Validation of the archimedes diabetes model

Challenges and opportunities for digital twins in precision medicine from a complex systems perspective

Digital twins (DTs) in precision medicine are increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. We argue that including mechanistic simulations that produce behavior based on explicitly defined biological hypotheses and multiscale mechanisms is beneficial. It enables the exploration of diverse therapeutic strategies and supports dynamic clinical decision-making through insights from network science, quantitative biology, and digital medicine.

Adverse drug events in cost-effectiveness models of pharmacological interventions for diabetes, diabetic retinopathy, and diabetic macular edema: a scoping review

OBJECTIVE

The objective of this review was to examine the role of adverse drug events (ADEs) caused by pharmacological interventions in cost-effectiveness models for diabetes mellitus, diabetic retinopathy, and diabetic macular edema.

INTRODUCTION

Guidelines for economic evaluation recognize the importance of including ADEs in the analysis, but in practice, consideration of ADEs in cost-effectiveness models seem to be vague. Inadequate inclusion of these harmful outcomes affects the reliability of the results, and the information provided by economic evaluation could be misleading. Reviewing whether and how ADEs are incorporated in cost-effectiveness models is necessary to understand the current practices of economic evaluation.

INCLUSION CRITERIA

Studies included were published between 2011-2022 in English, representing cost-effectiveness analyses using modeling framework for pharmacological interventions in the treatment of diabetes mellitus, diabetic retinopathy, or diabetic macular edema. Other types of analyses and other types of conditions were excluded.

METHODS

The databases searched included MEDLINE (PubMed), CINAHL (EBSCOhost), Scopus, Web of Science Core Collection, and NHS Economic Evaluation Database. Gray literature was searched via the National Institute for Health and Care Excellence, European Network for Health Technology Assessment, the National Institute for Health and Care Research, and the International Network of Agencies for Health Technology Assessment. The search was conducted on January 1, 2023. Titles and abstracts were screened for inclusion by 2 independent reviewers. Full-text review was conducted by 3 independent reviewers. A data extraction form was used to extract and analyze the data. Results were presented in tabular format with a narrative summary, and discussed in the context of existing literature and guidelines.

RESULTS

A total of 242 reports were extracted and analyzed in this scoping review. For the included analyses, type 2 diabetes was the most common disease (86%) followed by type 1 diabetes (10%), diabetic macular edema (9%), and diabetic retinopathy (0.4%). The majority of the included analyses used a health care payer perspective (88%) and had a time horizon of 30 years or more (75%). The most common model type was a simulation model (57%), followed by a Markov simulation model (18%). Of the included cost-effectiveness analyses, 26% included ADEs in the modeling, and 13% of the analyses excluded them. Most of the analyses (61%) partly considered ADEs; that is, only 1 or 2 ADEs were included. No difference in overall inclusion of ADEs between the different conditions existed, but the models for diabetic retinopathy and diabetic macular edema more often omitted the ADE-related impact on quality of life compared with the models for diabetes mellitus. Most analyses included ADEs in the models as probabilities (55%) or as a submodel (40%), and the most common source for ADE incidences were clinical trials (65%).

CONCLUSIONS

The inclusion of ADEs in cost-effectiveness models is suboptimal. The ADE-related costs were better captured than the ADE-related impact on quality of life, which was most pronounced in the models for diabetic retinopathy and diabetic macular edema. Future research should investigate the potential impact of ADEs on the results, and identify the criteria and policies for practical inclusion of ADEs in economic evaluation.

SUPPLEMENTAL DIGITAL CONTENT

A Finnish-language version of the abstract of this review is available: http://links.lww.com/SRX/A68 .

Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

Development and Validation of the US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) Model: Health Disparity and Economic Impact Model

BACKGROUND

Few simulation models have incorporated the interplay of diabetes, obesity, and cardiovascular disease (CVD); their upstream lifestyle and biological risk factors; and their downstream effects on health disparities and economic consequences.

METHODS

We developed and validated a US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) model that incorporates demographic, clinical, and lifestyle risk factors to jointly predict overall and racial-ethnic groups-specific obesity, diabetes, CVD, and cause-specific mortality for the US adult population aged 40 to 79 y at baseline. An individualized health care cost prediction model was further developed and integrated. This model incorporates nationally representative data on baseline demographics, lifestyle, health, and cause-specific mortality; dynamic changes in modifiable risk factors over time; and parameter uncertainty using probabilistic distributions. Validation analyses included assessment of 1) population-level risk calibration and 2) individual-level risk discrimination. To illustrate the application of the DOC-M model, we evaluated the long-term cost-effectiveness of a national produce prescription program.

RESULTS

Comparing the 15-y model-predicted population risk of primary outcomes among the 2001-2002 National Health and Nutrition Examination Survey (NHANES) cohort with the observed prevalence from age-matched cross-sectional 2003-2016 NHANES cohorts, calibration performance was strong based on observed-to-expected ratio and calibration plot analysis. In most cases, Brier scores fell below 0.0004, indicating a low overall prediction error. Using the Multi-Ethnic Study of Atherosclerosis cohorts, the c-statistics for assessing individual-level risk discrimination were 0.85 to 0.88 for diabetes, 0.93 to 0.95 for obesity, 0.74 to 0.76 for CVD history, and 0.78 to 0.81 for all-cause mortality, both overall and in three racial-ethnic groups. Open-source code for the model was posted at https://github.com/food-price/DOC-M-Model-Development-and-Validation.

CONCLUSIONS

The validated DOC-M model can be used to examine health, equity, and the economic impact of health policies and interventions on behavioral and clinical risk factors for obesity, diabetes, and CVD.

HIGHLIGHTS

We developed a novel microsimula'tion model for obesity, diabetes, and CVD, which intersect together and - critically for prevention and treatment interventions - share common lifestyle, biologic, and demographic risk factors.Validation analyses, including assessment of (1) population-level risk calibration and (2) individual-level risk discrimination, showed strong performance across the overall population and three major racial-ethnic groups for 6 outcomes (obesity, diabetes, CVD, and all-cause mortality, CVD- and DM-cause mortality)This paper provides a thorough explanation and documentation of the development and validation process of a novel microsimulation model, along with the open-source code (https://github.com/food-price/ DOCM_validation) for public use, to serve as a guide for future simulation model assessments, validation, and implementation.

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.

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit

INTRODUCTION

Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create a model of the Earth for climate research. While digital twins hold much promise for the neurocritical care unit, the question remains on how to best establish the rules that govern these models. This model will expand on our group's existing digital twin model for the treatment of sepsis.

METHODS

The authors of this project collaborated to create a Direct Acyclic Graph (DAG) and an initial series of 20 DELPHI statements, each with six accompanying sub-statements that captured the pathophysiology surrounding the management of acute ischemic strokes in the practice of Neurocritical Care (NCC). Agreement from a panel of 18 experts in the field of NCC was collected through a 7-point Likert scale with consensus defined a-priori by ≥ 80% selection of a 6 ("agree") or 7 ("strongly agree"). The endpoint of the study was defined as the completion of three separate rounds of DELPHI consensus. DELPHI statements that had met consensus would not be included in subsequent rounds of DELPHI consensus. The authors refined DELPHI statements that did not reach consensus with the guidance of de-identified expert comments for subsequent rounds of DELPHI. All DELPHI statements that reached consensus by the end of three rounds of DELPHI consensus would go on to be used to inform the construction of the digital twin model.

RESULTS

After the completion of three rounds of DELPHI, 93 (77.5%) statements reached consensus, 11 (9.2%) statements were excluded, and 16 (13.3%) statements did not reach a consensus of the original 120 DELPHI statements.

CONCLUSION

This descriptive study demonstrates the use of the DELPHI process to generate consensus among experts and establish a set of rules for the development of a digital twin model for use in the neurologic ICU. Compared to associative models of AI, which develop rules based on finding associations in datasets, digital twin AI created by the DELPHI process are easily interpretable models based on a current understanding of underlying physiology.

Acute on chronic liver failure: prognostic models and artificial intelligence applications

Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.

