Archimedes: a trial-validated model of diabetes

A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes

This literature review had two objectives: to identify models for predicting the risk of coronary heart diseases in patients with diabetes (DM); and to assess model quality in terms of risk of bias (RoB) and applicability for the purpose of health technology assessment (HTA). We undertook a targeted review of journal articles published in English, Dutch, Chinese, or Spanish in 5 databases from 1st January 2016 to 18th December 2022, and searched three systematic reviews for the models published after 2012. We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess RoB, and used findings from Betts et al. 2019, which summarized recommendations and criticisms of HTA agencies on cardiovascular risk prediction models, to assess model applicability for the purpose of HTA. As a result, 71 % and 67 % models reporting C-index showed good discrimination abilities (C-index >= 0.7). Of the 26 model studies and 30 models identified, only one model study showed low RoB in all domains, and no model was fully applicable for HTA. Since the major cause of high RoB is inappropriate use of analysis method, we advise clinicians to carefully examine the model performance declared by model developers, and to trust a model if all PROBAST domains except analysis show low RoB and at least one validation study conducted in the same setting (e.g. country) is available. Moreover, since general model applicability is not informative for HTA, novel adapted tools may need to be developed.

Digital twins in medicine

Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.

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.

A simple modeling framework for prediction in the human glucose-insulin system

Forecasting blood glucose (BG) levels with routinely collected data is useful for glycemic management. BG dynamics are nonlinear, complex, and nonstationary, which can be represented by nonlinear models. However, the sparsity of routinely collected data creates parameter identifiability issues when high-fidelity complex models are used, thereby resulting in inaccurate forecasts. One can use models with reduced physiological fidelity for robust and accurate parameter estimation and forecasting with sparse data. For this purpose, we approximate the nonlinear dynamics of BG regulation by a linear stochastic differential equation: we develop a linear stochastic model, which can be specialized to different settings: type 2 diabetes mellitus (T2DM) and intensive care unit (ICU), with different choices of appropriate model functions. The model includes deterministic terms quantifying glucose removal from the bloodstream through the glycemic regulation system and representing the effect of nutrition and externally delivered insulin. The stochastic term encapsulates the BG oscillations. The model output is in the form of an expected value accompanied by a band around this value. The model parameters are estimated patient-specifically, leading to personalized models. The forecasts consist of values for BG mean and variation, quantifying possible high and low BG levels. Such predictions have potential use for glycemic management as part of control systems. We present experimental results on parameter estimation and forecasting in T2DM and ICU settings. We compare the model's predictive capability with two different nonlinear models built for T2DM and ICU contexts to have a sense of the level of prediction achieved by this model.

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.

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

Assessing the Effect of Incretin Hormones and Other Insulin Secretagogues on Pancreatic Beta-Cell Function: Review on Mathematical Modelling Approaches

Mathematical modelling in glucose metabolism has proven very useful for different reasons. Several models have allowed deeper understanding of the relevant physiological and pathophysiological aspects and promoted new experimental activity to reach increased knowledge of the biological and physiological systems of interest. Glucose metabolism modelling has also proven useful to identify the parameters with specific physiological meaning in single individuals, this being relevant for clinical applications in terms of precision diagnostics or therapy. Among those model-based physiological parameters, an important role resides in those for the assessment of different functional aspects of the pancreatic beta cell. This study focuses on the mathematical models of incretin hormones and other endogenous substances with known effects on insulin secretion and beta-cell function, mainly amino acids, non-esterified fatty acids, and glucagon. We found that there is a relatively large number of mathematical models for the effects on the beta cells of incretin hormones, both at the cellular/organ level or at the higher, whole-body level. In contrast, very few models were identified for the assessment of the effect of other insulin secretagogues. Given the opportunities offered by mathematical modelling, we believe that novel models in the investigated field are certainly advisable.

A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: The Ravansar county anthropometric cohort study

Anthropometry is a Greek word that consists of the two words “Anthropo” meaning human species and “metery” meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.

Decision models in type 2 diabetes mellitus: A systematic review

AIMS

To reduce the burden of type 2 diabetes (T2DM), the disease decision model plays a vital role in supporting decision-making. Currently, there is no comprehensive summary and assessment of the existing decision models for T2DM. The objective of this review is to provide an overview of the characteristics and capabilities of published decision models for T2DM. We also discuss which models are suitable for different study demands.

