Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)

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.

DCRM 2.0: Multispecialty practice recommendations for the management of diabetes, cardiorenal, and metabolic diseases

The spectrum of cardiorenal and metabolic diseases comprises many disorders, including obesity, type 2 diabetes (T2D), chronic kidney disease (CKD), atherosclerotic cardiovascular disease (ASCVD), heart failure (HF), dyslipidemias, hypertension, and associated comorbidities such as pulmonary diseases and metabolism dysfunction-associated steatotic liver disease and metabolism dysfunction-associated steatohepatitis (MASLD and MASH, respectively, formerly known as nonalcoholic fatty liver disease and nonalcoholic steatohepatitis [NAFLD and NASH]). Because cardiorenal and metabolic diseases share pathophysiologic pathways, two or more are often present in the same individual. Findings from recent outcome trials have demonstrated benefits of various treatments across a range of conditions, suggesting a need for practice recommendations that will guide clinicians to better manage complex conditions involving diabetes, cardiorenal, and/or metabolic (DCRM) diseases. To meet this need, we formed an international volunteer task force comprising leading cardiologists, nephrologists, endocrinologists, and primary care physicians to develop the DCRM 2.0 Practice Recommendations, an updated and expanded revision of a previously published multispecialty consensus on the comprehensive management of persons living with DCRM. The recommendations are presented as 22 separate graphics covering the essentials of management to improve general health, control cardiorenal risk factors, and manage cardiorenal and metabolic comorbidities, leading to improved patient outcomes.

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.

The Determination of Diabetes Utilities, Costs, and Effects Model: A Cost-Utility Tool Using Patient-Level Microsimulation to Evaluate Sensor-Based Glucose Monitoring Systems in Type 1 and Type 2 Diabetes: Comparative Validation

OBJECTIVES

To assess the accuracy and validity of the Determination of Diabetes Utilities, Costs, and Effects (DEDUCE) model, a Microsoft-Excel-based tool for evaluating diabetes interventions for type 1 and type 2 diabetes.

METHODS

The DEDUCE model is a patient-level microsimulation, with complications predicted based on the Sheffield and Risk Equations for Complications Of type 2 diabetes models for type 1 and type 2 diabetes, respectively. For this tool to be useful, it must be validated to ensure that its complication predictions are accurate. Internal, external, and cross-validation was assessed by populating the DEDUCE model with the baseline characteristics and treatment effects reported in clinical trials used in the Fourth, Fifth, and Ninth Mount Hood Diabetes Challenges. Results from the DEDUCE model were evaluated against clinical results and previously validated models via mean absolute percentage error or percentage error.

RESULTS

The DEDUCE model performed favorably, predicting key outcomes, including cardiovascular disease in type 1 diabetes and all-cause mortality in type 2 diabetes. The model performed well against other models. In the Mount Hood 9 Challenge comparison, error was below the mean reported from comparator models for several outcomes, particularly for hazard ratios.

CONCLUSIONS

The DEDUCE model predicts diabetes-related complications from trials and studies well when compared with previously validated models. The model may serve as a useful tool for evaluating the cost-effectiveness of diabetes technologies.

The Health Economics of Automated Insulin Delivery Systems and the Potential Use of Time in Range in Diabetes Modeling: A Narrative Review

Intensive therapy with exogenous insulin is the treatment of choice for individuals living with type 1 diabetes (T1D) and some with type 2 diabetes, alongside regular glucose monitoring. The development of systems allowing (semi-)automated insulin delivery (AID), by connecting glucose sensors with insulin pumps and algorithms, has revolutionized insulin therapy. Indeed, AID systems have demonstrated a proven impact on overall glucose control, as indicated by effects on glycated hemoglobin (HbA1c), risk of severe hypoglycemia, and quality of life measures. An alternative endpoint for glucose control that has arisen from the use of sensor-based continuous glucose monitoring is the time in range (TIR) measure, which offers an indication of overall glucose control, while adding information on the quality of control with regard to blood glucose level stability. A review of literature on the health-economic value of AID systems was conducted, with a focus placed on the growing place of TIR as an endpoint in studies involving AID systems. Results showed that the majority of economic evaluations of AID systems focused on individuals with T1D and found AID systems to be cost-effective. Most studies incorporated HbA1c, rather than TIR, as a clinical endpoint to determine treatment effects on glucose control and subsequent quality-adjusted life year (QALY) gains. Likely reasons for the choice of HbA1c as the chosen endpoint is the use of this metric in most validated and established economic models, as well as the limited publicly available evidence on appropriate methodologies for TIR data incorporation within conventional economic evaluations. Future studies could include the novel TIR metric in health-economic evaluations as an additional measure of treatment effects and subsequent QALY gains, to facilitate a holistic representation of the impact of AID systems on glycemic control. This would provide decision makers with robust evidence to inform future recommendations for health care interventions.

Cost-Effectiveness Analysis of a Prescription Digital Therapeutic in Type 2 Diabetes

INTRODUCTION

BT-001 (AspyreRx™) prescription digital therapy, a form of personalized cognitive behavioral therapy, has demonstrated clinically meaningful and durable hemoglobin A1c reductions in patients with type 2 diabetes (T2D). The current study examined the cost-effectiveness of BT-001 plus standard of care (SoC) versus SoC alone in T2D over a lifetime horizon from a healthcare payer perspective.

METHODS

We modeled the T2D pathway using an individual patient-level simulation; clinical data were sourced from the intention-to-treat subset of the BT-001 randomized clinical trial (RCT). SoC across both arms included the composition of oral and injectable treatments for T2D. Events were simulated using the United Kingdom Prospective Diabetes Study Outcomes Model 2 risk equation. A 3-month model cycle length was used in the first year, then annual model cycles were used in line with the original risk engine specifications. Patient characteristics informed event equations and Monte Carlo random sampling was used to assess the occurrence of events within each model cycle. Incidence of hypoglycemic events, drug discontinuation, costs, and health utilities and disutility values were sourced from the literature.

RESULTS

From a payer perspective, BT-001 plus SoC versus SoC alone was dominant with a gain in quality-adjusted life years (QALYs) of 0.101 and cost savings of $7343 per patient over the lifetime horizon (i.e., more effective and less costly). BT-001 plus SoC was cost-effective at a willingness-to-pay of $100,000 per QALY (incremental net monetary benefit was $17,443). Savings with BT-001 were primarily driven by a reduction in drug acquisition costs. The reduction in hemoglobin A1c with BT-001 was associated with fewer T2D complications.

CONCLUSIONS

BT-001 plus SoC was estimated to dominate SoC alone over the lifetime horizon from a payer perspective, suggesting that using BT-001 can empower patients to better manage their diabetes with the potential for lifelong advantages.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

A Systematic Review of Methodologies Used in Models of the Treatment of Diabetes Mellitus

BACKGROUND

Diabetes mellitus is a chronic and complex disease, increasing in prevalence and consequent health expenditure. Cost-effectiveness models with long time horizons are commonly used to perform economic evaluations of diabetes' treatments. As such, prediction accuracy and structural uncertainty are important features in cost-effectiveness models of chronic conditions.

OBJECTIVES

The aim of this systematic review is to identify and review published cost-effectiveness models of diabetes treatments developed between 2011 and 2022 regarding their methodological characteristics. Further, it also appraises the quality of the methods used, and discusses opportunities for further methodological research.

METHODS

A systematic literature review was conducted in MEDLINE and Embase to identify peer-reviewed papers reporting cost-effectiveness models of diabetes treatments, with time horizons of more than 5 years, published in English between 1 January 2011 and 31 of December 2022. Screening, full-text inclusion, data extraction, quality assessment and data synthesis using narrative synthesis were performed. The Philips checklist was used for quality assessment of the included studies. The study was registered in PROSPERO (CRD42021248999).

