lower glucose, such as metformin, or that treat high blood pressure and blood levels of lipids, such as renin-angiotensin-system inhibitors and statins, have organ-protective effects in patients with T2DM. Amongst patients with T2DM treated with these traditional drugs, randomized clinical trials have confirmed the additional cardiorenal benefits of sodium-glucose co-transporter 2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1RA) and nonsteroidal mineralocorticoid receptor antagonists. The cardiorenal benefits of SGLT2i extended to patients with heart failure and/or chronic kidney disease without T2DM, whereas incretin-based therapy (such as GLP1RA) reduced cardiovascular events in patients with obesity and T2DM. However, considerable care gaps exist owing to insufficient detection, therapeutic inertia and poor adherence to these life-saving medications. In this Review, we discuss the complex interconnections of cardiorenal-metabolic diseases and strategies to implement evidence-based practice. Furthermore, we consider the need to conduct clinical trials combined with registers in specific patient segments to evaluate existing and emerging therapies to address unmet needs in T2DM.

Lowering hemoglobin A1c level to less than 6.0% in people with type 2 diabetes may reduce major adverse cardiovascular events: a Bayesian's narrative

Whether lowering the hemoglobin A1c to <6.0% in patients with type 2 diabetes can reduce the risk of cardiovascular disease (CVD) remains under debate. The ACCORDION and the VADT studies both found reductions in the primary CVD composite associated with intensive glycemic control, though the difference is not statistically significant. However, the lack of significance is often overinterpreted as non-effective: a p-value >.05 only implies that the study "failed to reject" the null hypothesis (i.e. lowering the A1c level to <6.0% results in no CVD benefit), which is different from concluding the null hypothesis being true. In this study, we used Bayesian analysis to reanalyze results from the ACCORDION and VADT-15 trials. Our results suggest achieving an A1c goal of <6.0% as compared to moderate control could result in a moderate risk reduction in MACE.

Using modern risk engines and machine learning/artificial intelligence to predict diabetes complications: A focus on the BRAVO model

Management of diabetes requires a multifaceted approach of risk factor reduction; through management of risk factors such as glucose, blood pressure and cholesterol. Goals for these risk factors often vary and guidelines suggest that this is based on patient characteristics and need to be individualized. Evaluating risk is therefore critically important to determine goals and choose appropriate treatments. A risk engine is an analytic tool that collects a large amount of population data allowing the simulation of the progression of diabetes with set equations over a period of time. Recently, a number of data cohorts have become available, leading to the development of newer risk engines that are more dynamic and generalizable. An example is the Building, Relating, Assessing, and Validating Outcomes in (BRAVO) diabetes model which was built on the ACCORD trial database. It is capable of accurately predicting diabetes comorbidities in an international population based on calibration with international clinical trial data. It has potential uses in risk stratification of patients, evaluation of interventions and calculation of their long term cost effectiveness. Recently, it has been used to simulate long term outcomes based on short term data, using difference modelling scenarios.

Projected Impact of the Medicare Part D Senior Savings Model on Diabetes-Related Health and Economic Outcomes Among Insulin Users Covered by Medicare

OBJECTIVE

The Medicare Part D Senior Savings Model (SSM) took effect on 1 January 2021. In this study we estimated the number of beneficiaries who would benefit from SSM and the long-term health and economic consequences of implementing this new policy.

RESEARCH DESIGN AND METHODS

Data for Medicare beneficiaries with diabetes treated with insulin were extracted from the 2018 Medical Expenditure Panel Survey. A validated diabetes microsimulation model estimated health and economic impacts of the new policy for the 5-year initial implementation period and a 20-year extended policy horizon. Costs were estimated from a health system perspective.

RESULTS

Of 4.2 million eligible Medicare beneficiaries, 1.6 million (38.3%) would benefit from the policy, and out-of-pocket (OOP) costs per year per beneficiary would decrease by 61% or $500 on average. Compared with non-White subgroups, the White population subgroups would have a higher proportion of SSM enrollees (29.6% vs. 43.7%) and a higher annual OOP cost reduction (reduction of $424 vs. $531). Among the SSM enrollees, one-third (605,125) were predicted to have improved insulin adherence due to lower cost sharing and improved health outcomes. In 5 years, the SSM would 1) avert 2,014 strokes, 935 heart attacks, 315 heart failure cases, and 344 end-stage renal disease cases; 2) gain 3,220 life-years and 3,381 quality-adjusted life-years (QALY); and 3) increase insulin cost and total medical cost by $3.5 billion and $2.8 billion. In 20 years, the number of avoided clinical outcomes, number of life-years and QALY gained, and the total and insulin cost would be larger.

CONCLUSIONS

The Medicare SSM may reduce the OOP costs for approximately one-third of the Medicare beneficiaries treated with insulin, improving health outcomes via increased insulin adherence. However, the SSM will also increase overall Medicare spending for insulin and overall medical costs, which may impact future premiums and benefits. Our findings can inform policy makers about the potential impact of the new Medicare SSM.