Forecasting Obesity and Type 2 Diabetes Incidence and Burden: The ViLA-Obesity Simulation Model

Background

Obesity is a major public health problem affecting millions of Americans and is considered one of the most potent risk factors for type 2 diabetes. Assessing future disease burden is important for informing policy-decision making for population health and healthcare.

Objective

The aim of this study was to develop a computer model of a cohort of children born in Los Angeles County to study the life course incidence and trends of obesity and its effect on type 2 diabetes mellitus.

Methods

We built the Virtual Los Angeles cohort—ViLA, an agent-based model calibrated to the population of Los Angeles County. In particular, we developed the ViLA-Obesity model, a simulation suite within our ViLA platform that integrated trends in the causes and consequences of obesity, focusing on diabetes as a key obesity consequence during the life course. Each agent within the model exhibited obesity- and diabetes-related healthy and unhealthy behaviors such as sugar-sweetened beverage consumption, physical activity, fast-food consumption, fresh fruits, and vegetable consumption. In addition, agents could gain or lose weight and develop type 2 diabetes mellitus with a certain probability dependent on the agent's socio-demographics, past behaviors and past weight or type 2 diabetes status. We simulated 98,230 inhabitants from birth to age 65 years, living in 235 neighborhoods.

Results

The age-specific incidence of obesity generally increased from 10 to 30% across the life span with two notable peaks at age 6–12 and 30–39 years, while that of type 2 diabetes mellitus generally increased from <2% at age 18–24 to reach a peak of 25% at age 40–49. The 16-year risks of obesity were 32.1% (95% CI: 31.8%, 32.4%) for children aged 2–17 and 81% (95% CI: 80.8%, 81.3%) for adults aged 18–65. The 48-year risk of type 2 diabetes mellitus was 53.4% (95% CI: 53.1%, 53.7%) for adults aged 18–65.

Conclusion

This ViLA-Obesity model provides an insight into the future burden of obesity and type 2 diabetes mellitus in Los Angeles County, one of the most diverse places in the United States. It serves as a platform for conducting experiments for informing evidence-based policy-making.

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.

Predictive modeling in neurocritical care using causal artificial intelligence

Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.

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.

Cost-Effectiveness of Multitarget Stool DNA Testing vs Colonoscopy or Fecal Immunochemical Testing for Colorectal Cancer Screening in Alaska Native People

OBJECTIVE

To estimate the cost-effectiveness of multitarget stool DNA testing (MT-sDNA) compared with colonoscopy and fecal immunochemical testing (FIT) for Alaska Native adults.

PATIENTS AND METHODS

A Markov model was used to evaluate the 3 screening test effects over 40 years. Outcomes included colorectal cancer (CRC) incidence and mortality, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). The study incorporated updated evidence on screening test performance and adherence and was conducted from December 15, 2016, through November 6, 2019.

RESULTS

With perfect adherence, CRC incidence was reduced by 52% (95% CI, 46% to 56%) using colonoscopy, 61% (95% CI, 57% to 64%) using annual FIT, and 66% (95% CI, 63% to 68%) using MT-sDNA. Compared with no screening, perfect adherence screening extends life by 0.15, 0.17, and 0.19 QALYs per person with colonoscopy, FIT, and MT-sDNA, respectively. Colonoscopy is the most expensive strategy: approximately $110 million more than MT-sDNA and $127 million more than FIT. With imperfect adherence (best case), MT-sDNA resulted in 0.12 QALYs per person vs 0.05 and 0.06 QALYs per person by FIT and colonoscopy, respectively. Probabilistic sensitivity analyses supported the base-case analysis. Under varied adherence scenarios, MT-sDNA either dominates or is cost-effective (ICERs, $1740-$75,868 per QALY saved) compared with FIT and colonoscopy.

CONCLUSION

Each strategy reduced costs and increased QALYs compared with no screening. Screening by MT-sDNA results in the largest QALY savings. In Markov model analysis, screening by MT-sDNA in the Alaska Native population was cost-effective compared with screening by colonoscopy and FIT for a wide range of adherence scenarios.

Economic Simulation Modeling in Type 2 Diabetes

PURPOSE OF REVIEW

This paper provides an overview of type 2 diabetes economic simulation modeling and reviews current topics of discussion and major challenges in the field.

RECENT FINDINGS

Important challenges in the field include increasing the generalizability of models and improving transparency in model reporting. To identify and address these issues, modeling groups have organized through the Mount Hood Diabetes Challenge meetings and developed tools (i.e., checklist, impact inventory) to standardize modeling methods and reporting of results. Accordingly, many newer diabetes models have begun utilizing these tools, allowing for improved comparability between diabetes models. In the last two decades, type 2 diabetes simulation models have improved considerably, due to the collaborative work performed through the Mount Hood Diabetes Challenge meetings. To continue to improve diabetes models, future work must focus on clarifying diabetes progression in racial/ethnic minorities and incorporating equity considerations into health economic analysis.

Economic Modelling of Chronic Kidney Disease: A Systematic Literature Review to Inform Conceptual Model Design

BACKGROUND

Chronic kidney disease (CKD) is a progressive condition that leads to irreversible damage to the kidneys and is associated with an increased incidence of cardiovascular events and mortality. As novel interventions become available, estimates of economic and clinical outcomes are needed to guide payer reimbursement decisions.

OBJECTIVE

The aim of the present study was to systematically review published economic models that simulated long-term outcomes of kidney disease to inform cost-effectiveness evaluations of CKD treatments.

METHODS

The review was conducted across four databases (MEDLINE, Embase, the Cochrane library and EconLit) and health technology assessment agency websites. Relevant information on each model was extracted. Transition and mortality rates were also extracted to assess the choice of model parameterisation on disease progression by simulating patient's time with end-stage renal disease (ESRD) and time to ESRD/death. The incorporation of cardiovascular disease in a population with CKD was qualitatively assessed across identified models.

RESULTS

The search identified 101 models that met the criteria for inclusion. Models were classified into CKD models (n = 13), diabetes models with nephropathy (n = 48), ESRD-only models (n = 33) and cardiovascular models with CKD components (n = 7). Typically, published models utilised frameworks based on either (estimated or measured) glomerular filtration rate (GFR) or albuminuria, in line with clinical guideline recommendations for the diagnosis and monitoring of CKD. Generally, two core structures were identified, either a microsimulation model involving albuminuria or a Markov model utilising CKD stages and a linear GFR decline (although further variations on these model structures were also identified). Analysis of parameter variability in CKD disease progression suggested that mean time to ESRD/death was relatively consistent across model types (CKD models 28.2 years; diabetes models with nephropathy 24.6 years). When evaluating time with ESRD, CKD models predicted extended ESRD survival over diabetes models with nephropathy (mean time with ESRD 8.0 vs. 3.8 years).

DISCUSSION

This review provides an overview of how CKD is typically modelled. While common frameworks were identified, model structure varied, and no single model type was used for the modelling of patients with CKD. In addition, many of the current methods did not explicitly consider patient heterogeneity or underlying disease aetiology, except for diabetes. However, the variability of individual patients' GFR and albuminuria trajectories perhaps provides rationale for a model structure designed around the prediction of individual patients' GFR trajectories. Frameworks of future CKD models should be informed and justified based on clinical rationale and availability of data to ensure validity of model results. In addition, further clinical and observational research is warranted to provide a better understanding of prognostic factors and data sources to improve economic modelling accuracy in CKD.

Approaches to Risk Assessment Among Older Patients With Diabetes

PURPOSE OF REVIEW

A patient's prognosis and risk of adverse drug effects are important considerations for individualizing care of older patients with diabetes. This review summarizes the evidence for risk assessment and proposes approaches for clinicians in the context of current clinical guidelines.

RECENT FINDINGS

Diabetes guidelines vary in their recommendations for how life expectancy should be estimated and used to inform the selection of glycemic targets. Readily available prognostic tools may improve estimation of life expectancy but require validation among patients with diabetes. Treatment decisions based on prognosis are difficult for clinicians to communicate and for patients to understand. Determining hypoglycemia risk involves assessing major risk factors; models to synthesize these factors have been developed. Applying risk assessment to individualize diabetes care is complex and currently relies heavily on clinician judgment. More research is need to validate structured approaches to risk assessment and determine how to incorporate them into patient-centered diabetes care.