MATERIALS AND METHODS

Four databases (PubMed, Web of Science, Embase, and the Cochrane Library) were electronically searched for papers published from inception to August 2020. Search terms were: "Diabetes-Mellitus, Type 2", "cost-utility", "quality-of-life", and "decision model". Reference lists of the included studies were manually searched. Two reviewers independently screened the titles and abstracts following the inclusion and exclusion criteria. If there was insufficient information to include or exclude a study, then a full-text version was sought. The extracted information included basic information, study details, population characteristics, basic modeling methodologies, model structure, and data inputs for the included applications, model outcomes, model validation, and uncertainty.

RESULTS

Fourteen unique decision models for T2DM were identified. Markov chains and risk equations were utilized by four and three models, respectively. Three models utilized both. Except for the Archimedes model, all other models (n = 13) implemented an annual cycle length. The time horizon of most models was flexible. Fourteen models had differences in the division of health states. Ten models emphasized macrovascular and microvascular complications. Six models included adverse events. Majority of the models (n = 11) were patient-level simulation models. Eleven models simulated annual changes in risk factors (body mass index, glycemia, HbA1c, blood pressure (systolic and/or diastolic), and lipids (total cholesterol and/or high-density lipoprotein)). All models reported the main data sources used to develop health states of complications. Most models (n = 11) could deal with the uncertainty of models, which were described in varying levels of detail in the primary studies. Eleven studies reported that one or more validation checks were performed.

CONCLUSIONS

The existing decision models for T2DM are heterogeneous in terms of the level of detail in the classification of health states. Thus, more attention should be focused on balancing the desired level of complexity against the required level of transparency in the development of T2DM decision models.

Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin

Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. To this purpose, we propose a "digital twin" of patients modeling the human body as a whole and providing a panoramic view over individuals' conditions. Methods: We propose a general framework that composes advanced artificial intelligence (AI) approaches and integrates mathematical modeling in order to provide a panoramic view over current and future pathophysiological conditions. Our modular architecture is based on a graph neural network (GNN) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GAN) providing a proof of concept of transcriptomic integrability. Results: We tested our digital twin model on two simulated clinical case studies combining information at organ, tissue, and cellular level. We provided a panoramic overview over current and future patient's conditions by monitoring and forecasting clinically relevant endpoints representing the evolution of patient's vital parameters using the GNN model. We showed how to use the GAN to generate multi-tissue expression data for blood and lung to find associations between cytokines conditioned on the expression of genes in the renin-angiotensin pathway. Our approach was to detect inflammatory cytokines, which are known to have effects on blood pressure and have previously been associated with SARS-CoV-2 infection (e.g., CXCR6, XCL1, and others). Significance: The graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modeling with AI. We believe that this work represents a step forward toward next-generation devices for precision and predictive medicine.

Mathematical Modeling for the Physiological and Clinical Investigation of Glucose Homeostasis and Diabetes

Mathematical modeling in the field of glucose metabolism has a longstanding tradition. The use of models is motivated by several reasons. Models have been used for calculating parameters of physiological interest from experimental data indirectly, to provide an unambiguous quantitative representation of pathophysiological mechanisms, to determine indices of clinical usefulness from simple experimental tests. With the growing societal impact of type 2 diabetes, which involves the disturbance of the glucose homeostasis system, development and use of models in this area have increased. Following the approaches of physiological and clinical investigation, the focus of the models has spanned from representations of whole body processes to those of cells, i.e., from in vivo to in vitro research. Model-based approaches for linking in vivo to in vitro research have been proposed, as well as multiscale models merging the two areas. The success and impact of models has been variable. Two kinds of models have received remarkable interest: those widely used in clinical applications, e.g., for the assessment of insulin sensitivity and β-cell function and some models representing specific aspects of the glucose homeostasis system, which have become iconic for their efficacy in describing clearly and compactly key physiological processes, such as insulin secretion from the pancreatic β cells. Models are inevitably simplified and approximate representations of a physiological system. Key to their success is an appropriate balance between adherence to reality, comprehensibility, interpretative value and practical usefulness. This has been achieved with a variety of approaches. Although many models concerning the glucose homeostasis system have been proposed, research in this area still needs to address numerous issues and tackle new opportunities. The mathematical representation of the glucose homeostasis processes is only partial, also because some mechanisms are still only partially understood. For in vitro research, mathematical models still need to develop their potential. This review illustrates the problems, approaches and contribution of mathematical modeling to the physiological and clinical investigation of glucose homeostasis and diabetes, focusing on the most relevant and stimulating models.

