Chronic disease modeling and simulation software

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.

Simulation of long-term impact of intravitreal anti-VEGF therapy on patients with severe non-proliferative diabetic retinopathy

OBJECTIVE

A simulation model was constructed to assess long-term outcomes of proactively treating severe non-proliferative diabetic retinopathy (NPDR) with anti-vascular endothelial growth factor (anti-VEGF) therapy versus delaying treatment until PDR develops.

METHODS AND ANALYSIS

Simulated patients were generated using a retrospective real-world cohort of treatment-naive patients identified in an electronic medical records database (IBM Explorys) between 2011 and 2017. Impact of anti-VEGF treatment was derived from clinical trial data for intravitreal aflibercept (PANORAMA) and ranibizumab (RISE/RIDE), averaged by weighted US market share. Real-world risk of PDR progression was modelled using Cox multivariable regression. The Monte Carlo simulation model examined rates of progression to PDR and sustained blindness (visual acuity <20/200) for 2 million patients scaled to US NPDR disease prevalence. Simulated progression rates from severe NPDR to PDR over 5 years and blindness rates over 10 years were compared for delayed versus early-treatment patients.

RESULTS

Real-world data from 77 454 patients with mild-to-severe NPDR simulated 2 million NPDR patients, of which 86 680 had severe NPDR. Early treatment of severe NPDR with anti-VEGF therapy led to a 51.7% relative risk reduction in PDR events over 5 years (15 704 early vs 32 488 delayed), with a 19.4% absolute risk reduction (18.1% vs 37.5%). Sustained blindness rates at 10 years were 4.4% for delayed and 1.9% for early treatment of severe NPDR.

CONCLUSION

The model suggests treating severe NPDR early with anti-VEGF therapy, rather than delaying treatment until PDR develops, could significantly reduce PDR incidence over 5 years and sustained blindness over 10 years.

FSHR antagonists can trigger a PCOS-like state

Over the recent years, FSHR has become an important target for development of fertility regulating agents, as impairment of FSH-FSHR interaction can lead to subfertility or infertility. In our previous study, we identified a 9-mer peptide (FSHβ (89-97)) that exhibited FSHR antagonist activity. The histopathological and biochemical observations indicated, in addition to FSHR antagonism, a striking resemblance to a PCOS-like state. These observations led us to hypothesize that use of FSHR antagonists can trigger a PCOS-like state. In the present study, to validate this hypothesis, we performed qRT-PCR validation using ovarian tissue samples from our previous study. Expression of three genes known to be differentially expressed in PCOS was evaluated and found to be similar to the PCOS state. To further test the hypothesis, theoretical simulations were carried out by using the human menstrual cycle model available in the literature. Model simulations for FSHR antagonism were indicative of increased testosterone levels, increased ratio of luteinizing hormone/follicle stimulating hormone, and stockpiling of secondary follicles, which are typical characteristics of PCOS. The findings of this study will be relevant while reviewing the utility of FSHR antagonists for fertility regulation and reproductive medicine.Abbreviations: FSH: Follicle-stimulating hormone; FSHR: Follicle-stimulating hormone receptor; cAMP: Cyclic adenosine 3'5' monophosphate; PKA: Protein kinase A; PI3K: Phosphoinositide 3-kinase; PKB: protein kinase B; ERK1/2: Extracellular signal-regulated protein kinase 1/2; MAPK: Mitogen-activated protein kinases; T: testosterone; E2: estradiol; PCOS: Polycystic ovarian syndrome; LH: luteinizing hormone; Lhcgr: luteinizing hormone/choriogonadotropin receptor; CYP17A1: cytochrome P450 family 17 subfamily A member 1; Inhba: inhibin subunit beta A; qRT-PCR: Real-Time quantitative reverse transcription polymerase chain reaction; FSHβ: Follicle-stimulating hormone β subunit; Ct: Cycle threshold; Rn18s: Rattus norvegicus 18S ribosomal RNA.

Cost-Effectiveness of a Diabetes Self-Management Education and Support Intervention Led by Community Health Workers and Peer Leaders: Projections From the Racial and Ethnic Approaches to Community Health Detroit Trial

OBJECTIVE

To simulate the long-term cost-effectiveness of a peer leader (PL)-led diabetes self-management support (DSMS) program following a structured community health worker (CHW)-led diabetes self-management education (DSME) program in reducing risks of complications in people with type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

The trial randomized 222 Latino adults with T2D to 1) enhanced usual care (EUC); 2) a CHW-led, 6-month DSME program and 6 months of CHW-delivered monthly telephone outreach (CHW only); or 3) a CHW-led, 6-month DSME program and 12 months of PL-delivered weekly group sessions with telephone outreach to those unable to attend (CHW + PL). Empirical data from the trial and the validated Michigan Model for Diabetes were used to estimate cost and health outcomes over a 20-year time horizon from a health care sector perspective, discounting both costs and benefits at 3% annually. The primary outcome measure was the incremental cost-effectiveness ratio (ICER).