Current Status and Future Directions for Electronic Point-of-Care Clinical Decision Support to Improve Diabetes Management in Primary Care

In the past decade there have been major improvements in the design, use, and effectiveness of point-of-care clinical decision support (CDS) systems to improve quality of care for patients with diabetes and related conditions. Advances in data exchange, data security, and human factors research have driven these improvements. Current diabetes CDS systems have high use rates, high clinician/user satisfaction rates, and have measurably improved glucose control, blood pressure control, and cardiovascular risk trajectories in adults with diabetes. As diabetes care increasingly relies on complex biomarker-driven risk prediction methods to optimize care goals and prioritize treatment options based on potential benefit to an individual patient, CDS systems will become indispensable tools to guide clinician and patient decision-making. In this study we describe specific challenges that must be addressed further to improve the design, implementation, and effectiveness of primary care diabetes CDS systems in coming years.

Impact of Public Health Interventions on Obesity and Type 2 Diabetes Prevention: A Simulation Study

INTRODUCTION

Little is known about what interventions worked or did not work in slowing the obesity epidemic. The long-term comparative effectiveness of environmental and behavioral public health interventions for obesity and type 2 diabetes prevention over an individual's life course is relatively unexplored. The potential impact and long-term collective effectiveness of environmental and behavioral interventions on obesity and type 2 diabetes throughout the life course was evaluated.

METHODS

The Virtual Los Angeles Obesity Model developed in 2016 was used to estimate the incidence and prevalence of obesity and type 2 diabetes under current and hypothetical interventions among 98,000 individuals born in 2009 and followed from birth to age 65 years. Analyses were performed in 2016 and completed in 2018.

RESULTS

The 48-year risk of type 2 diabetes was 0.533 (95% CI=0.446, 0.629) under the natural course, 0.451 (95% CI=0.334, 0.570) under the physical activity intervention, and 0.443 (95% CI=0.389, 0.495) under the fast-food intervention. The 64-year risk of obesity was 0.892 (95% CI=0.879, 0.903) under the natural course, 0.876 (95% CI=0.850, 0.899) under the physical activity intervention, and 0.864 (95% CI=0.856, 0.873) under the fast-food intervention. The other interventions had little or no long-term effects. When all the interventions were applied, the population risk ratios were 0.942 (95% CI=0.914, 0.967) and 0.634 (95% CI=0.484, 0.845) for obesity and type 2 diabetes, respectively.

CONCLUSIONS

Implementing health interventions continuously throughout the life span and in combination with other interventions could substantially halt the obesity and the type 2 diabetes epidemics.

Event simulation and external validation applied in published health economic models for obesity: a systematic review

INTRODUCTION

This study aims to determine methodological variations in the event simulation approaches of published health economic decision models, in the field of obesity, and to investigate whether their predictiveness and validity were investigated via external event validation techniques, which investigate how well the model reproduces reality.

AREAS COVERED

A systematic review identified a total of 87 relevant papers, of which 72 that simulated obesity-associated events were included. Most frequently simulated events were coronary heart disease (≈ 83%), type 2 diabetes (≈ 74%), and stroke (≈ 66%). Only for ten published model-based health economic assessments in obesity an external event validation was performed (14%; 10 of 72), and only for one the predictiveness and validity of the event simulation was investigated in a cohort of obese subjects.

EXPERT COMMENTARY

We identified a wide range of obesity related event simulation approaches. Published obesity models lack information on the predictive quality and validity of the applied event simulation approaches. Further work on comparing and validating these event simulation approaches is required to investigate their predictiveness and validity, which will offer guidance future modelling in the field of obesity.

Development of a Pharmacoeconomic Model to Demonstrate the Effect of Clinical Pharmacist Involvement in Diabetes Management

BACKGROUND

A data collection tool was developed and nationally deployed to clinical pharmacists (CPs) working in advanced practice provider roles within the Department of Veterans Affairs to document interventions and associated clinical outcomes. Intervention and short-term clinical outcome data derived from the tool were used to populate a validated clinical outcomes modeling program to predict long-term clinical and economic effects.

OBJECTIVE

To predict the long-term effect of CP-provided pharmacotherapy management on outcomes and costs for patients with type 2 diabetes.

METHODS

Baseline patient demographics and biomarkers were extracted for type 2 diabetic patients having > 1 encounter with a CP using the tool between January 5, 2013, and November 20, 2014. Treatment biomarker values were extracted 12 months after the patient's initial visit with the CP. The number of visits with the CP was extracted from the electronic medical record, and duration of visit time was quantified by Current Procedural Terminology codes. Simulation modeling was performed on 3 patient cohorts-those with a baseline hemoglobin A1c of 8% to < 9%, 9% to < 10%, and ≥ 10%-to estimate long-term cost and clinical outcomes using modeling based on pivotal trial data (the Archimedes Model). A sensitivity analysis was conducted to assess the extent to which our results were dependent on assumptions related to program effectiveness and costs.

RESULTS

A total of 7,310 patients were included in the analysis. Analysis of costs and events on 2-, 3-, 5-, and 10-year time horizons demonstrated significant reductions in major adverse cardiovascular events (MACEs), myocardial infarctions (MIs), episodes of acute heart failure, foot ulcers, and foot amputations in comparison with a control group receiving usual guideline-directed medical care. In the cohort with a baseline A1c of ≥ 10%, the absolute risk reduction was 1.82% for MACE, 1.73% for MI, 2.43% for acute heart failure, 5.38% for foot ulcers, and 2.03% for foot amputations. The incremental cost-effectiveness ratios for cost per quality-adjusted life-year during the 2-, 3-, 5-, and 10-year time horizons were cost-effective for the cohorts of patients with a baseline A1c of 9% to < 10% and ≥ 10%.

CONCLUSIONS

CPs acting as advanced practice providers reduced A1c from baseline for veterans with type 2 diabetes compared with modeled usual care. Archimedes modeling of the A1c reductions projects a decreased incidence of diabetes complications and overall health care spending when compared with modeled usual care.

DISCLOSURES

There was no outside funding source or sponsor for this project. None of the authors report any conflicts of interest. The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the U.S. Department of Veterans Affairs. Preliminary data from this project were previously presented in abstract form at the Academy of Managed Care Pharmacy 27th Annual Meeting and Expo; April 8-10, 2015; in San Diego, California.

Modeling of Diabetes and Its Clinical Impact

Understanding all aspects of diabetes treatment is hindered by the complexity of this chronic disease and its multifaceted complications and comorbidities, including social and financial impacts. In vivo studies as well as clinical trials provided invaluable information for unraveling not only metabolic processes but also risk estimations of, for example, complications. These approaches are often time- and cost-consuming and have frequently been supported by simulation models. Simulation models provide the opportunity to investigate diabetes treatment from additional viewpoints and with alternative objectives. This review presents selected models focusing either on metabolic processes or risk estimations and financial outcomes to provide a basic insight into this complex subject. It also discusses opportunities and challenges of modeling diabetes.

Simultaneous Risk Factor Control Using Telehealth to slOw Progression of Diabetic Kidney Disease (STOP-DKD) study: Protocol and baseline characteristics of a randomized controlled trial

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the United States. Multiple risk factors contribute to DKD development, yet few interventions target more than a single DKD risk factor at a time. This manuscript describes the study protocol, recruitment, and baseline participant characteristics for the Simultaneous Risk Factor Control Using Telehealth to slOw Progression of Diabetic Kidney Disease (STOP-DKD) study. The STOP-DKD study is a randomized controlled trial designed to evaluate the effectiveness of a multifactorial behavioral and medication management intervention to mitigate kidney function decline at 3 years compared to usual care. The intervention consists of up to 36 monthly educational modules delivered via telephone by a study pharmacist, home blood pressure monitoring, and medication management recommendations delivered electronically to primary care physicians. Patients seen at seven primary care clinics in North Carolina, with diabetes and [1] uncontrolled hypertension and [2] evidence of kidney dysfunction (albuminuria or reduced estimated glomerular filtration rate [eGFR]) were eligible to participate. Study recruitment completed in December 2014. Of the 281 participants randomized, mean age at baseline was 61.9; 52% were male, 56% were Black, and most were high school graduates (89%). Baseline co-morbidity was high- mean blood pressure was 134/76 mmHg, mean body mass index was 35.7 kg/m2, mean eGFR was 80.7 ml/min/1.73 m2, and mean glycated hemoglobin was 8.0%. Experiences of recruiting and implementing a comprehensive DKD program to individuals at high risk seen in the primary care setting are provided.