Active learning for efficiently training emulators of computationally expensive mathematical models

An emulator is a fast-to-evaluate statistical approximation of a detailed mathematical model (simulator). When used in lieu of simulators, emulators can expedite tasks that require many repeated evaluations, such as sensitivity analyses, policy optimization, model calibration, and value-of-information analyses. Emulators are developed using the output of simulators at specific input values (design points). Developing an emulator that closely approximates the simulator can require many design points, which becomes computationally expensive. We describe a self-terminating active learning algorithm to efficiently develop emulators tailored to a specific emulation task, and compare it with algorithms that optimize geometric criteria (random latin hypercube sampling and maximum projection designs) and other active learning algorithms (treed Gaussian Processes that optimize typical active learning criteria). We compared the algorithms' root mean square error (RMSE) and maximum absolute deviation from the simulator (MAX) for seven benchmark functions and in a prostate cancer screening model. In the empirical analyses, in simulators with greatly varying smoothness over the input domain, active learning algorithms resulted in emulators with smaller RMSE and MAX for the same number of design points. In all other cases, all algorithms performed comparably. The proposed algorithm attained satisfactory performance in all analyses, had smaller variability than the treed Gaussian Processes, and, on average, had similar or better performance as the treed Gaussian Processes in six out of seven benchmark functions and in the prostate cancer model.

Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response During the First 24 Hours of Sepsis

To develop and verify a digital twin model of critically ill patient using the causal artificial intelligence approach to predict the response to specific treatment during the first 24 hours of sepsis.

DESIGN

Directed acyclic graphs were used to define explicitly the causal relationship among organ systems and specific treatments used. A hybrid approach of agent-based modeling, discrete-event simulation, and Bayesian network was used to simulate treatment effect across multiple stages and interactions of major organ systems (cardiovascular, neurologic, renal, respiratory, gastrointestinal, inflammatory, and hematology). Organ systems were visualized using relevant clinical markers. The application was iteratively revised and debugged by clinical experts and engineers. Agreement statistics was used to test the performance of the model by comparing the observed patient response versus the expected response (primary and secondary) predicted by digital twin.

SETTING

Medical ICU of a large quaternary- care academic medical center in the United States.

PATIENTS OR SUBJECTS

Adult (> 18 year yr old), medical ICU patients were included in the study.

INTERVENTIONS

No additional interventions were made beyond the standard of care for this study.

MEASUREMENTS AND MAIN RESULTS

During the verification phase, model performance was prospectively tested on 145 observations in a convenience sample of 29 patients. Median age was 60 years (54-66 d) with a median Sequential Organ Failure Assessment score of 9.5 (interquartile range, 5.0-14.0). The most common source of sepsis was pneumonia, followed by hepatobiliary. The observations were made during the first 24 hours of the ICU admission with one-step interventions, comparing the output in the digital twin with the real patient response. The agreement between the observed versus and the expected response ranged from fair (kappa coefficient of 0.41) for primary response to good (kappa coefficient of 0.65) for secondary response to the intervention. The most common error detected was coding error in 50 observations (35%), followed by expert rule error in 29 observations (20%) and timing error in seven observations (5%).

CONCLUSIONS

We confirmed the feasibility of development and prospective testing of causal artificial intelligence model to predict the response to treatment in early stages of critical illness. The availability of qualitative and quantitative data and a relatively short turnaround time makes the ICU an ideal environment for development and testing of digital twin patient models. An accurate digital twin model will allow the effect of an intervention to be tested in a virtual environment prior to use on real patients.

Investigating the Role of Childhood Adiposity in the Development of Adult Type 2 Diabetes in a 64-year Follow-up Cohort: An Application of the Parametric G-formula Within an Agent-based Simulation Study

BACKGROUND

The contribution of childhood obesity to adult type 2 diabetes (T2DM), not through adult adiposity, as well as the causal pathways through which childhood obesity increases adult T2DM risk are not well understood. This study investigated the contribution of childhood obesity to incident T2DM including pathways not through adult adiposity, and explored whether race modified this contribution.