The transferability and validity of a population-level simulation model for the economic evaluation of interventions in diabetes: the MICADO model

AIMS

Valid health economic models are essential to inform the adoption and reimbursement of therapies for diabetes mellitus. Often existing health economic models are applied in other countries and settings than those where they were developed. This practice requires assessing the transferability of a model developed from one setting to another. We evaluate the transferability of the MICADO model, developed for the Dutch 2007 setting, in two different settings using a range of adjustment steps. MICADO predicts micro- and macrovascular events at the population level.

METHODS

MICADO simulation results were compared to observed events in an Italian 2000-2015 cohort (Casale Monferrato Survey [CMS]) and in a Dutch 2008-2019 (Hoorn Diabetes Care Center [DCS]) cohort after adjusting the demographic characteristics. Additional adjustments were performed to: (1) risk factors prevalence at baseline, (2) prevalence of complications, and (3) all-cause mortality risks by age and sex. Model validity was assessed by mean average percentage error (MAPE) of cumulative incidences over 10 years of follow-up, where lower values mean better accuracy.

RESULTS

For mortality, MAPE was lower for CMS compared to DCS (0.38 vs. 0.70 following demographic adjustment) and adjustment step 3 improved it to 0.20 in CMS, whereas step 2 showed best results in DCS (0.65). MAPE for heart failure and stroke in DCS were 0.11 and 0.22, respectively, while for CMS was 0.42 and 0.41.

CONCLUSIONS

The transferability of the MICADO model varied by event and per cohort. Additional adjustments improved prediction of events for MICADO. To ensure a valid model in a new setting it is imperative to assess the impact of adjustments in terms of model accuracy, even when this involves the same country, but a new time period.

Potential Gains in Life Expectancy Associated With Achieving Treatment Goals in US Adults With Type 2 Diabetes

Importance

Improvements in control of factors associated with diabetes risk in the US have stalled and remain suboptimal. The benefit of continually improving goal achievement has not been evaluated to date.

Objective

To quantify potential gains in life expectancy (LE) among people with type 2 diabetes (T2D) associated with lowering glycated hemoglobin (HbA1c), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), and body mass index (BMI) toward optimal levels.

Design, Setting, and Participants

In this decision analytical model, the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes microsimulation model was calibrated to a nationally representative sample of adults with T2D from the National Health and Nutrition Examination Survey (2015-2016) using their linked short-term mortality data from the National Death Index. The model was then used to conduct the simulation experiment on the study population over a lifetime. Data were analyzed from January to October 2021.

Exposure

The study population was grouped into quartiles on the basis of levels of HbA1c, SBP, LDL-C, and BMI. LE gains associated with achieving better control were estimated by moving people with T2D from the current quartile of each biomarker to the lower quartiles.

Main Outcomes and Measures

Life expectancy.

Results

Among 421 individuals, 194 (46%) were women, and the mean (SD) age was 65.6 (8.9) years. Compared with a BMI of 41.4 (mean of the fourth quartile), lower BMIs of 24.3 (first), 28.6 (second), and 33.0 (third) were associated with 3.9, 2.9, and 2.0 additional life-years, respectively, in people with T2D. Compared with an SBP of 160.4 mm Hg (fourth), lower SBP levels of 114.1 mm Hg (first), 128.2 mm Hg (second), and 139.1 mm Hg (third) were associated with 1.9, 1.5, and 1.1 years gained in LE in people with T2D, respectively. A lower LDL-C level of 59 mg/dL (first), 84.0 mg/dL (second), and 107.0 mg/dL (third) were associated with 0.9, 0.7, and 0.5 years gain in LE, compared with LDL-C of 146.2 mg/dL (fourth). Reducing HbA1c from 9.9% (fourth) to 7.7% (third) was associated with 3.4 years gain in LE. However, a further reduction to 6.8% (second) was associated with only a mean of 0.5 years gain in LE, and from 6.8% to 5.9% (first) was not associated with LE benefit. Overall, reducing HbA1c from the fourth quartile to the first is associated with an LE gain of 3.8 years.

Conclusions and Relevance

These findings can be used by clinicians to motivate patients in achieving the recommended treatment goals and to help prioritize interventions and programs to improve diabetes care in the US.

Efficacy of iGlarLixi on 5-year risk of diabetes-related complications: A simulation study

OBJECTIVE

This study simulated the 5-year risk of diabetes complications associated with the use of iGlarLixi, a fixed-ratio combination of insulin glargine 100 U/ml and lixisenatide, in type 2 diabetes (T2D) using the BRAVO diabetes model.