TRIAL REGISTRATION

NCT01829256.

The current and potential health benefits of the National Health Service Health Check cardiovascular disease prevention programme in England: A microsimulation study

BACKGROUND

The National Health Service (NHS) Health Check programme was introduced in 2009 in England to systematically assess all adults in midlife for cardiovascular disease risk factors. However, its current benefit and impact on health inequalities are unknown. It is also unclear whether feasible changes in how it is delivered could result in increased benefits. It is one of the first such programmes in the world. We sought to estimate the health benefits and effect on inequalities of the current NHS Health Check programme and the impact of making feasible changes to its implementation.

METHODS AND FINDINGS

We developed a microsimulation model to estimate the health benefits (incident ischaemic heart disease, stroke, dementia, and lung cancer) of the NHS Health Check programme in England. We simulated a population of adults in England aged 40-45 years and followed until age 100 years, using data from the Health Survey of England (2009-2012) and the English Longitudinal Study of Aging (1998-2012), to simulate changes in risk factors for simulated individuals over time. We used recent programme data to describe uptake of NHS Health Checks and of 4 associated interventions (statin medication, antihypertensive medication, smoking cessation, and weight management). Estimates of treatment efficacy and adherence were based on trial data. We estimated the benefits of the current NHS Health Check programme compared to a healthcare system without systematic health checks. This counterfactual scenario models the detection and treatment of risk factors that occur within 'routine' primary care. We also explored the impact of making feasible changes to implementation of the programme concerning eligibility, uptake of NHS Health Checks, and uptake of treatments offered through the programme. We estimate that the NHS Health Check programme prevents 390 (95% credible interval 290 to 500) premature deaths before 80 years of age and results in an additional 1,370 (95% credible interval 1,100 to 1,690) people being free of disease (ischaemic heart disease, stroke, dementia, and lung cancer) at age 80 years per million people aged 40-45 years at baseline. Over the life of the cohort (i.e., followed from 40-45 years to 100 years), the changes result in an additional 10,000 (95% credible interval 8,200 to 13,000) quality-adjusted life years (QALYs) and an additional 9,000 (6,900 to 11,300) years of life. This equates to approximately 300 fewer premature deaths and 1,000 more people living free of these diseases each year in England. We estimate that the current programme is increasing QALYs by 3.8 days (95% credible interval 3.0-4.7) per head of population and increasing survival by 3.3 days (2.5-4.1) per head of population over the 60 years of follow-up. The current programme has a greater absolute impact on health for those living in the most deprived areas compared to those living in the least deprived areas (4.4 [2.7-6.5] days of additional quality-adjusted life per head of population versus 2.8 [1.7-4.0] days; 5.1 [3.4-7.1] additional days lived per head of population versus 3.3 [2.1-4.5] days). Making feasible changes to the delivery of the existing programme could result in a sizable increase in the benefit. For example, a strategy that combines extending eligibility to those with preexisting hypertension, extending the upper age of eligibility to 79 years, increasing uptake of health checks by 30%, and increasing treatment rates 2.5-fold amongst eligible patients (i.e., 'maximum potential' scenario) results in at least a 3-fold increase in benefits compared to the current programme (1,360 premature deaths versus 390; 5,100 people free of 1 of the 4 diseases versus 1,370; 37,000 additional QALYs versus 10,000; 33,000 additional years of life versus 9,000). Ensuring those who are assessed and eligible for statins receive statins is a particularly important strategy to increase benefits. Estimates of overall benefit are based on current incidence and management, and future declines in disease incidence or improvements in treatment could alter the actual benefits observed in the long run. We have focused on the cardiovascular element of the NHS Health Check programme. Some important noncardiovascular health outcomes (e.g., chronic obstructive pulmonary disease [COPD] prevention from smoking cessation and cancer prevention from weight loss) and other parts of the programme (e.g., brief interventions to reduce harmful alcohol consumption) have not been modelled.

CONCLUSIONS

Our model indicates that the current NHS Health Check programme is contributing to improvements in health and reducing health inequalities. Feasible changes in the organisation of the programme could result in more than a 3-fold increase in health benefits.

Delays in treatment intensification with oral antidiabetic drugs and risk of microvascular and macrovascular events in patients with poor glycaemic control: An individual patient simulation study

AIMS

To use the Archimedes model to estimate the consequences of delays in oral antidiabetic drug (OAD) treatment intensification on glycaemic control and long-term outcomes at 5 and 20 years.

MATERIALS AND METHODS

Using real-world data, we modelled a cohort of hypothetical patients with glycated haemoglobin (HbA1c) ≥8%, on metformin, with no history of insulin use. The cohort included 3 strata based on the number of OADs taken at baseline. The first add-on in the intensification sequence was a sulphonylurea, next was a dipeptidyl peptidase-4 inhibitor, and last, a thiazolidinedione. The scenarios included either no delay or delay, based on observed and extrapolated times to intensification.

RESULTS

At 1 year, HbA1c was 6.8% for patients intensifying without delay, and 8.2% for those delaying intensification. For no delay vs delay, risks of major adverse cardiac events, myocardial infarction, heart failure and amputations were reduced by 18.0%, 25.0%, 13.7%, and 20.4%, respectively, at 5 years; severe hypoglycaemia risk, however, increased to 19% for the no delay scenario vs 12.5% for delay. At 20 years, the results showed similar trends to those at 5 years.

CONCLUSIONS

Timing of intensification of OAD therapy according to guideline recommendations led to greater reductions in HbA1c and lower risks of complications, but higher risks of hypoglycaemia than delaying intensification. These results highlight the potential impact of timely treatment intensification on long-term outcomes.

Description of a New Predictive Modeling Approach That Correlates the Risk and Associated Cost of Well-Defined Diabetes-Related Complications With Changes in Glycated Hemoglobin (HbA1c)

The modeling approach described here is designed to support the development of spreadsheet-based simple predictive models. It is based on 3 pillars: association of the complications with HbA1c changes, incidence of the complications, and average cost per event of the complication. For each pillar, the goal of the analysis was (1) to find results for a large diversity of populations with a focus on countries/regions, diabetes type, age, diabetes duration, baseline HbA1c value, and gender; (2) to assess the range of incidences and associations previously reported. Unlike simple predictive models, which mostly are based on only 1 source of information for each of the pillars, we conducted a comprehensive, systematic literature review. Each source found was thoroughly reviewed and only sources meeting quality expectations were considered. The approach allows avoidance of unintended use of extreme data. The user can utilize (1) one of the found sources, (2) the found range as validation for the found figures, or (3) the average of all found publications for an expedited estimate. The modeling approach is intended for use in average insulin-treated diabetes populations in which the baseline HbA1c values are within an average range (6.5% to 11.5%); it is not intended for use in individuals or unique diabetes populations (eg, gestational diabetes). Because the modeling approach only considers diabetes-related complications that are positively associated with HbA1c decreases, the costs of negatively associated complications (eg, severe hypoglycemic events) must be calculated separately.

Validation of the Economic and Health Outcomes Model of Type 2 Diabetes Mellitus (ECHO-T2DM)

BACKGROUND

The Economic and Health Outcomes Model of Type 2 Diabetes Mellitus (ECHO-T2DM) was developed to address study questions pertaining to the cost-effectiveness of treatment alternatives in the care of patients with type 2 diabetes mellitus (T2DM). Naturally, the usefulness of a model is determined by the accuracy of its predictions. A previous version of ECHO-T2DM was validated against actual trial outcomes and the model predictions were generally accurate. However, there have been recent upgrades to the model, which modify model predictions and necessitate an update of the validation exercises.