RESULTS

The literature search identified 30 studies presenting 29 unique cost-effectiveness models of type 1 and/or type 2 diabetes treatments. The review identified 26 type 2 diabetes mellitus (T2DM) models, 3 type 1 DM (T1DM) models and one model for both types of diabetes. Fifteen models were patient-level models, whereas 14 were at cohort level. Parameter uncertainty was assessed thoroughly in most of the models, whereas structural uncertainty was seldom addressed. All the models where validation was conducted performed well. The methodological quality of the models with respect to structure was high, whereas with respect to data modelling it was moderate.

CONCLUSIONS

Models developed in the past 12 years for health economic evaluations of diabetes treatments are of high-quality and make use of advanced methods. However, further developments are needed to improve the statistical modelling component of cost-effectiveness models and to provide better assessment of structural uncertainty.

Prognostic models for heart failure in patients with type 2 diabetes: a systematic review and meta-analysis

OBJECTIVE

To provide a systematic review, critical appraisal, assessment of performance and generalisability of all the reported prognostic models for heart failure (HF) in patients with type 2 diabetes (T2D).

METHODS

We performed a literature search in Medline, Embase, Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Scopus (from inception to July 2022) and grey literature to identify any study developing and/or validating models predicting HF applicable to patients with T2D. We extracted data on study characteristics, modelling methods and measures of performance, and we performed a random-effects meta-analysis to pool discrimination in models with multiple validation studies. We also performed a descriptive synthesis of calibration and we assessed the risk of bias and certainty of evidence (high, moderate, low).

RESULTS

Fifty-five studies reporting on 58 models were identified: (1) models developed in patients with T2D for HF prediction (n=43), (2) models predicting HF developed in non-diabetic cohorts and externally validated in patients with T2D (n=3), and (3) models originally predicting a different outcome and externally validated for HF (n=12). RECODe (C-statistic=0.75 95% CI (0.72, 0.78), 95% prediction interval (PI) (0.68, 0.81); high certainty), TRS-HFDM (C-statistic=0.75 95% CI (0.69, 0.81), 95% PI (0.58, 0.87); low certainty) and WATCH-DM (C-statistic=0.70 95% CI (0.67, 0.73), 95% PI (0.63, 0.76); moderate certainty) showed the best performance. QDiabetes-HF demonstrated also good discrimination but was externally validated only once and not meta-analysed.

CONCLUSIONS

Among the prognostic models identified, four models showed promising performance and, thus, could be implemented in current clinical practice.

Effectiveness and cost-effectiveness of six GLP-1RAs for treatment of Chinese type 2 diabetes mellitus patients that inadequately controlled on metformin: a micro-simulation model

Objective

To systematically estimate and compare the effectiveness and cost-effectiveness of the glucagon-like peptide-1 receptor agonists (GLP-1RAs) approved in China and to quantify the relationship between the burden of diabetic comorbidities and glycosylated hemoglobin (HbA1c) or body mass index (BMI).

Methods

To estimate the costs (US dollars, USD) and quality-adjusted life years (QALY) for six GLP-1RAs (exenatide, loxenatide, lixisenatide, dulaglutide, semaglutide, and liraglutide) combined with metformin in the treatment of patients with type 2 diabetes mellitus (T2DM) which is inadequately controlled on metformin from the Chinese healthcare system perspective, a discrete event microsimulation cost-effectiveness model based on the Chinese Hong Kong Integrated Modeling and Evaluation (CHIME) simulation model was developed. A cohort of 30,000 Chinese patients was established, and one-way sensitivity analysis and probabilistic sensitivity analysis (PSA) with 50,000 iterations were conducted considering parameter uncertainty. Scenario analysis was conducted considering the impacts of research time limits. A network meta-analysis was conducted to compare the effects of six GLP-1RAs on HbA1c, BMI, systolic blood pressure, and diastolic blood pressure. The incremental net monetary benefit (INMB) between therapies was used to evaluate the cost-effectiveness. China's per capita GDP in 2021 was used as the willingness-to-pay threshold. A generalized linear model was used to quantify the relationship between the burden of diabetic comorbidities and HbA1c or BMI.

Results

During a lifetime, the cost for a patient ranged from USD 42,092 with loxenatide to USD 47,026 with liraglutide, while the QALY gained ranged from 12.50 with dulaglutide to 12.65 with loxenatide. Compared to exenatide, the INMB of each drug from highest to lowest were: loxenatide (USD 1,124), dulaglutide (USD -1,418), lixisenatide (USD -1,713), semaglutide (USD -4,298), and liraglutide (USD -4,672). Loxenatide was better than the other GLP-1RAs in the base-case analysis. Sensitivity and scenario analysis results were consistent with the base-case analysis. Overall, the price of GLP-1RAs most affected the results. Medications with effective control of HbA1c or BMI were associated with a significantly smaller disease burden (p < 0.05).

Conclusion

Loxenatide combined with metformin was identified as the most economical choice, while the long-term health benefits of patients taking the six GLP-1RAs are approximate.

A New Type 2 Diabetes Microsimulation Model to Estimate Long-Term Health Outcomes, Costs, and Cost-Effectiveness

OBJECTIVES

This study aimed to develop a microsimulation model to estimate the health effects, costs, and cost-effectiveness of public health and clinical interventions for preventing/managing type 2 diabetes.

METHODS

We combined newly developed equations for complications, mortality, risk factor progression, patient utility, and cost-all based on US studies-in a microsimulation model. We performed internal and external validation of the model. To demonstrate the model's utility, we predicted remaining life-years, quality-adjusted life-years (QALYs), and lifetime medical cost for a representative cohort of 10 000 US adults with type 2 diabetes. We then estimated the cost-effectiveness of reducing hemoglobin A1c from 9% to 7% among adults with type 2 diabetes, using low-cost, generic, oral medications.

RESULTS

The model performed well in internal validation; the average absolute difference between simulated and observed incidence for 17 complications was < 8%. In external validation, the model was better at predicting outcomes in clinical trials than in observational studies. The cohort of US adults with type 2 diabetes was projected to have an average of 19.95 remaining life-years (from mean age 61), incur $187 729 in discounted medical costs, and accrue 8.79 discounted QALYs. The intervention to reduce hemoglobin A1c increased medical costs by $1256 and QALYs by 0.39, yielding an incremental cost-effectiveness ratio of $9103 per QALY.

CONCLUSIONS

Using equations exclusively derived from US studies, this new microsimulation model achieves good prediction accuracy in US populations. The model can be used to estimate the long-term health impact, costs, and cost-effectiveness of interventions for type 2 diabetes in the United States.

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.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

External Validation of the BRAVO Diabetes Model Using the EXSCEL Clinical Trial Data

INTRODUCTION

We have developed the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes model, an individual-level, discrete-time microsimulation model specifically designed for type 2 diabetes (T2D) management. This study aims to validate the model's performance when populated exclusively with a fully de-identified dataset to ensure its applicability in secure settings.

METHODS

Patient-level data from the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial were fully de-identified by removing all identifiable information and masking numerical values (e.g., age, body mass index) within ranges to minimize the risk of re-identification. To populate the simulation, we imputed the masked numerical values using data from the National Health and Nutrition Examination Survey (NHANES). We applied the BRAVO model to the baseline data to predict 7-year study outcomes for the EXSCEL trial and assessed its discrimination power and calibration using C-statistics and Brier scores.

RESULTS

The model demonstrated acceptable discrimination and calibration in predicting the first occurrence of non-fatal myocardial infarction, non-fatal stroke, heart failure, revascularization, and all-cause mortality. Even with the fully deidentified data from the EXSCEL trial primarily presented in ranges rather than specific values, the BRAVO model exhibited robust prediction performance for diabetes complications and mortality.

CONCLUSIONS

This study demonstrates the feasibility of using the BRAVO model in settings where only fully de-identified patient-level data are available.

Prediction of complications in health economic models of type 2 diabetes: a review of methods used

AIM

Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions.

METHODS

PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated.