RESULTS

Over 20 years, the CHW + PL intervention had an ICER of $28,800 and $5,900 per quality-adjusted life-year (QALY) gained compared with the EUC and CHW-only interventions, respectively. The CHW-only intervention had an ICER of $430,600 per QALY gained compared with the EUC intervention. In sensitivity analyses, the results comparing the CHW + PL with EUC and CHW-only interventions were robust to changes in intervention effects and costs.

CONCLUSIONS

The CHW + PL-led DSME/DSMS intervention improved health and provided good value compared with the EUC intervention. The 6-month CHW-led DSME intervention without further postintervention CHW support was not cost effective in Latino adults with T2D.

Importance of Geospatial Heterogeneity in Chronic Disease Burden for Policy Planning in an Urban Setting Using a Case Study of Singapore

Chronic disease burdens continue to rise in highly dense urban environments where clustering of type II diabetes mellitus, acute myocardial infarction, stroke, or any combination of these three conditions is occurring. Many individuals suffering from these conditions will require longer-term care and access to clinics which specialize in managing their illness. With Singapore as a case study, we utilized census data in an agent-modeling approach at an individual level to estimate prevalence in 2020 and found high-risk clusters with >14,000 type II diabetes mellitus cases and 2000-2500 estimated stroke cases. For comorbidities, 10% of those with type II diabetes mellitus had a past acute myocardial infarction episode, while 6% had a past stroke. The western region of Singapore had the highest number of high-risk individuals at 173,000 with at least one chronic condition, followed by the east at 169,000 and the north with the least at 137,000. Such estimates can assist in healthcare resource planning, which requires these spatial distributions for evidence-based policymaking and to investigate why such heterogeneities exist. The methodologies presented can be utilized within any urban setting where census data exists.

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

BACKGROUND

There is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s' European populations.

OBJECTIVE

The objective of this study was to develop a risk engine with multiple risk equations using a recent patient cohort with type 2 diabetes mellitus reflective of the US population.

METHODS

A total of 17 risk equations for predicting diabetes-related microvascular and macrovascular events, hypoglycemia, mortality, and progression of diabetes risk factors were estimated using the data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (n = 10,251). Internal and external validation processes were used to assess performance of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine. One-way sensitivity analysis was conducted to examine the impact of risk factors on mortality at the population level.

RESULTS

The BRAVO risk engine added several risk factors including severe hypoglycemia and common US racial/ethnicity categories compared with the UKPDS risk engine. The BRAVO risk engine also modeled mortality escalation associated with intensive glycemic control (i.e., glycosylated hemoglobin < 6.5%). External validation showed a good prediction power on 28 endpoints observed from other clinical trials (slope = 1.071, R² = 0.86).

CONCLUSION

The BRAVO risk engine for the US diabetes cohort provides an alternative to the UKPDS risk engine. It can be applied to assist clinical and policy decision making such as cost-effective resource allocation in USA.

The 1-hour post-load glucose level is more effective than HbA1c for screening dysglycemia

AIM

To assess the performance of HbA1c and the 1-h plasma glucose (PG ≥ 155 mg/dl; 8.6 mmol/l) in identifying dysglycemia based on the oral glucose tolerance test (OGTT) from a real-world clinical care setting.

METHODS

This was a diagnostic test accuracy study. For this analysis, we tested the HbA1c diagnostic criteria advocated by the American Diabetes Association (ADA 5.7-6.4 %) and International Expert Committee (IEC 6.0-6.4 %) against conventional OGTT criteria. We also tested the utility of 1-h PG ≥ mg/dl; 8.6 mmol/l. Prediabetes was defined according to ADA-OGTT guidelines. Spearman correlation tests were used to determine the relationships between HbA1c, 1-h PG with fasting, 2-h PG and indices of insulin sensitivity and β-cell function. The levels of agreement between diagnostic methods were ascertained using Cohen's kappa coefficient (Κ). Receiver operating characteristic (ROC) curve was used to analyze the performance of the HbA1c and 1-h PG test in identifying prediabetes considering OGTT as reference diagnostic criteria. The diagnostic properties of different HbA1c thresholds were contrasted by determining sensitivity, specificity and likelihood ratios (LR).