OBJECTIVES

The objectives of this study were to extend the methods available for evaluating model validity, to conduct a formal model validation of ECHO-T2DM (version 2.3.0) in accordance with the principles espoused by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM), and secondarily to evaluate the relative accuracy of four sets of macrovascular risk equations included in ECHO-T2DM.

METHODS

We followed the ISPOR/SMDM guidelines on model validation, evaluating face validity, verification, cross-validation, and external validation. Model verification involved 297 'stress tests', in which specific model inputs were modified systematically to ascertain correct model implementation. Cross-validation consisted of a comparison between ECHO-T2DM predictions and those of the seminal National Institutes of Health model. In external validation, study characteristics were entered into ECHO-T2DM to replicate the clinical results of 12 studies (including 17 patient populations), and model predictions were compared to observed values using established statistical techniques as well as measures of average prediction error, separately for the four sets of macrovascular risk equations supported in ECHO-T2DM. Sub-group analyses were conducted for dependent vs. independent outcomes and for microvascular vs. macrovascular vs. mortality endpoints.

RESULTS

All stress tests were passed. ECHO-T2DM replicated the National Institutes of Health cost-effectiveness application with numerically similar results. In external validation of ECHO-T2DM, model predictions agreed well with observed clinical outcomes. For all sets of macrovascular risk equations, the results were close to the intercept and slope coefficients corresponding to a perfect match, resulting in high R ² and failure to reject concordance using an F test. The results were similar for sub-groups of dependent and independent validation, with some degree of under-prediction of macrovascular events.

CONCLUSION

ECHO-T2DM continues to match health outcomes in clinical trials in T2DM, with prediction accuracy similar to other leading models of T2DM.

Validation and calibration of structural models that combine information from multiple sources

INTRODUCTION

Mathematical models that attempt to capture structural relationships between their components and combine information from multiple sources are increasingly used in medicine. Areas covered: We provide an overview of methods for model validation and calibration and survey studies comparing alternative approaches. Expert commentary: Model validation entails a confrontation of models with data, background knowledge, and other models, and can inform judgments about model credibility. Calibration involves selecting parameter values to improve the agreement of model outputs with data. When the goal of modeling is quantitative inference on the effects of interventions or forecasting, calibration can be viewed as estimation. This view clarifies issues related to parameter identifiability and facilitates formal model validation and the examination of consistency among different sources of information. In contrast, when the goal of modeling is the generation of qualitative insights about the modeled phenomenon, calibration is a rather informal process for selecting inputs that result in model behavior that roughly reproduces select aspects of the modeled phenomenon and cannot be equated to an estimation procedure. Current empirical research on validation and calibration methods consists primarily of methodological appraisals or case-studies of alternative techniques and cannot address the numerous complex and multifaceted methodological decisions that modelers must make. Further research is needed on different approaches for developing and validating complex models that combine evidence from multiple sources.

Use of Biomarkers in Predicting the Onset, Monitoring the Progression, and Risk Stratification for Patients with Type 2 Diabetes Mellitus

BACKGROUND

As the worldwide prevalence of type 2 diabetes mellitus (T2DM) increases, it is even more important to develop cost-effective methods to predict and diagnose the onset of diabetes, monitor progression, and risk stratify patients in terms of subsequent cardiovascular and diabetes complications.

CONTENT

Nonlaboratory clinical risk scores based on risk factors and anthropomorphic data can help identify patients at greatest risk of developing diabetes, but glycemic indices (hemoglobin A1c, fasting plasma glucose, and oral glucose tolerance tests) are the cornerstones for diagnosis, and the basis for monitoring therapy. Although family history is a strong predictor of T2DM, only small populations of patients carry clearly identifiable genetic mutations. Better modalities for detection of insulin resistance would improve earlier identification of dysglycemia and guide effective therapy based on therapeutic mechanisms of action, but improved standardization of insulin assays will be required. Although clinical risk models can stratify patients for subsequent cardiovascular risk, the addition of cardiac biomarkers, in particular, high-sensitivity troponin and natriuretic peptide provide, significantly improves model performance and risk stratification.

CONCLUSIONS

Much more research, prospectively planned and with clear treatment implications, is needed to define novel biomarkers that better identify the underlying pathogenic etiologies of dysglycemia. When compared with traditional risk features, biomarkers provide greater discrimination of future risk, and the integration of cardiac biomarkers should be considered part of standard risk stratification in patients with T2DM.

GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world

The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real‐world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model and predictions, data sources, validation and sensitivity analyses, disease area and software. We identified 12 articles in which four approaches were used: multi‐state models, discrete event simulation models, physiology‐based models and survival and generalized linear models. Studies predicted outcomes over longer time periods in different patient populations, including patients with lower levels of adherence or persistence to treatment or examined doses not tested in trials. Eight studies included individual patient data. Seven examined cardiovascular and metabolic diseases and three neurological conditions. Most studies included sensitivity analyses, but external validation was performed in only three studies. We conclude that mathematical modelling to predict real‐world effectiveness of drug interventions is not widely used at present and not well validated. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.

Why Do Health Economists Promote Technology Adoption Rather Than the Search for Efficiency? A Proposal for a Change in Our Approach to Economic Evaluation in Health Care

At a time of intense pressure on health care budgets, the technology management challenge is for disinvestment in low-value technologies and reinvestment in higher value alternatives. The aim of this article is to explore ways in which health economists might begin to redress the observed imbalance between the evaluation of new and existing in-use technologies. The argument is not against evaluating new technologies but in favor of the “search for efficiency,” where the ultimate objective is to identify reallocations that improve population health in the face of resource scarcity. We explore why in-use technologies may be of low value and consider how economic evaluation analysts might embrace a broader efficiency lens, first through “technology management” (a process of analysis and evidence-informed decision making throughout a technology’s life cycle) and progressing through “pathway management” (the search for efficiency gains across entire clinical care pathways). A number of model-based examples are used to illustrate the approaches.

Evaluation of a patient self-directed mealtime insulin titration algorithm: a US payer perspective

Objective To model the potential economic impact of implementing the AUTONOMY once daily (Q1D) patient self-titration mealtime insulin dosing algorithm vs standard of care (SOC) among a population of patients with Type 2 diabetes living in the US. Methods Three validated models were used in this analysis: The Treatment Transitions Model (TTM) was used to generate the primary results, while both the Archimedes (AM) and IMS Core Diabetes Models (IMS) were used to test the veracity of the primary results produced by TTM. Models used data from a 'real world' representative sample of patients (2012 US National Health and Nutrition Examination Survey) that matched the characteristics of US patients enrolled in the randomized controlled trial 'AUTONOMY' cohort. The base-case time horizon was 10 years. Results The modeling results from TTM demonstrated that total costs in the base-case were reduced by $1732, with savings predicted to occur as early as year 1. Results from the three models were consistent, showing a reduction in total costs for all sensitivity analyses. Limitations Data from short-term clinical trials were used to develop long-term projections. The nature of such extrapolation leads to increased uncertainty. Conclusion The results from all three models indicate that the AUTONOMY Q1D algorithm has the potential to abate total costs as early as the first year.

A Systematic Review of Cost-Effectiveness Models in Type 1 Diabetes Mellitus

BACKGROUND

Critiques of cost-effectiveness modelling in type 1 diabetes mellitus (T1DM) are scarce and are often undertaken in combination with type 2 diabetes mellitus (T2DM) models. However, T1DM is a separate disease, and it is therefore important to appraise modelling methods in T1DM.

OBJECTIVES

This review identified published economic models in T1DM and provided an overview of the characteristics and capabilities of available models, thus enabling a discussion of best-practice modelling approaches in T1DM.

METHODS

A systematic review of Embase(®), MEDLINE(®), MEDLINE(®) In-Process, and NHS EED was conducted to identify available models in T1DM. Key conferences and health technology assessment (HTA) websites were also reviewed. The characteristics of each model (e.g. model structure, simulation method, handling of uncertainty, incorporation of treatment effect, data for risk equations, and validation procedures, based on information in the primary publication) were extracted, with a focus on model capabilities.