RESULTS

The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly.

CONCLUSIONS

The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

Predicting and preventing heart failure in type 2 diabetes

The burden of heart failure among people with type 2 diabetes is increasing globally. People with comorbid type 2 diabetes and heart failure often have worse outcomes than those with only one of these conditions-eg, higher hospitalisation and mortality rates. Therefore, it is essential to implement optimal heart failure prevention strategies for people with type 2 diabetes. A detailed understanding of the pathophysiology underlying the occurrence of heart failure in type 2 diabetes can aid clinicians in identifying relevant risk factors and lead to early interventions that can help prevent heart failure. In this Review, we discuss the pathophysiology and risk factors of heart failure in type 2 diabetes. We also review the risk assessment tools for predicting heart failure incidence in people with type 2 diabetes as well as the data from clinical trials that have assessed the efficacy of lifestyle and pharmacological interventions. Finally, we discuss the potential challenges in implementing new management approaches and offer pragmatic recommendations to help overcome these challenges.

Developing a prediction model for all-cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China

BACKGROUND

All-cause mortality risk prediction models for patients with type 2 diabetes mellitus (T2DM) in mainland China have not been established. This study aimed to fill this gap.

METHODS

Based on the Shanghai Link Healthcare Database, patients diagnosed with T2DM and aged 40-99 years were identified between January 1, 2013 and December 31, 2016 and followed until December 31, 2021. All the patients were randomly allocated into training and validation sets at a 2:1 ratio. Cox proportional hazards models were used to develop the all-cause mortality risk prediction model. The model performance was evaluated by discrimination (Harrell C-index) and calibration (calibration plots).

RESULTS

A total of 399 784 patients with T2DM were eventually enrolled, with 68 318 deaths over a median follow-up of 6.93 years. The final prediction model included age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, glycosylated hemoglobin, and high-density lipoprotein cholesterol. The model showed good discrimination and calibration in the validation sets: the mean C-index value was 0.8113 (range 0.8110-0.8115) and the predicted risks closely matched the observed risks in the calibration plots.

CONCLUSIONS

This study constructed the first 5-year all-cause mortality risk prediction model for patients with T2DM in south China, with good predictive performance.

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.

A machine learning approach identifies modulators of heart failure hospitalization prevention among patients with type 2 diabetes: A revisit to the ACCORD trial

BACKGROUND

To examine patient characteristics that may modulate the heterogeneous treatment effect of intensive systolic blood pressure control (SBP) and intensive glycemic control on incident heart failure (HF) risk in people with type 2 diabetes.

METHODS

We analyzed 10,251 participants from the ACCORD glucose trial, and 4733 from the SBP sub-trial separately. We applied a robust machine-learning (ML) algorithm, namely the causal forest/causal tree analysis, to each trial to identify participants' characteristics that modulate the effectiveness of each trial intervention.

RESULTS

Diastolic blood pressure (DBP) was found to interact with intensive glycemic control and impact outcomes. An increased HF risk associated with intensive glycemic control (absolute risk change (ARC): 2.28 %, 95 % confidence interval (CI): 0.69 % to 3.90 %; relative risk (RR):1.57, 95 % CI: 1.15 to 2.20; P < 0.05) was observed in individuals with baseline DBP at the lowest tertile (45-69 mmHg), while no changes in HF risk associated with intensive glycemic control were observed in individuals with baseline DBP at the middle (70-79 mmHg) and the highest tertiles (80-100 mmHg). Liver function was identified as a modulator of intensive BP control, and baseline Alanine transaminase (ALT) level was a sensitive marker for the modulating effect. Only individuals with baseline ALT at the lowest tertile (8-19 mg/dl) benefited from the intensive BP control for HF prevention (ARC: -1.95 %, 95 % CI: -4.06 % to 0.11 %; RR:0.62. 95 % CI: 0.27 to 0.94; P < 0.05).

CONCLUSIONS

Our study is the first to observe and quantify the potential synergistic harmful effect when low DBP was combined with an intensive blood glucose intervention. Recognizing these may help clinicians develop a more precise approach to such treatments, thus increasing the efficiency and outcomes of diabetes treatments.

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.

Clinical Prediction Models for Heart Failure Hospitalization in Type 2 Diabetes: A Systematic Review and Meta-Analysis

Background Clinical prediction models have been developed for hospitalization for heart failure in type 2 diabetes. However, a systematic evaluation of these models' performance, applicability, and clinical impact is absent. Methods and Results We searched Embase, MEDLINE, Web of Science, Google Scholar, and Tufts' clinical prediction registry through February 2021. Studies needed to report the development, validation, clinical impact, or update of a prediction model for hospitalization for heart failure in type 2 diabetes with measures of model performance and sufficient information for clinical use. Model assessment was done with the Prediction Model Risk of Bias Assessment Tool, and meta-analyses of model discrimination were performed. We included 15 model development and 3 external validation studies with data from 999 167 people with type 2 diabetes. Of the 15 models, 6 had undergone external validation and only 1 had low concern for risk of bias and applicability (Risk Equations for Complications of Type 2 Diabetes). Seven models were presented in a clinically useful manner (eg, risk score, online calculator) and 2 models were classified as the most suitable for clinical use based on study design, external validity, and point-of-care usability. These were Risk Equations for Complications of Type 2 Diabetes (meta-analyzed c-statistic, 0.76) and the Thrombolysis in Myocardial Infarction Risk Score for Heart Failure in Diabetes (meta-analyzed c-statistic, 0.78), which was the simplest model with only 5 variables. No studies reported clinical impact. Conclusions Most prediction models for hospitalization for heart failure in patients with type 2 diabetes have potential concerns with risk of bias or applicability, and uncertain external validity and clinical impact. Future research is needed to address these knowledge gaps.

Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts

INTRODUCTION

As novel therapies for chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) become available, their long-term benefits should be evaluated using CKD progression models. Existing models offer different modeling approaches that could be reused, but it may be challenging for modelers to assess commonalities and differences between the many available models. Additionally, the data and underlying population characteristics informing model parameters may not always be evident. Therefore, this study reviewed and summarized existing modeling approaches and data sources for CKD in T2DM, as a reference for future model development.

METHODS

This systematic literature review included computer simulation models of CKD in T2DM populations. Searches were implemented in PubMed (including MEDLINE), Embase, and the Cochrane Library, up to October 2021. Models were classified as cohort state-transition models (cSTM) or individual patient simulation (IPS) models. Information was extracted on modeled kidney disease states, risk equations for CKD, data sources, and baseline characteristics of derivation cohorts in primary data sources.

RESULTS

The review identified 49 models (21 IPS, 28 cSTM). A five-state structure was standard among state-transition models, comprising one kidney disease-free state, three kidney disease states [frequently including albuminuria and end-stage kidney disease (ESKD)], and one death state. Five models captured CKD regression and three included cardiovascular disease (CVD). Risk equations most commonly predicted albuminuria and ESKD incidence, while the most predicted CKD sequelae were mortality and CVD. Most data sources were well-established registries, cohort studies, and clinical trials often initiated decades ago in predominantly White populations in high-income countries. Some recent models were developed from country-specific data, particularly for Asian countries, or from clinical outcomes trials.

CONCLUSION

Modeling CKD in T2DM is an active research area, with a trend towards IPS models developed from non-Western data and single data sources, primarily recent outcomes trials of novel renoprotective treatments.

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.