RESULTS

Of the 212 high-risk individuals, 70 (33 %) were identified with prediabetes, and 1-h PG showed a stronger association with 2-h PG, insulin sensitivity index, and β-cell function than HbA1c (P < 0.05). Furthermore, the level of agreement between 1-h PG ≥ 155 mg/dl (8.6 mmol/l) and the OGTT (Κ[95 % CI]: 0.40[0.28-0.53]) diagnostic test was stronger than that of ADA-HbA1c criteria 0.1[0.03-0.16] and IEC criteria (0.17[0.04-0.30]). The ROC (AUC[95 % CI]) for HbA1c and 1-h PG were 0.65[0.57-0.73] and 0.79[0.72-0.85], respectively. Importantly, 1-h PG ≥ 155 mg/dl (8.6 mmol/l) showed good sensitivity (74.3 % [62.4-84.0]) and specificity 69.7 % [61.5-77.1]) with a LR of 2.45. The ability of 1-h PG to discriminate prediabetes was better than that of HbA1c (∆AUC: -0.14; Z value: 2.5683; P = 0.01022).

CONCLUSION

In a real-world clinical practice setting, the 1-h PG ≥ 155 mg/dl (8.6 mmol/l) is superior for detecting high-risk individuals compared with HbA1c. Furthermore, HbA1c is a less precise correlate of insulin sensitivity and β-cell function than the 1-h PG and correlates poorly with the 2-h PG during the OGTT.

The Michigan Model for Coronary Heart Disease in Type 2 Diabetes: Development and Validation

OBJECTIVES

The aim of this study was to develop and validate a computer simulation model for coronary heart disease (CHD) in type 2 diabetes mellitus (T2DM) that reflects current medical and surgical treatments.

RESEARCH DESIGN AND METHODS

We modified the structure of the CHD submodel in the Michigan Model for Diabetes to allow for revascularization procedures before and after first myocardial infarction, for repeat myocardial infarctions and repeat revascularization procedures, and for congestive heart failure. Transition probabilities that reflect the direct effects of medical and surgical therapies on outcomes were derived from the literature and calibrated to recently published population-based epidemiologic studies and randomized controlled clinical trials. Monte Carlo techniques were used to implement a discrete-state and discrete-time multistate microsimulation model. Performance of the model was assessed using internal and external validation. Simple regression analysis (simulated outcome=b(0)+b(1)×published outcome) was used to evaluate the validation results.

RESULTS

For the 21 outcomes in the six studies used for internal validation, R(2) was 0.99, and the slope of the regression line was 0.98. For the 16 outcomes in the five studies used for external validation, R(2) was 0.81, and the slope was 0.84.

CONCLUSIONS

Our new computer simulation model predicted the progression of CHD in patients with T2DM and will be incorporated into the Michigan Model for Diabetes to assess the cost-effectiveness of alternative strategies to prevent and treat T2DM.

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.

Agent-based modeling of noncommunicable diseases: a systematic review

We reviewed the use of agent-based modeling (ABM), a systems science method, in understanding noncommunicable diseases (NCDs) and their public health risk factors. We systematically reviewed studies in PubMed, ScienceDirect, and Web of Sciences published from January 2003 to July 2014. We retrieved 22 relevant articles; each had an observational or interventional design. Physical activity and diet were the most-studied outcomes. Often, single agent types were modeled, and the environment was usually irrelevant to the studied outcome. Predictive validation and sensitivity analyses were most used to validate models. Although increasingly used to study NCDs, ABM remains underutilized and, where used, is suboptimally reported in public health studies. Its use in studying NCDs will benefit from clarified best practices and improved rigor to establish its usefulness and facilitate replication, interpretation, and application.

Systems Epidemiology: What's in a Name?

Systems biology is an interdisciplinary effort to integrate molecular, cellular, tissue, organ, and organism levels of function into computational models that facilitate the identification of general principles. Systems medicine adds a disease focus. Systems epidemiology adds yet another level consisting of antecedents that might contribute to the disease process in populations. In etiologic and prevention research, systems-type thinking about multiple levels of causation will allow epidemiologists to identify contributors to disease at multiple levels as well as their interactions. In public health, systems epidemiology will contribute to the improvement of syndromic surveillance methods. We encourage the creation of computational simulation models that integrate information about disease etiology, pathogenetic data, and the expertise of investigators from different disciplines.

Validation of economic and health outcomes simulation model of type 2 diabetes mellitus (ECHO-T2DM)

OBJECTIVE

This study constructed the Economic and Health Outcomes Model for type 2 diabetes mellitus (ECHO-T2DM), a long-term stochastic microsimulation model, to predict the costs and health outcomes in patients with T2DM. Naturally, the usefulness of the model depends upon its predictive accuracy. The objective of this work is to present results of a formal validation exercise of ECHO-T2DM.

METHODS

The validity of ECHO-T2DM was assessed using criteria recommended by the International Society for Pharmacoeconomics and Outcomes Research/Society for Medical Decision Making (ISPOR/SMDM). Specifically, the results of a number of clinical trials were predicted and compared with observed study end-points using a scatterplot and regression approach. An F-test of the best-fitting regression was added to assess whether it differs statistically from the identity (45°) line defining perfect predictions. In addition to testing the full model using all of the validation study data, tests were also performed of microvascular, macrovascular, and survival outcomes separately. The validation tests were also performed separately by type of data (used vs not used to construct the model, economic simulations, and treatment effects).