RESULTS

We identified 13 unique models. Overall, the included studies varied greatly in scope as well as in the quality and quantity of information reported, but six of the models (Archimedes, CDM [Core Diabetes Model], CRC DES [Cardiff Research Consortium Discrete Event Simulation], DCCT [Diabetes Control and Complications Trial], Sheffield, and EAGLE [Economic Assessment of Glycaemic control and Long-term Effects of diabetes]) were the most rigorous and thoroughly reported. Most models were Markov based, and cohort and microsimulation methods were equally common. All of the more comprehensive models employed microsimulation methods. Model structure varied widely, with the more holistic models providing a comprehensive approach to microvascular and macrovascular events, as well as including adverse events. The majority of studies reported a lifetime horizon, used a payer perspective, and had the capability for sensitivity analysis.

CONCLUSIONS

Several models have been developed that provide useful insight into T1DM modelling. Based on a review of the models identified in this study, we identified a set of 'best in class' methods for the different technical aspects of T1DM modelling.

How Do Diabetes Models Measure Up? A Review of Diabetes Economic Models and ADA Guidelines

Introduction: Economic models and computer simulation models have been used for assessing short-term cost-effectiveness of interventions and modelling long-term outcomes and costs. Several guidelines and checklists have been published to improve the methods and reporting. This article presents an overview of published diabetes models with a focus on how well the models are described in relation to the considerations described by the American Diabetes Association (ADA) guidelines. Methods: Relevant electronic databases and National Institute for Health and Care Excellence (NICE) guidelines were searched in December 2012. Studies were included in the review if they estimated lifetime outcomes for patients with type 1 or type 2 diabetes. Only unique models, and only the original papers were included in the review. If additional information was reported in subsequent or paired articles, then additional citations were included. References and forward citations of relevant articles, including the previous systematic reviews were searched using a similar method to pearl growing. Four principal areas were included in the ADA guidance reporting for models: transparency, validation, uncertainty, and diabetes specific criteria. Results: A total of 19 models were included. Twelve models investigated type 2 diabetes, two developed type 1 models, two created separate models for type 1 and type 2, and three developed joint type 1 and type 2 models. Most models were developed in the United States, United Kingdom, Europe or Canada. Later models use data or methods from earlier models for development or validation. There are four main types of models: Markov-based cohort, Markov-based microsimulations, discrete-time microsimulations, and continuous time differential equations. All models were long-term diabetes models incorporating a wide range of compilations from various organ systems. In early diabetes modelling, before the ADA guidelines were published, most models did not include descriptions of all the diabetes specific components of the ADA guidelines but this improved significantly by 2004. Conclusion: A clear, descriptive short summary of the model was often lacking. Descriptions of model validation and uncertainty were the most poorly reported of the four main areas, but there exist conferences focussing specifically on the issue of validation. Interdependence between the complications was the least well incorporated or reported of the diabetes-specific criterion.

Impact of treatment with rosuvastatin and atorvastatin on cardiovascular outcomes: evidence from the Archimedes-simulated clinical trials

Objective

No clinical trials have been conducted to directly compare the effect of the two high-intensity statins, rosuvastatin and atorvastatin, on cardiovascular outcomes. However, three such trials have been computer-simulated using the Archimedes model, an individual-based simulation of human physiology and behaviors, treatment interventions, and health care systems. The results are reviewed here.

Methods

The first simulated trial assessed clinical outcomes in patients receiving available doses of the two drugs. The second assessed the impact of initial treatment decisions, while the third assessed the effect of switching from rosuvastatin to atorvastatin.

Results

In the first simulated trial, treatment with rosuvastatin was estimated to result in greater reductions than treatment with atorvastatin in major adverse cardiac event (MACE) rates at 5 years and 20 years at all doses examined (relative risk [RR]: 0.897, 0.888, and 0.930 at 5 years for rosuvastatin 20 mg vs atorvastatin 40 mg, rosuvastatin 40 mg vs atorvastatin 80 mg, and rosuvastatin 20 mg vs atorvastatin 80 mg, respectively; all P<0.05). In the second simulated trial, outcomes were significantly better in patients initially prescribed rosuvastatin than in those initially prescribed atorvastatin (RR of MACE at 5 years: 0.918; P<0.001). In the third simulated trial, risk of MACE was significantly greater in patients switching from rosuvastatin to atorvastatin than in those remaining on rosuvastatin (RR at 5 years: 1.109; P<0.001).

Conclusion

The results of these simulated clinical trials suggest improved outcomes among patients receiving rosuvastatin relative to patients receiving atorvastatin in various clinical settings.

Methods for Building and Validating Equations for Physiology-Based Mathematical Models: Glucose Metabolism and Type 2 Diabetes in the Archimedes Model

OBJECTIVES

Describe steps for deriving and validating equations for physiology processes for use in mathematical models. Illustrate the steps using glucose metabolism and Type 2 diabetes in the Archimedes model.

METHODS AND RESULTS

The steps are as follows: identify relevant variables, describe their relationships, identify data sources that relate the variables, correct for biases in data sources, use curve fitting algorithms to estimate equations, validate the accuracy of curve fitting against empirical data, perform partially and fully independent external validations, examine any discrepancies to determine causes and make corrections, and periodically update and revalidate equations as necessary. Specific methods depend on the available data. Specific data sources and methods are illustrated for equations that represent the cause of Type 2 diabetes and its effect on fasting plasma glucose in the Archimedes model. Methods for validating the equations are illustrated. Applications enabled by including physiological equations in healthcare models are discussed.

CONCLUSIONS

The methods can be used to derive equations that represent the relationships between physiological variables and the causes of diseases and that validate well against empirical data.

Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient's autoregulatory capacity?

Bioregulatory systems medicine (BrSM) is a paradigm that aims to advance current medical practices. The basic scientific and clinical tenets of this approach embrace an interconnected picture of human health, supported largely by recent advances in systems biology and genomics, and focus on the implications of multi-scale interconnectivity for improving therapeutic approaches to disease. This article introduces the formal incorporation of these scientific and clinical elements into a cohesive theoretical model of the BrSM approach. The authors review this integrated body of knowledge and discuss how the emergent conceptual model offers the medical field a new avenue for extending the armamentarium of current treatment and healthcare, with the ultimate goal of improving population health.

Projection of osteoporosis-related fractures and costs in China: 2010-2050

A state-transition microsimulation model was used to project the substantial economic burden to the Chinese healthcare system of osteoporosis-related fractures. Annual number and costs of osteoporosis-related fractures were estimated to double by 2035 and will increase to 5.99 (95 % CI 5.44, 6.55) million fractures costing $25.43 (95 % CI 23.92, 26.95) billion by 2050. Consequently, cost-effective intervention policies must urgently be identified in an attempt to minimize the impact of fractures.

INTRODUCTION

The aim of the study was to project the osteoporosis-related fractures and costs for the Chinese population aged ≥50 years from 2010 to 2050.

METHODS

A state-transition microsimulation model was used to simulate the annual incident fractures and costs. The simulation was performed with a 1-year cycle length and from the Chinese healthcare system perspective. Incident fractures and annual costs were estimated from 100 unique patient populations for year 2010, by multiplying the age- and sex-specific annual fracture risks and costs of fracture by the corresponding population totals in each of the 100 categories. Projections for 2011-2050 were performed by multiplying the 2010 risks and costs of fracture by the respective annual population estimates. Costs were presented in 2013 US dollars.

RESULTS

Approximately 2.33 (95 % CI 2.08, 2.58) million osteoporotic fractures were estimated to occur in 2010, costing $9.45 (95 % CI 8.78, 10.11) billion. Females sustained approximately three times more fractures than males, accounting for 76 % of the total costs from 1.85 (95 % CI 1.68, 2.01) million fractures. The annual number and costs of osteoporosis-related fractures were estimated to double by 2035 and will increase to 5.99 (95 % CI 5.44, 6.55) million fractures costing $25.43 (95 % CI 23.92, 26.95) billion by 2050.

CONCLUSIONS

Our study demonstrated that osteoporosis-related fractures cause a substantial economic burden which will markedly increase over the coming decades. Consequently, healthcare resource planning must consider these increasing costs, and cost-effective screening and intervention policies must urgently be identified in an attempt to minimize the impact of fractures on the health of the burgeoning population as well as the healthcare budget.