Development and validation of a risk prediction model for chronic kidney disease among individuals with type 2 diabetes

Many studies had established the chronic kidney disease (CKD) prediction models, but most of them were conducted on the general population and not on patients with type 2 diabetes, especially in Asian populations. This study aimed to develop a risk prediction model for CKD in patients with type 2 diabetes from the Diabetes Care Management Program (DCMP) in Taiwan. This research was a retrospective cohort study. We used the DCMP database to set up a cohort of 4,601 patients with type 2 diabetes without CKD aged 40–92 years enrolled in the DCMP program of a Taichung medical center in 2002–2016. All patients were followed up until incidences of CKD, death, and loss to follow-up or 2016. The dataset for participants of national DCMP in 2002–2004 was used as external validation. The incident CKD cases were defined as having one of the following three conditions: ACR data greater than or equal to 300 (mg/g); both eGFR data less than 60 (ml/min/1.73 m²) and ACR data greater than or equal to 30 (mg/g); and eGFR data less than 45 (ml/min/1.73 m²). The study subjects were randomly allocated to derivation and validation sets at a 2:1 ratio. Cox proportional hazards regression model was used to identify the risk factors of CKD in the derivation set. Time-varying area under receiver operating characteristics curve (AUC) was used to evaluate the performance of the risk model. After an average of 3.8 years of follow-up period, 3,067 study subjects were included in the derivation set, and 786 (25.63%) were newly diagnosed CKD cases. A total of 1,534 participants were designated to the validation set, and 378 (24.64%) were newly diagnosed CKD cases. The final CKD risk factors consisted of age, duration of diabetes, insulin use, estimated glomerular filtration rate, albumin-to-creatinine ratio, high-density lipoprotein cholesterol, triglyceride, diabetes retinopathy, variation in HbA1c, variation in FPG, and hypertension drug use. The AUC values of 1-, 3-, and 5-year CKD risks were 0.74, 0.76, and 0.77 in the validation set, respectively, and were 0.76, 0.77, and 0.76 in the sample for external validation, respectively. The value of Harrell’s c-statistics was 0.76 (0.74, 0.78). The proposed model is the first CKD risk prediction model for type 2 diabetes patients in Taiwan. The 1-, 3-, and 5-year CKD risk prediction models showed good prediction accuracy. The model can be used as a guide for clinicians to develop medical plans for future CKD preventive intervention in Chinese patients with type 2 diabetes.

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.

Predicting Real-world Hypoglycemia Risk in American Adults With Type 1 or 2 Diabetes Mellitus Prescribed Insulin and/or Secretagogues: Protocol for a Prospective, 12-Wave Internet-Based Panel Survey With Email Support (the iNPHORM [Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-world Models] Study)

BACKGROUND

Hypoglycemia prognostic models contingent on prospective, self-reported survey data offer a powerful avenue for determining real-world event susceptibility and interventional targets.

OBJECTIVE

This protocol describes the design and implementation of the 1-year iNPHORM (Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-world Models) study, which aims to measure real-world self-reported severe and nonsevere hypoglycemia incidence (daytime and nocturnal) in American adults with type 1 or 2 diabetes mellitus prescribed insulin and/or secretagogues, and develop and internally validate prognostic models for severe, nonsevere daytime, and nonsevere nocturnal hypoglycemia. As a secondary objective, iNPHORM aims to quantify the effects of different antihyperglycemics on hypoglycemia rates.

METHODS

iNPHORM is a prospective, 12-wave internet-based panel survey that was conducted across the United States. Americans (aged 18-90 years) with self-reported type 1 or 2 diabetes mellitus prescribed insulin and/or secretagogues were conveniently sampled via the web from a pre-existing, closed, probability-based internet panel (sample frame). A sample size of 521 baseline responders was calculated for this study. Prospective data on hypoglycemia and potential prognostic factors were self-assessed across 14 closed, fully automated questionnaires (screening, baseline, and 12 monthly follow-ups) that were piloted using semistructured interviews (n=3) before fielding; no face-to-face contact was required as part of the data collection. Participant responses will be analyzed using multivariable count regression and machine learning techniques to develop and internally validate prognostic models for 1-year severe and 30-day nonsevere daytime and nocturnal hypoglycemia. The causal effects of different antihyperglycemics on hypoglycemia rates will also be investigated.

RESULTS

Recruitment and data collection occurred between February 2020 and March 2021 (ethics approval was obtained on December 17, 2019). A total of 1694 participants completed the baseline questionnaire, of whom 1206 (71.19%) were followed up for 12 months. Most follow-up waves (10,470/14,472, 72.35%) were completed, translating to a participation rate of 179% relative to our target sample size. Over 70.98% (856/1206) completed wave 12. Analyses of sample characteristics, quality metrics, and hypoglycemia incidence and prognostication are currently underway with published results anticipated by fall 2022.

CONCLUSIONS

iNPHORM is the first hypoglycemia prognostic study in the United States to leverage prospective, longitudinal self-reports. The results will contribute to improved real-world hypoglycemia risk estimation and potentially safer, more effective clinical diabetes management.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04219514; https://clinicaltrials.gov/ct2/show/NCT04219514.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/33726.

DCRM Multispecialty Practice Recommendations for the management of diabetes, cardiorenal, and metabolic diseases

Type 2 diabetes (T2D), chronic kidney disease (CKD), atherosclerotic cardiovascular disease (ASCVD), and heart failure (HF)-along with their associated risk factors-have overlapping etiologies, and two or more of these conditions frequently occur in the same patient. Many recent cardiovascular outcome trials (CVOTs) have demonstrated the benefits of agents originally developed to control T2D, ASCVD, or CKD risk factors, and these agents have transcended their primary indications to confer benefits across a range of conditions. This evolution in CVOT evidence calls for practice recommendations that are not constrained by a single discipline to help clinicians manage patients with complex conditions involving diabetes, cardiorenal, and/or metabolic (DCRM) diseases. The ultimate goal for these recommendations is to be comprehensive yet succinct and easy to follow by the nonexpert-whether a specialist or a primary care clinician. To meet this need, we formed a volunteer task force comprising leading cardiologists, nephrologists, endocrinologists, and primary care physicians to develop the DCRM Practice Recommendations, a multispecialty consensus on the comprehensive management of the patient with complicated metabolic disease. The task force recommendations are based on strong evidence and incorporate practical guidance that is clinically relevant and simple to implement, with the aim of improving outcomes in patients with DCRM. The recommendations are presented as 18 separate graphics covering lifestyle therapy, patient self-management education, technology for DCRM management, prediabetes, cognitive dysfunction, vaccinations, clinical tests, lipids, hypertension, anticoagulation and antiplatelet therapy, antihyperglycemic therapy, hypoglycemia, nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH), ASCVD, HF, CKD, and comorbid HF and CKD, as well as a graphical summary of medications used for DCRM.

Validating the UK prospective diabetes study outcome model 2 using data of 94,946 Israeli patients with type 2 diabetes

AIMS

To externally validate the United Kingdom Prospective Diabetes Study (UKPDS) Outcome Model 2 (OM2) in contemporary Israeli patient populations.

METHODS

De-identified patient data on demographics, time-varying risk factors, and clinical events of newly diagnosed type 2 diabetes patients were extracted from the Maccabi Healthcare Services (MHS) diabetes registry over years 2000-2013. Depending on the baseline risk, patients were categorized into low-risk and intermediate-risk groups. In addition to assessing discriminatory performance, the predicted and observed 15-year cumulative incidences of diabetes complications and death were compared among all patients and for the two risk-groups.

RESULTS

The discriminatory capability of OM2 was moderate to good, C-statistic ranging 0.71-0.95. The model overpredicted the risk for MI, blindness and death (Predicted/Observed events (P/O: 1.32-2.31)), and underpredicted the risk of IHD (P/O: 0.5). In patients with a low baseline risk, overpredictions were even more pronounced. OM2 performed well in predicting renal failure and ulcer risk in patients with a low risk but predicted well the risk of death, stroke, CHF, and amputation in patients with an intermediate risk.

CONCLUSION

OM2 demonstrated good to moderate discrimination capability for predicting diabetes complications and mortality risks in Israeli diabetes population. The prediction performance differed between patients with different baseline risks.