RESULTS

The intercept and slope coefficients of the best-fitting regression line between the predicted outcomes and corresponding trial end-points in the main analysis were -0.0011 and 1.067, respectively, and the R(2) was 0.95. A formal F-test of no difference between the fitted line and the identity line could not be rejected (p = 0.16). The high R(2) confirms that the data points are closely (and linearly) associated with the fitted regression line. Additional analyses identified that disagreement was highest for macrovascular end-points, for which the intercept and slope coefficients were 0.0095 and 1.225, respectively. The R(2) was 0.95 and the estimated intercept and slope coefficients were 0.017 and 1.048, respectively, for mortality, and the F-test was narrowly rejected (p = 0.04). The sub-set of microvascular end-points showed some tendency to over-predict (the slope coefficient was 1.095), although concordance between predictions and observed values could not be rejected (p = 0.16).

LIMITATIONS

Important study limitations include: (1) data availability limited one to tests based on end-of-study outcomes rather than time-varying outcomes during the studies analyzed; (2) complex inclusion and exclusion criteria in two studies were difficult to replicate; (3) some of the studies were older and reflect outdated treatment patterns; and (4) the authors were unable to identify published data on resource use and costs of T2DM suitable for testing the validity of the economic calculations.

CONCLUSIONS

Using conventional methods, ECHO-T2DM simulated the treatment, progression, and patient outcomes observed in important clinical trials with an accuracy consistent with other well-accepted models. Macrovascular outcomes were over-predicted, which is common in health-economic models of diabetes (and may be related to a general over-prediction of event rates in the United Kingdom Prospective Diabetes Study [UKPDS] Outcomes Model). Work is underway in ECHO-T2DM to incorporate new risk equations to improve model prediction.

Computer modeling of diabetes and its complications: a report on the Fifth Mount Hood challenge meeting

OBJECTIVES

The Mount Hood Challenge meetings provide a forum for computer modelers of diabetes to discuss and compare models, to assess predictions against data from clinical trials and other studies, and to identify key future developments in the field. This article reports the proceedings of the Fifth Mount Hood Challenge in 2010.

METHODS

Eight modeling groups participated. Each group was given four modeling challenges to perform (in type 2 diabetes): to simulate a trial of a lipid-lowering intervention (The Atorvastatin Study for Prevention of Coronary Heart Disease Endpoints in Non-Insulin-Dependent Diabetes Mellitus [ASPEN]), to simulate a trial of a blood glucose-lowering intervention (Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation [ADVANCE]), to simulate a trial of a blood pressure-lowering intervention (Cardiovascular Risk in Diabetes [ACCORD]), and (optional) to simulate a second trial of blood glucose-lowering therapy (ACCORD). Model outcomes for each challenge were compared with the published findings of the respective trials.

RESULTS

The results of the models varied from each other and, in some cases, from the published trial data in important ways. In general, the models performed well in terms of predicting the relative benefit of interventions, but performed less well in terms of quantifying the absolute risk of complications in patients with type 2 diabetes. Methodological challenges were highlighted including matching trial end-point definitions, the importance of assumptions concerning the progression of risk factors over time, and accurately matching the patient characteristics from each trial.

CONCLUSIONS

The Fifth Mount Hood Challenge allowed modelers, through systematic comparison and validation exercises, to identify important differences between models, address key methodological challenges, and discuss avenues of research to improve future diabetes models.

Use of Secondary Data to Estimate Instantaneous Model Parameters of Diabetic Heart Disease: Lemonade Method

With the increasing burden of chronic diseases on the health care system, Markov-type models are becoming popular to predict the long-term outcomes of early intervention and to guide disease management. However, statisticians have not been actively involved in the development of these models. Typically, the models are developed by using secondary data analysis to find a single "best" study to estimate each transition in the model. However, due to the nature of secondary data analysis, there frequently are discrepancies between the theoretical model and the design of the studies being used. This paper illustrates a likelihood approach to correctly model the design of clinical studies under the conditions where 1) the theoretical model may include an instantaneous state of distinct interest to the researchers, and 2) the study design may be such that study data can not be used to estimate a single parameter in the theoretical model of interest. For example, a study may ignore intermediary stages of disease. Using our approach, not only can we accommodate the two conditions above, but more than one study may be used to estimate model parameters. In the spirit of "If life gives you lemon, make lemonade", we call this method "Lemonade Method". Simulation studies are carried out to evaluate the finite sample property of this method. In addition, the method is demonstrated through application to a model of heart disease in diabetes.