Clinical trial simulation methods for estimating the impact of DPP-4 inhibitors on cardiovascular disease

Introduction

Dipeptidyl peptidase-4 (DPP-4) inhibitors are a class of oral antidiabetic agents for the treatment of type 2 diabetes mellitus, which lower blood glucose without causing severe hypoglycemia. However, the first cardiovascular (CV) safety trials have only recently reported their results, and our understanding of these therapies remains incomplete. Using clinical trial simulations, we estimated the effectiveness of DPP-4 inhibitors in preventing major adverse cardiovascular events (MACE) in a population like that enrolled in the SAVOR-TIMI (the Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus – Thrombolysis in Myocardial Infarction) 53 trial.

Methods

We used the Archimedes Model to simulate a clinical trial of individuals (N=11,000) with diagnosed type 2 diabetes and elevated CV risk, based on established disease or multiple risk factors. The DPP-4 class was modeled with a meta-analysis of HbA_1c and weight change, pooling results from published trials of alogliptin, linagliptin, saxagliptin, sitagliptin, and vildagliptin. The study treatments were added-on to standard care, and outcomes were tracked for 20 years.

Results

The DPP-4 class was associated with an HbA_1c drop of 0.66% (0.71%, 0.62%) and a weight drop of 0.14 (−0.07, 0.36) kg. These biomarker improvements produced a relative risk (RR) for MACE at 5 years of 0.977 (0.968, 0.986). The number needed to treat to prevent one occurrence of MACE at 5 years was 327 (233, 550) in the elevated CV risk population.

Conclusion

Consistent with recent trial publications, our analysis indicates that DPP-4 inhibitors do not increase the risk of MACE relative to the standard of care. This study provides insights about the long-term benefits of DPP-4 inhibitors and supports the interpretation of the published CV safety trial results.

Screening for and treatment of osteoporosis: construction and validation of a state-transition microsimulation cost-effectiveness model

This study aimed to document and validate a new cost-effectiveness model of osteoporosis screening and treatment strategies. The state-transition microsimulation model demonstrates strong internal and external validity. It is an important tool for researchers and policy makers to test the cost-effectiveness of osteoporosis screening and treatment strategies.

INTRODUCTION

The objective of this study was to document and validate a new cost-effectiveness model of screening for and treatment of osteoporosis.

METHODS

A state-transition microsimulation model using a lifetime horizon was constructed with seven Markov states (no history of fractures, hip fracture, vertebral fracture, wrist fracture, other fracture, postfracture state, and death) describing the most important clinical outcomes of osteoporotic fractures. Tracker variables were used to record patients' history, such as fracture events, duration of treatment, and time since last screening. The model was validated for Chinese postmenopausal women receiving screening and treatment versus no screening. Goodness-of-fit analyses were performed for internal and external validation. External validity was tested by comparing life expectancy, osteoporosis prevalence rate, and lifetime and 10-year fracture risks with published data not used in the model.

RESULTS

The model represents major clinical facets of osteoporosis-related conditions. Age-specific hip, vertebral, and wrist fracture incidence rates were accurately reproduced (the regression line slope was 0.996, R(2) = 0.99). The changes in costs, effectiveness, and cost-effectiveness were consistent with changes in both one-way and probabilistic sensitivity analysis. The model predicted life expectancy and 10-year any major osteoporotic fracture risk at the age of 65 of 19.01 years and 13.7%, respectively. The lifetime hip, clinical vertebral, and wrist fracture risks at age 50 were 7.9, 29.8, and 18.7% respectively, all consistent with reported data.

CONCLUSIONS

Our model demonstrated good internal and external validity, ensuring it can be confidently applied in economic evaluations of osteoporosis screening and treatment strategies.

A model of mild hypoglycemia

OBJECTIVE

To investigate the impact associated with mild hypoglycemia among patients with type 2 diabetes (T2DM) in the United States and to identify risk factors among different subpopulations.

METHODS

We performed a literature search to gather available data allowing estimation of rates of mild hypoglycemia. Because risk factors are interdependent, risk factors included in the model were based on those reported within multivariate analyses or judged to be biologically plausible by the medical community. Based on literature search results, we built a mathematical model predicting the rates of mild hypoglycemia in individual patients as a function of the patient's antidiabetic medications, hemoglobin A1c levels, duration of diabetes, kidney function, and body mass index.

RESULTS

We estimated an overall average rate of mild hypoglycemia among US patients with T2DM of 2.2 ± 0.8 events per person per year. Patients taking oral antidiabetic medications only had an average rate of 1.9 ± 0.8 events per person per year. The average rate for all patients taking insulin, including those combining it with other antidiabetic medications, was 4.9 ± 2.0 events per person per year. Mild hypoglycemia rates increased with age, with 80-year-old patients experiencing 1.5 times the risk of 40-year-old patients. Based on published values for direct and indirect medical costs for mild hypoglycemia events, we determined that the economic impact in the US of mild hypoglycemic events is approximately $900 million per year, roughly equal to that of severe hypoglycemic events. One of the key limitations to our model is that it applies to the US population under standard medical care and not to clinical trials and does not include certain known risk factors such as rigorous exercise.

CONCLUSIONS

Understanding the benefit versus risk of glycemic control and hypoglycemia is fundamental to the successful management of patients with T2DM. Our validated hypoglycemia model is an important step in addressing this issue and may be helpful to researchers, clinicians, and payers to determine the patients who are at the highest risk for hypoglycemia, whether a patient is experiencing events at 'higher-than-expected' rates, and the corresponding economic burden.

The Community Diabetes Education (CoDE) program: cost-effectiveness and health outcomes

BACKGROUND

Limited evidence exists regarding the long-term effects of community health worker-led diabetes management programs on health outcomes and cost-effectiveness, particularly in low-income, ethnic minority populations.

PURPOSE

To examine the long-term cost-effectiveness and improvements in diabetes-related complications of a diabetes education and management intervention led by community health workers among uninsured Mexican Americans.

METHODS

Clinical data, changes in hemoglobin A1c over 12 months, and costs from an RCT of 180 uninsured Mexican Americans with type 2 diabetes conducted in 2006 were utilized for secondary analyses in 2012. Simulation modeling was used to estimate long-term cost and health outcomes using the validated Archimedes Model. The absolute differences for the incremental cost-effectiveness ratios and cumulative incidence of diabetes-related complications were derived by comparing intervention and control groups.

RESULTS

During a 20-year time horizon, participants who received the intervention would be expected to have significantly lower hemoglobin A1c levels (p<0.001), fewer foot ulcers (p<0.001), and a reduced number of foot amputations (p=0.005) in comparison with a control group receiving usual medical care. An incremental cost-effectiveness ratio of $355 per quality-adjusted life year gained was estimated for intervention participants during the same time period.

CONCLUSIONS

A simulated clinical trial suggests that a community health worker-led diabetes intervention is a cost-effective way to reduce diabetes-related complications for uninsured Mexican Americans during a 20-year horizon in comparison to usual medical care.

Validation of the IHE Cohort Model of Type 2 Diabetes and the impact of choice of macrovascular risk equations

BACKGROUND

Health-economic models of diabetes are complex since the disease is chronic, progressive and there are many diabetic complications. External validation of these models helps building trust and satisfies demands from decision makers. We evaluated the external validity of the IHE Cohort Model of Type 2 Diabetes; the impact of using alternative macrovascular risk equations; and compared the results to those from microsimulation models.

METHODS

The external validity of the model was analysed from 12 clinical trials and observational studies by comparing 167 predicted microvascular, macrovascular and mortality outcomes to the observed study outcomes. Concordance was examined using visual inspection of scatterplots and regression-based analysis, where an intercept of 0 and a slope of 1 indicate perfect concordance. Additional subgroup analyses were conducted on 'dependent' vs. 'independent' endpoints and microvascular vs. macrovascular vs. mortality endpoints.

RESULTS

Visual inspection indicates that the model predicts outcomes well. The UKPDS-OM1 equations showed almost perfect concordance with observed values (slope 0.996), whereas Swedish NDR (0.952) and UKPDS-OM2 (0.899) had a slight tendency to underestimate. The R2 values were uniformly high (>0.96). There were no major differences between 'dependent' and 'independent' outcomes, nor for microvascular and mortality outcomes. Macrovascular outcomes tended to be underestimated, most so for UKPDS-OM2 and least so for NDR risk equations.