The PRIME Type 2 Diabetes Model: a novel, patient-level model for estimating long-term clinical and cost outcomes in patients with type 2 diabetes mellitus

BACKGROUND AND AIMS

The growing burden of diabetes mellitus and recent progress in understanding cardiovascular outcomes for type 2 diabetes (T2D) patients continue to make the disease a priority for healthcare decision-makers around the world. Our objective was to develop a new, product-independent model capable of projecting long-term clinical and cost outcomes for populations with T2D to support health economic evaluation.

METHODS

Following a systematic literature review to identify longitudinal study data, existing T2D models and risk formulae for T2D populations, a model was developed (the PRIME Type 2 Diabetes Model [PRIME T2D Model]) in line with good practice guidelines to simulate disease progression, diabetes-related complications and mortality. The model runs as a patient-level simulation and is capable of simulating treatment algorithms and risk factor progression, and projecting the cumulative incidence of macrovascular and microvascular complications as well as hypoglycemic events. The PRIME T2D Model can report clinical outcomes, quality-adjusted life expectancy, direct and indirect costs, along with standard measures of cost-effectiveness and is capable of probabilistic sensitivity analysis. Several approaches novel to T2D modeling were utilized, such as combining risk formulae using a weighted model averaging approach that takes into account patient characteristics to evaluate complication risk.

RESULTS

Validation analyses comparing modeled outcomes with published studies demonstrated that the PRIME T2D Model projects long-term patient outcomes consistent with those reported for a number of long-term studies, including cardiovascular outcomes trials. All root mean squared deviation (RMSD) values for internal validations (against published studies used to develop the model) were 1.1% or less and all external validation RMSDs were 3.7% or less.

CONCLUSIONS

The PRIME T2D Model is a product-independent analysis tool that is available online and offers new approaches to long-standing challenges in diabetes modeling and may become a useful tool for informing healthcare policy.HIGHLIGHTSThe PRIME Type 2 Diabetes (T2D) Model is a new, product-independent simulation model.The model offers new approaches to long-standing challenges in diabetes modeling.PRIME T2D Model projects outcomes consistent with those from clinical trials.The model is designed to be a useful tool for informing healthcare policy in T2D.

Development and validation of an equation to predict the incidence of coronary heart disease in patients with type 2 diabetes in Japan

Objective

In the diabetes treatment policy after the Kumamoto Declaration 2013, it is difficult to accurately predict the incidence of complications in patients using the JJ risk engine. This study was conducted to develop a prediction equation suitable for the current diabetes treatment policy using patient data from Kitasato University Kitasato Institute Hospital (Hospital A) and to externally validate the developed equation using patient data from Kitasato University Hospital (Hospital B). Outlier tests were performed on the patient data from Hospital A to exclude the outliers. Prediction equation was developed using the patient data excluding the outliers and was subjected to external validation.

Results

By excluding outlier data, we could develop a new prediction equation for the incidence of coronary heart disease (CHD) as a complication of type 2 diabetes, incorporating the use of antidiabetic drugs with a high risk of hypoglycemia. This is the first prediction equation in Japan that incorporates the use of antidiabetic drugs. We believe that it will be useful in preventive medicine for treatment for people at high risk of CHD as a complication of diabetes or other diseases. In the future, we would like to confirm the accuracy of this equation at other facilities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-021-05844-w.

Lifetime Cost-effectiveness of Oral Semaglutide Versus Dulaglutide and Liraglutide in Patients With Type 2 Diabetes Inadequately Controlled With Oral Antidiabetics

PURPOSE

To estimate the incremental cost-utility ratio of oral semaglutide (14 mg once daily) vs other glucagon-like peptide 1 receptor agonist treatments among adults with type 2 diabetes that was inadequately controlled with 1 to 2 oral antidiabetic drugs from a US payer perspective.

METHODS

A state-transition model with a competing risk approach was developed for diabetic complications and risk of cardiovascular events based on the UK Prospective Diabetes Study Outcomes Model 1 equations. Baseline population characteristics reflect the PIONEER 4 trial (Efficacy and Safety of Oral Semaglutide Versus Liraglutide and Versus Placebo in Subjects With Type 2 Diabetes Mellitus) of oral semaglutide. Model comparators included subcutaneous semaglutide, dulaglutide, and liraglutide. Treatment effects (change in glycosylated hemoglobin, weight, and systolic blood pressure) were estimated by network meta-analysis. Drug, management, and event costs (in 2019 US dollars), survival after nonfatal events, and utilities were obtained from the literature. Costs and quality-adjusted life-year (QALY) outcomes were discounted at 3% annually over a lifetime horizon. Probabilistic and 1-way sensitivity analyses were performed.

FINDINGS

Total estimated costs and QALYs were $144,065 and 12.98 for oral semaglutide, $145,721 and 12.96 for dulaglutide, $145,833 and 12.99 for SC semaglutide, and $149,428 and 12.97 for liraglutide, respectively. Oral semaglutide was less costly and more effective than dulaglutide and liraglutide but less costly than subcutaneous semaglutide with similar effectiveness. Oral semaglutide was favored versus subcutaneous semaglutide in 52.10% of model replications at a willingness-to-pay of $150,000 per QALY.

IMPLICATIONS

Oral semaglutide is predicted to offer health benefits similar to subcutaneous semaglutide and ahead of dulaglutide and liraglutide. Oral semaglutide is a cost-effective glucagon-like peptide 1 receptor agonist treatment option.

Data Analysis of the Risks of Type 2 Diabetes Mellitus Complications before Death Using a Data-Driven Modelling Approach: Methodologies and Challenges in Prolonged Diseases

(1) Background: A disease prediction model derived from real-world data is an important tool for managing type 2 diabetes mellitus (T2D). However, an appropriate prediction model for the Asian T2D population has not yet been developed. Hence, this study described construction details of the T2D Holistic Care model via estimating the probability of diabetes-related complications and the time-to-occurrence from a population-based database. (2) Methods: The model was based on the database of a Taiwan pay-for-performance reimbursement scheme for T2D between November 2002 and July 2017. A nonhomogeneous Markov model was applied to simulate multistate (7 main complications and death) transition probability after considering the sequential and repeated difficulties. (3) Results: The Markov model was constructed based on clinical care information from 163,452 patients with T2D, with a mean follow-up time of 5.5 years. After simulating a cohort of 100,000 hypothetical patients over a 10-year time horizon based on selected patient characteristics at baseline, a good predicted complication and mortality rates with a small range of absolute error (0.3–3.2%) were validated in the original cohort. Better and optimal predictabilities were further confirmed compared to the UKPDS Outcomes model and applied the model to other Asian populations, respectively. (4) Contribution: The study provides well-elucidated evidence to apply real-world data to the estimation of the occurrence and time point of major diabetes-related complications over a patient’s lifetime. Further applications in health decision science are encouraged.

Health State Utility Values for Type 2 Diabetes and Related Complications in East and Southeast Asia: A Systematic Review and Meta-Analysis

OBJECTIVES

East and Southeast Asia has the greatest burden of diabetes in the world. We sought to derive a reference set of utility values for type 2 diabetes without complication and disutility (utility decrement) values for important diabetes-related complications to better inform economic evaluation.

METHODS

A systematic review to identify utility values for diabetes and related complications reported in East and Southeast Asia. We searched MEDLINE (OVID) from inception to May 26, 2020 for utility values elicited using direct and indirect methods. Identified studies were assessed for quality based on the National Institute of Health and Care Excellence guidelines. Utility and disutility estimates were pooled by meta-analyses with subgroup analyses to evaluate differences by nationality and valuation instrument. (PROSPERO: CRD42020191075).

RESULTS

We identified 17 studies for the systematic review from a total of 13 035 studies in the initial search, of which 13 studies met the quality criteria for inclusion in the meta-analyses. The pooled utility value for diabetes without complication was 0.88 (95% CI 0.83-0.93), with the pooled utility decrement for associated complications ranged from 0.00 (for excess BMI) to 0.18 (for amputation). The utility values were consistently more conservative than previous estimates derived in Western populations. Utility decrements were comparable for SF-6D and EQ-5D valuation instruments and for Chinese and other Asian groups.