METHODS

The six-month efficacy data of iGlarLixi and Standard of Care (SOC) were extracted from the LixiLan-O (NCT02058147) and ORIGIN (NCT00069784) trials, respectively. The trial participants' baseline characteristics were standardized to the ACCORD trial through a matching method. The BRAVO diabetes simulation model was used to project the 5-year complications based on T2D people baseline characteristics and treatment efficacy. An optimistic scenario where the six-month relative efficacy of iGlarLixi (i.e., iGlarlixi-SOC) lasted for 5 years, and a conservative scenario where the relative effect of iGlarLixi waned to none within 5 years were simulated.

RESULTS

iGlarLixi compared with SOC was found to reduce HbA1c (-1.4%), SBP (-3.4 mm Hg), and BMI (-0.6 kg/m²) in six months. We simulated a 5-year risk reduction in major adverse cardiovascular events (MACE) (relative risk [RR] 0.77, 95% CI: 0.67-0.88), and all-cause mortality (RR 0.94, 95% CI: 0.92-0.96) under an optimistic scenario, and MACE (RR 0.86, 95% CI: 0.75-0.96), and all-cause mortality (RR 0.96, 95% CI: 0.92-0.98) under a conservative scenario.

CONCLUSIONS

The long-term use of iGlarLixi may lead to a substantial reduction in diabetes-related complications among people with T2D at elevated risk for CVD. The use of a simulation model to evaluate outcomes of treatment in a well-characterized patient cohort is novel. Such an approach may serve as a template for future evaluation of medications and combinations when the effect of a treatment is known, but a long-term outcome trial is not feasible.

Impact of Quality Improvement (QI) Program on 5-Year Risk of Diabetes-Related Complications: A Simulation Study

OBJECTIVE

We successfully implemented the American Diabetes Association's (ADA) Diabetes INSIDE (INspiring System Improvement with Data-Driven Excellence) quality improvement (QI) program at a university hospital and safety-net health system (Tulane and Parkland), focused on system-wide improvement in poorly controlled type 2 diabetes (HbA1c >8.0% [64 mmol/mol]). In this study, we estimated the 5-year risk reduction in complications and mortality associated with the QI program.

RESEARCH DESIGN AND METHODS

The QI implementation period was 1 year, followed by the postintervention period of 6 months to evaluate the impact of QI on clinical measures. We measured the differences between the baseline and postintervention clinical outcomes in 2,429 individuals with HbA1c >8% (64 mmol/mol) at baseline and used the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes model to project the 5-year risk reduction of diabetes-related complications under the assumption that intervention benefits persist over time. An alternative assumption that intervention benefits diminish by 30% every year was also tested.

RESULTS

The QI program was associated with reductions in HbA1c (-0.84%) and LDL cholesterol (LDL-C) (-5.94 mg/dL) among individuals with HbA1c level >8.0% (64 mmol/mol), with greater reduction in HbA1c (-1.67%) and LDL-C (-6.81 mg/dL) among those with HbA1c level >9.5% at baseline (all P < 0.05). The implementation of the Diabetes INSIDE QI program was associated with 5-year risk reductions in major adverse cardiovascular events (MACE) (relative risk [RR] 0.78 [95% CI 0.75-0.81]) and all-cause mortality (RR 0.83 [95% CI 0.82-0.85]) among individuals with baseline HbA1c level >8.0% (64 mmol/mol), and MACE (RR 0.60 [95% CI 0.56-0.65]) and all-cause mortality (RR 0.61 [95% CI 0.59-0.64]) among individuals with baseline HbA1c level >9.5% (80 mmol/mol). Sensitivity analysis also identified a substantially lower risk of diabetes-related complications and mortality associated with the QI program.

CONCLUSIONS

Our modeling results suggest that the ADA's Diabetes INSIDE QI program would benefit the patients and population by substantially reducing the 5-year risk of complications and mortality in individuals with diabetes.

Characterizing the Validity and Real-World Utility of Health Technology Assessments in Healthcare: Future Directions Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modelling"

With their article, Grutters et al raise an important question: What do successful health technology assessments (HTAs) look like, and what is their real-world utility in decision-making? While many HTAs are published in peer-reviewed journals, many are considered proprietary and their attributes remain confidential, limiting researchers’ ability to answer these questions. Models for economic evaluations like cost-effectiveness analyses (CEAs) synthesize a wide range of evidence, are often statistically and mathematically sophisticated, and require untestable assumptions. As such, there is nearly universal agreement among researchers that enhancing transparency is an important issue in health economic modeling. However, the definition of transparency and guidelines for its implementation vary. Model registration combined with a linked database of model-based economic evaluations has been proposed as a solution, whereby registered models and their accompanying technical and nontechnical documentation are sourced into a single publicly-available repository, ideally in a standardized format to ensure consistent and complete representation of features, code, data sources, results, validation exercises, and policy recommendations. When such a repository is ultimately created, modelers will not have to reinvent the wheel for every new drug launched or new treatment pathway. These more open and transparent approaches will have substantial implications for model accuracy, reliability, and validity, improving trust and acceptance by healthcare decision-makers.