CONCLUSIONS

External validation indicates that the IHE Cohort Model of Type 2 Diabetes has predictive accuracy in line with microsimulation models, indicating that the trade-off in accuracy using cohort simulation might not be that large. While the choice of risk equations was seen to matter, each were associated with generally reasonable results, indicating that the choice must reflect the specifics of the application. The largest variation was observed for macrovascular outcomes. There, NDR performed best for relatively recent and well-treated patients, while UKPDS-OM1 performed best for the older UKPDS cohort.

Modeling effects of SGLT-2 inhibitor dapagliflozin treatment versus standard diabetes therapy on cardiovascular and microvascular outcomes

AIMS

Dapagliflozin, a sodium-glucose cotransporter 2 (SGLT-2) inhibitor, has been shown to lower glycated hemoglobin (HbA1c), weight, blood pressure and serum uric acid in clinical trials. Plasma lipids were also evaluated as exploratory variables. The goal of this study was to estimate the long-term cardiovascular (CV) and microvascular outcomes of dapagliflozin added to the standard of care (SOC) versus SOC using simulation methodology.

METHODS

The Archimedes Model, a validated model of human physiology, diseases and healthcare systems, was used to model a type 2 diabetes mellitus (T2DM) population derived from National Health and Nutrition Examination Survey (NHANES) with HbA1c 7-10%, taking a single oral antidiabetic agent [metformin, sulfonylureas SU or thiazolidinedione (TZD)] at the beginning of the trial. A 20-year trial was simulated comparing dapagliflozin 10 mg, given in addition to SOC, with SOC alone. SOC was based on American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) 2012 guidelines and included diet, metformin, SU, TZD, dipeptidyl peptidase-4 (DPP-4), glucagon-like peptide-1 (GLP-1), and insulin therapies, with usage levels reflective of those in NHANES. Dapagliflozin effects were derived from phase 3 clinical trial results. End points included CV and microvascular outcomes.

RESULTS

Over a 20-year period, patients on dapagliflozin were projected to experience relative reductions in the incidence of myocardial infarction (MI), stroke, CV death, and all-cause death of 13.8, 9.1, 9.6 and 5.0%, respectively, and relative reductions in the incidence of end-stage renal disease (ESRD), foot amputation, and diabetic retinopathy of 18.7, 13.0 and 9.8%, respectively, when compared with SOC.

CONCLUSIONS

On the basis of simulation results, adding dapagliflozin to currently available treatment options is projected to further decrease the CV and microvascular complications associated with T2DM.

Questionnaire to assess relevance and credibility of modeling studies for informing health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report

The evaluation of the cost and health implications of agreeing to cover a new health technology is best accomplished using a model that mathematically combines inputs from various sources, together with assumptions about how these fit together and what might happen in reality. This need to make assumptions, the complexity of the resulting framework, the technical knowledge required, as well as funding by interested parties have led many decision makers to distrust the results of models. To assist stakeholders reviewing a model's report, questions pertaining to the credibility of a model were developed. Because credibility is insufficient, questions regarding relevance of the model results were also created. The questions are formulated such that they are readily answered and they are supplemented by helper questions that provide additional detail. Some responses indicate strongly that a model should not be used for decision making: these trigger a "fatal flaw" indicator. It is hoped that the use of this questionnaire, along with the three others in the series, will help disseminate what to look for in comparative effectiveness evidence, improve practices by researchers supplying these data, and ultimately facilitate their use by health care decision makers.

Preclinical PK/PD modeling and human efficacious dose projection for a glucokinase activator in the treatment of diabetes

Human Hexokinase IV, or glucokinase (GK), is a regulator of glucose concentrations in the body. It plays a key role in pancreatic insulin secretion as well as glucose biotransformation in the liver, making it a potentially viable target for treatment of Type 2 diabetes. Allosteric activators of GK have been shown to decrease blood glucose concentrations in both animals and humans. Here, the development of a mathematical model is presented that describes glucose modulation in an ob/ob mouse model via administration of a potent GK activator, with the goal of projecting a human efficacious dose and plasma exposure. The model accounts for the allosteric interaction between GK, the activator, and glucose using a modified Hill function. Based on model simulations using data from the ob/ob mouse and in vitro studies, human projections of glucose response to the GK activator are presented, along with dose and regimen predictions to maintain clinically significant decreases in blood glucose in a Type 2 diabetic patient. This effort serves as a basis to build a detailed mechanistic understanding of GK and its role as a therapeutic target for Type 2 diabetes, and it highlights the benefits of using such an approach in a drug discovery setting.

Impact of switching treatment from rosuvastatin to atorvastatin on rates of cardiovascular events

BACKGROUND

Now that generic atorvastatin has become available, a process of switching from rosuvastatin to atorvastatin may occur and could persist until the patent on branded rosuvastatin expires. It is important to understand the impact that such therapy may have on patients' cardiovascular (CV) health.

OBJECTIVES

This simulated study estimates the impact of switching patients treated with rosuvastatin to atorvastatin on rates of CV events over a 5-year period.

METHODS

A study of 50,038 virtual dyslipidemic patients aged 45 to 70 years was conducted using the Archimedes model. Virtual patients were created based on the profiles of patients in the National Health and Nutrition Examination Survey (NHANES). Statin treatment models were constructed based on data from published studies, including STELLAR, JUPITER, CARDS, ASCOT, and TNT. Patients were started on a dose of rosuvastatin based on their ATP III low-density lipoprotein cholesterol (LDL-C) goal and the distributions of statin use observed in US pharmacy claims data. Patients were monitored for 5 years, during which time they received regular visits with the opportunity to increase their dosage if they were above their LDL-C goal. In the experimental arm, patients were switched from rosuvastatin to atorvastatin at the first clinic visit 6 weeks after initiating rosuvastatin (using an atorvastatin dose twice the rosuvastatin milligram-dose). No switching occurred in the control arm, and patients were titrated as necessary per ATP III cholesterol management guidelines. The rate of first occurrence of a major adverse cardiovascular event (MACE; myocardial infarction, stroke, and/or cardiovascular-related death) over the 5-year period was estimated for each study arm.

RESULTS

After 5 years, in the atorvastatin-switched arm compared with continuing rosuvastatin, 4.8% fewer patients reached goal (87% vs 91%, respectively). The 5-year relative risk for MACE with switching was 1.109 (95% CI, 1.092-1.127), and the number needed to harm (NNH) to incur 1 additional MACE over 5 years was 262, favoring treatment with rosuvastatin. In diabetic individuals who were switched to atorvastatin, the 5-year relative risk for MACE was 1.121 (95% CI, 1.091-1.151), and the NNH over 5 years was 195, indicating greater risk in diabetic individuals. The results were insensitive to adherence rates and LDL-C goal values.

CONCLUSIONS

This study found that switching from rosuvastatin to atorvastatin led to fewer patients attaining LDL-C goal and a greater risk for MACE.

The 'Global Outcomes Score': a quality measure, based on health outcomes, that compares current care to a target level of care

The quality of health care is measured today using performance measures that calculate the percentage of people whose health conditions are managed according to specified processes or who meet specified treatment goals. This approach has several limitations. For instance, each measure looks at a particular process, risk factor, or biomarker one by one, and each uses sharp thresholds for defining "success" versus "failure." We describe a new measure of quality called the Global Outcomes Score (GO Score), which represents the proportion of adverse outcomes expected to be prevented in a population under current levels of care compared to a target level of care, such as 100 percent performance on certain clinical guidelines. We illustrate the use of the GO Score to measure blood pressure and cholesterol care in a longitudinal study of people at risk of atherosclerotic diseases, or hardening of the arteries. In that population the baseline GO Score was 40 percent, which indicates that the care being delivered was 40 percent as effective in preventing myocardial infarctions and strokes as our target level of care. The GO Score can be used to assess the potential effectiveness of different interventions such as prevention activities, tests, and treatments.