CONCLUSIONS

A reference set of pooled disutility and utility values for type 2 diabetes and its complications in East and Southeast Asian populations yielded more conservative estimates than Western populations.

Development and validation of optimal phenomapping methods to estimate long-term atherosclerotic cardiovascular disease risk in patients with type 2 diabetes

AIMS/HYPOTHESIS

Type 2 diabetes is a heterogeneous disease process with variable trajectories of CVD risk. We aimed to evaluate four phenomapping strategies and their ability to stratify CVD risk in individuals with type 2 diabetes and to identify subgroups who may benefit from specific therapies.

METHODS

Participants with type 2 diabetes and free of baseline CVD in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial were included in this study (N = 6466). Clustering using Gaussian mixture models, latent class analysis, finite mixture models (FMMs) and principal component analysis was compared. Clustering variables included demographics, medical and social history, laboratory values and diabetes complications. The interaction between the phenogroup and intensive glycaemic, combination lipid and intensive BP therapy for the risk of the primary outcome (composite of fatal myocardial infarction, non-fatal myocardial infarction or unstable angina) was evaluated using adjusted Cox models. The phenomapping strategies were independently assessed in an external validation cohort (Look Action for Health in Diabetes [Look AHEAD] trial: n = 4211; and Bypass Angioplasty Revascularisation Investigation 2 Diabetes [BARI 2D] trial: n = 1495).

RESULTS

Over 9.1 years of follow-up, 789 (12.2%) participants had a primary outcome event. FMM phenomapping with three phenogroups was the best-performing clustering strategy in both the derivation and validation cohorts as determined by Bayesian information criterion, Dunn index and improvement in model discrimination. Phenogroup 1 (n = 663, 10.3%) had the highest burden of comorbidities and diabetes complications, phenogroup 2 (n = 2388, 36.9%) had an intermediate comorbidity burden and lowest diabetes complications, and phenogroup 3 (n = 3415, 52.8%) had the fewest comorbidities and intermediate burden of diabetes complications. Significant interactions were observed between phenogroups and treatment interventions including intensive glycaemic control (p-interaction = 0.042) and combination lipid therapy (p-interaction < 0.001) in the ACCORD, intensive lifestyle intervention (p-interaction = 0.002) in the Look AHEAD and early coronary revascularisation (p-interaction = 0.003) in the BARI 2D trial cohorts for the risk of the primary composite outcome. Favourable reduction in the risk of the primary composite outcome with these interventions was noted in low-risk participants of phenogroup 3 but not in other phenogroups. Compared with phenogroup 3, phenogroup 1 participants were more likely to have severe/symptomatic hypoglycaemic events and medication non-adherence on follow-up in the ACCORD and Look AHEAD trial cohorts.

CONCLUSIONS/INTERPRETATION

Clustering using FMMs was the optimal phenomapping strategy to identify replicable subgroups of patients with type 2 diabetes with distinct clinical characteristics, CVD risk and response to therapies.

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

INTRODUCTION

Patients with diabetes mellitus are risk of premature death. In this study, we developed a machine learning-driven predictive risk model for all-cause mortality among patients with type 2 diabetes mellitus using multiparametric approach with data from different domains.

RESEARCH DESIGN AND METHODS

This study used territory-wide data of patients with type 2 diabetes attending public hospitals or their associated ambulatory/outpatient facilities in Hong Kong between January 1, 2009 and December 31, 2009. The primary outcome is all-cause mortality. The association of risk variables and all-cause mortality was assessed using Cox proportional hazards models. Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method.

RESULTS

A total of 273 678 patients (mean age: 65.4±12.7 years, male: 48.2%, median follow-up: 142 (IQR=106-142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate: 3.4%/year; 2.7 million patient-years). Multivariate Cox regression found the following significant predictors of all-cause mortality: age, male gender, baseline comorbidities, anemia, mean values of neutrophil-to-lymphocyte ratio, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, HbA1c and fasting blood glucose (FBG), measures of variability of both HbA1c and FBG. The above parameters were incorporated into a score-based predictive risk model that had a c-statistic of 0.73 (95% CI 0.66 to 0.77), which was improved to 0.86 (0.81 to 0.90) and 0.87 (0.84 to 0.91) using random survival forests and deep survival learning models, respectively.

CONCLUSIONS

A multiparametric model incorporating variables from different domains predicted all-cause mortality accurately in type 2 diabetes mellitus. The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions.

Development and validation of the CHIME simulation model to assess lifetime health outcomes of prediabetes and type 2 diabetes in Chinese populations: A modeling study

BACKGROUND

Existing predictive outcomes models for type 2 diabetes developed and validated in historical European populations may not be applicable for East Asian populations due to differences in the epidemiology and complications. Despite the continuum of risk across the spectrum of risk factor values, existing models are typically limited to diabetes alone and ignore the progression from prediabetes to diabetes. The objective of this study is to develop and externally validate a patient-level simulation model for prediabetes and type 2 diabetes in the East Asian population for predicting lifetime health outcomes.

METHODS AND FINDINGS

We developed a health outcomes model from a population-based cohort of individuals with prediabetes or type 2 diabetes: Hong Kong Clinical Management System (CMS, 97,628 participants) from 2006 to 2017. The Chinese Hong Kong Integrated Modeling and Evaluation (CHIME) simulation model comprises of 13 risk equations to predict mortality, micro- and macrovascular complications, and development of diabetes. Risk equations were derived using parametric proportional hazard models. External validation of the CHIME model was assessed in the China Health and Retirement Longitudinal Study (CHARLS, 4,567 participants) from 2011 to 2018 for mortality, ischemic heart disease, cerebrovascular disease, renal failure, cataract, and development of diabetes; and against 80 observed endpoints from 9 published trials using 100,000 simulated individuals per trial. The CHIME model was compared to United Kingdom Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2) and Risk Equations for Complications Of type 2 Diabetes (RECODe) by assessing model discrimination (C-statistics), calibration slope/intercept, root mean square percentage error (RMSPE), and R2. CHIME risk equations had C-statistics for discrimination from 0.636 to 0.813 internally and 0.702 to 0.770 externally for diabetes participants. Calibration slopes between deciles of expected and observed risk in CMS ranged from 0.680 to 1.333 for mortality, myocardial infarction, ischemic heart disease, retinopathy, neuropathy, ulcer of the skin, cataract, renal failure, and heart failure; 0.591 for peripheral vascular disease; 1.599 for cerebrovascular disease; and 2.247 for amputation; and in CHARLS outcomes from 0.709 to 1.035. CHIME had better discrimination and calibration than UKPDS-OM2 in CMS (C-statistics 0.548 to 0.772, slopes 0.130 to 3.846) and CHARLS (C-statistics 0.514 to 0.750, slopes -0.589 to 11.411); and small improvements in discrimination and better calibration than RECODe in CMS (C-statistics 0.615 to 0.793, slopes 0.138 to 1.514). Predictive error was smaller for CHIME in CMS (RSMPE 3.53% versus 10.82% for UKPDS-OM2 and 11.16% for RECODe) and CHARLS (RSMPE 4.49% versus 14.80% for UKPDS-OM2). Calibration performance of CHIME was generally better for trials with Asian participants (RMSPE 0.48% to 3.66%) than for non-Asian trials (RMPSE 0.81% to 8.50%). Main limitations include the limited number of outcomes recorded in the CHARLS cohort, and the generalizability of simulated cohorts derived from trial participants.

CONCLUSIONS

Our study shows that the CHIME model is a new validated tool for predicting progression of diabetes and its outcomes, particularly among Chinese and East Asian populations that has been lacking thus far. The CHIME model can be used by health service planners and policy makers to develop population-level strategies, for example, setting HbA1c and lipid targets, to optimize health outcomes.

Interdisciplinary assessment and diagnostic algorithm: The role of the diabetologist

In recent years, many studies have revealed the importance of heart failure (HF) development in type 2 diabetes (T2D), which increases the morbidity and mortality during the course of diabetes. In this context, it became important to emphasize the role of both cardiologists and diabetologists in the early diagnosis and further adequate treatment of HF in T2D. While HF appears in two major forms, with reduced or preserved ejection fraction (EF), namely HFrEF and HFpEF, it became important to define the optimal approach to the diagnostics. Regarding HFrEF, the role of cardiological methods remained dominant, while the complexity of early diagnosis requires nowadays more active participation of diabetologists. The absence of abundant symptoms and echocardiographic findings imposed the need for the use of risk markers based on metabolic variables and low-grade inflammation parameters. Following that unmet need, numerous studies have defined the possible relationship between metabolic variables in diabetes and the risk for HF. Moreover, attempts have been made to integrate biochemical and clinical parameters into risk score engines and some of them gave promising results. However, the follow-up studies in T2D subjects are needed to determine the clinical relevance of these new approaches.

Predictive models for cardiovascular and kidney outcomes in patients with type 2 diabetes: systematic review and meta-analyses

OBJECTIVE

To inform a clinical practice guideline (BMJ Rapid Recommendations) considering sodium glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists for treatment of adults with type 2 diabetes, we summarised the available evidence regarding the performance of validated risk models on cardiovascular and kidney outcomes in these patients.

METHODS

We systematically searched bibliographic databases in January 2020 to identify observational studies evaluating risk models for all-cause and cardiovascular mortality, heart failure (HF) hospitalisations, end-stage kidney disease (ESKD), myocardial infarction (MI) and ischaemic stroke in ambulatory adults with type 2 diabetes. Using a random effects model, we pooled discrimination measures for each model and outcome, separately, and descriptively summarised calibration plots, when available. We used the Prediction Model Risk of Bias Assessment Tool to assess risk of bias of each included study and the Grading of Recommendations, Assessment, Development, and Evaluation approach to evaluate our certainty in the evidence.

RESULTS

Of 22 589 publications identified, 15 observational studies reporting on seven risk models proved eligible. Among the seven models with >1 validation cohort, the Risk Equations for Complications of Type 2 Diabetes (RECODe) had the best calibration in primary studies and the highest pooled discrimination measures for the following outcomes: all-cause mortality (C-statistics 0.75, 95% CI 0.70 to 0.80; high certainty), cardiovascular mortality (0.79, 95% CI 0.75 to 0.84; low certainty), ESKD (0.73, 95% CI 0.52 to 0.94; low certainty), MI (0.72, 95% CI 0.69 to 0.74; moderate certainty) and stroke (0.71, 95% CI 0.68 to 0.74; moderate certainty). This model does not, however, predict risk of HF hospitalisations.

CONCLUSION

Of available risk models, RECODe proved to have satisfactory calibration in primary validation studies and acceptable discrimination superior to other models, though with high risk of bias in most primary studies.

TRIAL REGISTRATION NUMBER

CRD42020168351.

Macrovascular Risk Equations Based on the CANVAS Program

BACKGROUND

Widely used risk equations for cardiovascular outcomes for individuals with type 2 diabetes mellitus (T2DM) have been incapable of predicting cardioprotective effects observed in recent cardiovascular outcomes trials (CVOTs) involving individuals with T2DM at high risk for or with established cardiovascular disease (CVD).

OBJECTIVE

We developed cardiovascular and mortality risk equations using patient-level data from the CANVAS (CANagliflozin cardioVascular Assessment Study) Program to address this shortcoming.

METHODS

Data from 10,142 patients with T2DM at high risk for or with established CVD, randomized to canagliflozin + standard of care (SoC) or SoC alone and followed for a mean duration of 3.6 years in the CANVAS Program were used to derive parametric risk equations for myocardial infarction (MI), stroke, hospitalization for heart failure (HHF), and death. Accumulated knowledge from the widely used UKPDS-OM2 (United Kingdom Prospective Diabetes Study Outcomes Model 2) was leveraged, and any departures in parameterization were limited to those necessary to provide adequate goodness of fit. Candidate explanatory covariates were selected using only the placebo arm to minimize confounding effects. Internal validation was performed separately by study treatment arm.

RESULTS

UKPDS-OM2 predicted CANVAS Program outcomes poorly. Recalibrating UKPDS-OM2 intercepts improved calibration in some cases. Refitting the coefficients but otherwise preserving the UKPDS-OM2 structure improved the fit substantially, which was sufficient for stroke and death. For MI, reselecting UKPDS-OM2 covariates and functional form proved sufficient. For HHF, selection from a broad set of candidate covariates and inclusion of a canagliflozin indicator was required.

CONCLUSION

These risk equations address some of the limitations of widely used risk equations, such as the UKPDS-OM2, for modeling cardioprotective treatments for individuals with T2DM and high cardiovascular risk, including derivation from overly healthy patients treated with agents that lack cardioprotection and have been described as reflecting a different therapeutic era. Future work is needed to examine external validity.

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.

Development and Internal Validation of a Discrete Event Simulation Model of Diabetic Kidney Disease Using CREDENCE Trial Data

INTRODUCTION

The Canagliflozin and Renal Endpoints in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE) study showed that compared with placebo, canagliflozin 100 mg significantly reduced the risk of major cardiovascular events and adverse renal outcomes in patients with diabetic kidney disease (DKD). We developed a simulation model that can be used to estimate the long-term health and economic consequences of DKD treatment interventions for patients matching the CREDENCE study population.

METHODS

The CREDENCE Economic Model of DKD (CREDEM-DKD) was developed using patient-level data from CREDENCE (which recruited patients with estimated glomerular filtration rate 30 to < 90 mL/min/1.73 m2, urinary albumin to creatinine ratio > 300-5000 mg/g, and taking the maximum tolerated dose of a renin-angiotensin-aldosterone system inhibitor). Risk prediction equations were fit for start of maintenance dialysis, doubling of serum creatinine, hospitalization for heart failure, nonfatal myocardial infarction, nonfatal stroke, and all-cause mortality. A micro-simulation model was constructed using these risk equations combined with user-definable kidney transplant event risks. Internal validation was performed by loading the model to replicate the CREDENCE study and comparing predictions with trial Kaplan-Meier estimate curves. External validation was performed by loading the model to replicate a subgroup of the CANagliflozin cardioVascular Assessment Study (CANVAS) Program with patient characteristics that would have qualified for inclusion in CREDENCE.

RESULTS

Risk prediction equations generally fit well and exhibited good concordance, especially for the placebo arm. In the canagliflozin arm, modest underprediction was observed for myocardial infarction, along with overprediction of dialysis, doubling of serum creatinine, and all-cause mortality. Discrimination was strong (0.85) for the renal outcomes, but weaker for the macrovascular outcomes and all-cause mortality (0.60-0.68). The model performed well in internal and external validation exercises.

CONCLUSION

CREDEM-DKD is an important new tool in the evaluation of treatment interventions in the DKD population.

TRIAL REGISTRATION

ClinicalTrials.gov identifier, NCT02065791.

Managing the multifaceted nature of type 2 diabetes using once-weekly injectable GLP-1 receptor agonist therapy

WHAT IS KNOWN AND OBJECTIVE

As a highly prevalent chronic condition associated with complications and high mortality rates, it is important for pharmacists to have a comprehensive understanding of the impact of type 2 diabetes (T2D) and available treatment options. The use of injectable glucagon-like peptide 1 receptor agonists (GLP-1 RAs) is recommended as an effective and convenient treatment regimen for improving glycaemic control in individuals with T2D, with a good safety profile; however, the wider extent of its potential benefits often are unknown to clinical pharmacists. The objective of this article is to provide an overview of the impact of T2D on individuals and to discuss the multifaceted role of once-weekly (QW) GLP-1 RAs in addressing these challenges.

METHODS

This is a narrative review of the published literature regarding the use of injectable GLP-1 RAs in managing health complications in people with T2D.

RESULTS AND DISCUSSION

Recent findings reveal additional benefits of GLP-1 RAs in managing T2D complications, including atherosclerotic cardiovascular (CV) disease, retinopathy, neuropathy, and nephropathy. Dulaglutide and semaglutide have been shown to provide additional CV benefit in patients at high risk of CV events compared with standard of care/placebo and may offer renal protection in patients with chronic kidney disease. Cost-effectiveness studies, taking into consideration these different complications, have shown that QW GLP-1 RAs were cost-effective compared with other therapies. GLP-1 RAs may also help to improve overall health-related quality of life, reducing the risk of depression and 'diabetes distress', and limiting the risk of hypoglycaemia.

WHAT IS NEW AND CONCLUSION

From the literature, this appears to be the first review of the evidence supporting the multifaceted role of QW GLP-1 RAs in T2D, with particular emphasis on their use in comorbid conditions, as well as associated potential financial and well-being benefits. The results suggest that QW GLP-1 RAs may be an attractive treatment option for improving glycaemic control in T2D, especially in individuals with (or at risk of) additional comorbidities or health complications.

Does the Encounter Type Matter When Defining Diabetes Complications in Electronic Health Records?

BACKGROUND

Electronic health records (EHRs) and claims records are widely used in defining type 2 diabetes mellitus (T2DM) complications across different types of health care encounters.

OBJECTIVE

This study investigates whether using different EHR encounter types to define diabetes complications may lead to different results when examining associations between diabetes complications and their risk factors in patients with T2DM.

RESEARCH DESIGN

The study cohort of 64,855 adult patients with T2DM was created from EHR data from the Research Action for Health Network (REACHnet), using the Surveillance Prevention, and Management of Diabetes Mellitus (SUPREME-DM) definitions. Incidence of coronary heart disease (CHD) and stroke events were identified using International Classification of Diseases (ICD)-9/10 codes and grouped by encounter types: (1) inpatient (IP) or emergency department (ED) type, or (2) any health care encounter type. Cox proportional hazards regression was used to estimate associations between diabetes complications (ie, CHD and stroke) and risk factors (ie, low-density lipoprotein cholesterol and hemoglobin A1c).

RESULTS

The incidence rates of CHD and stroke in all health care settings were more than twice the incidence rates of CHD and stroke in IP/ED settings. The age-adjusted and multivariable-adjusted hazard ratios for incident CHD and stroke across different levels of low-density lipoprotein cholesterol and hemoglobin A1c were similar between IP/ED and all settings.

CONCLUSION

While there are large variations in incidence rates of CHD and stroke as absolute risks, the associations between both CHD and stroke and their respective risk factors measured by hazard ratios as relative risks are similar, regardless of alternative definitions.

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.

Using the BRAVO Risk Engine to Predict Cardiovascular Outcomes in Clinical Trials With Sodium-Glucose Transporter 2 Inhibitors

OBJECTIVE

This study evaluated the ability of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine to accurately project cardiovascular outcomes in three major clinical trials-BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME), Canagliflozin Cardiovascular Assessment Study (CANVAS), and Dapagliflozin Effect on Cardiovascular Events-Thrombolysis in Myocardial Infarction (DECLARE-TIMI 58) trial-on sodium-glucose cotransporter 2 inhibitors (SGLT2is) to treat patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Baseline data from the publications of the three trials were obtained and entered into the BRAVO model to predict cardiovascular outcomes. Projected benefits of reducing risk factors of interest (A1C, systolic blood pressure [SBP], LDL, or BMI) on cardiovascular events were evaluated, and simulated outcomes were compared with those observed in each trial.

RESULTS

BRAVO achieved the best prediction accuracy when simulating outcomes of the CANVAS and DECLARE-TIMI 58 trials. For EMPA-REG OUTCOME, a mild bias was observed (∼20%) in the prediction of mortality and angina. The effect of risk reduction on outcomes in treatment versus placebo groups predicted by the BRAVO model strongly correlated with the observed effect of risk reduction on the trial outcomes as published. Finally, the BRAVO engine revealed that most of the clinical benefits associated with SGLT2i treatment are through A1C control, although reductions in SBP and BMI explain a proportion of the observed decline in cardiovascular events.

CONCLUSIONS

The BRAVO risk engine was effective in predicting the benefits of SGLT2is on cardiovascular health through improvements in commonly measured risk factors, including A1C, SBP, and BMI. Since these benefits are individually small, the use of the complex, dynamic BRAVO model is ideal to explain the cardiovascular outcome trial results.

Risk stratification tools for heart failure in the diabetes clinic

The advent of Sodium Glucose Transporter 2-inhibitors (SGLT2-i) in recent years gave endocrinologists the opportunity to actively treat and prevent heart failure (HF) in patients with type 2 diabetes (T2DM). While the relationship between T2DM and HF has been extensively reviewed, previous works focused mostly on epidemiology, pathophysiology and treatment of HF in T2DM. The aim of our work was to aid health care professionals in identifying individuals at high risk for this dreadful complication. Recent guidelines recommend to use drugs with proven cardiovascular benefits (Glucagon-like peptide-1 receptor agonists (GLP1-RA) and SGLT2-i) in patients with previous cardiovascular disease (CVD) and to prefer SGLT2-i in patients with known HF. In everyday clinical practice, the choice between these two drug classes in patients without known HF or atherosclerotic CVD is mostly arbitrary and based on the side effect profile. Recently, risk stratification tools to estimate HF incidence have been developed in order to guide treatment with a view to bring precision medicine into diabetes care. With this purpose, we provide a review of the tools able to predict HF incidence for patients in primary CVD prevention as well as risk of future hospitalizations for patients with known HF.

Durability of glycaemic control in patients with type 2 diabetes after metformin failure: Prognostic model derivation and validation using the DISCOVER study

AIM

To develop and internally validate prognostic models on the long-term durability of glycaemic control in patients with type 2 diabetes after metformin failure.

MATERIALS AND METHODS

DISCOVER is a 3-year, prospective observational study across six continents investigating second-line glucose-lowering therapies. In this analysis from 35 countries, we included patients on metformin initiating second-line glucose-lowering medication(s) because of physician-defined lack of efficacy. The outcome was durability of glycaemic control, defined as three consecutive levels of HbA1c at 6-, 12- and 24-month follow-up at target (HbA1c equal to or lower than the level when the physician initiated the second-line therapy in patients with baseline HbA1c ≤7% [53 mmol/mol]; and equal to or lower than 7% in those with baseline HbA1c >7%). We developed and internally validated two prognostic models: a base model, which included age, sex, ethnicity, country income group, baseline HbA1c and second-line therapy, and an advanced model, established through statistical variable selections from a model including base variables and 13 additional predictors selected from a literature review. We used logistic regression to develop and 500 bootstrapping samples to internally validate the models; discrimination and calibration were used to assess model performance.

RESULTS

Overall, 896 out of 2995 participants (29.9%) had sustained glycaemic control. The base model performed well: Nagelkerke R² was 0.13, C-index 0.70 (95% CI: 0.68, 0.71) and bias-corrected C-index 0.69 after internal validation. Diabetes duration, insurance type, estimated glomerular filtration rate and glucose self-monitoring were additionally selected in the advanced model, which had only a slightly better performance compared with the base model: Nagelkerke R² 0.20, C-index 0.71 (95% CI: 0.69, 0.73) and bias-corrected C-index 0.70. Calibration plots showed good calibrations of both validated models.

CONCLUSION

These prognostic models, which include simple demographic and routinely collected clinical information, enabled the estimation of the probability of 2-year sustained glycaemic control in patients after metformin failure. The models have been implemented into a web-based tool to support healthcare professionals in their decisions.