METHODS

We used data from the Virtual Los Angeles Cohort, an agent-based longitudinal birth cohort composed of 98,230 simulated individuals born in 2009 and followed until age 65 years. We applied the parametric mediational g-formula to the causal mediation analysis investigating the impact of childhood obesity on the development of adult T2DM.

RESULTS

The marginal adjusted odds ratio (aOR) for the total effect of childhood obesity on adult T2DM was 1.37 (95% CI = 1.32, 1.46). Nearly all the effect of childhood obesity on adult T2DM was mostly attributable to pathways other than through adult obesity; the aOR for the pure direct effect was 1.36 (95% CI = 1.31, 1.41). In all racial subpopulations, a similar 3% of the total effect of childhood obesity on adult T2DM was attributable to its effect on adult obesity.

CONCLUSIONS

Childhood obesity remains a risk factor for adult T2DM separate from its effects on adult obesity. This study emphasizes the potential benefits of early interventions and illustrates that agent-based simulation models could serve as virtual laboratories for exploring mechanisms in obesity research.

Artificial intelligence and computer simulation models in critical illness

Widespread implementation of electronic health records has led to the increased use of artificial intelligence (AI) and computer modeling in clinical medicine. The early recognition and treatment of critical illness are central to good outcomes but are made difficult by, among other things, the complexity of the environment and the often non-specific nature of the clinical presentation. Increasingly, AI applications are being proposed as decision supports for busy or distracted clinicians, to address this challenge. Data driven "associative" AI models are built from retrospective data registries with missing data and imprecise timing. Associative AI models lack transparency, often ignore causal mechanisms, and, while potentially useful in improved prognostication, have thus far had limited clinical applicability. To be clinically useful, AI tools need to provide bedside clinicians with actionable knowledge. Explicitly addressing causal mechanisms not only increases validity and replicability of the model, but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.

Establishment and Validation of a Risk Prediction Model for Early Diabetic Kidney Disease Based on a Systematic Review and Meta-Analysis of 20 Cohorts

BACKGROUND

Identifying patients at high risk of diabetic kidney disease (DKD) helps improve clinical outcome.

PURPOSE

To establish a model for predicting DKD.

DATA SOURCES

The derivation cohort was from a meta-analysis. The validation cohort was from a Chinese cohort.

STUDY SELECTION

Cohort studies that reported risk factors of DKD with their corresponding risk ratios (RRs) in patients with type 2 diabetes were selected. All patients had estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m² and urinary albumin-to-creatinine ratio (UACR) <30 mg/g at baseline.

DATA EXTRACTION

Risk factors and their corresponding RRs were extracted. Only risk factors with statistical significance were included in our DKD risk prediction model.

DATA SYNTHESIS

Twenty cohorts including 41,271 patients with type 2 diabetes were included in our meta-analysis. Age, BMI, smoking, diabetic retinopathy, hemoglobin A_1c, systolic blood pressure, HDL cholesterol, triglycerides, UACR, and eGFR were statistically significant. All these risk factors were included in the model except eGFR because of the significant heterogeneity among studies. All risk factors were scored according to their weightings, and the highest score was 37.0. The model was validated in an external cohort with a median follow-up of 2.9 years. A cutoff value of 16 was selected with a sensitivity of 0.847 and a specificity of 0.677.

LIMITATIONS

There was huge heterogeneity among studies involving eGFR. More evidence is needed to power it as a risk factor of DKD.

CONCLUSIONS

The DKD risk prediction model consisting of nine risk factors established in this study is a simple tool for detecting patients at high risk of DKD.

Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study

With the development of data mining, machine learning offers opportunities to improve discrimination by analyzing complex interactions among massive variables. To test the ability of machine learning algorithms for predicting risk of type 2 diabetes mellitus (T2DM) in a rural Chinese population, we focus on a total of 36,652 eligible participants from the Henan Rural Cohort Study. Risk assessment models for T2DM were developed using six machine learning algorithms, including logistic regression (LR), classification and regression tree (CART), artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM). The model performance was measured in an area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value and area under precision recall curve. The importance of variables was identified based on each classifier and the shapley additive explanations approach. Using all available variables, all models for predicting risk of T2DM demonstrated strong predictive performance, with AUCs ranging between 0.811 and 0.872 using laboratory data and from 0.767 to 0.817 without laboratory data. Among them, the GBM model performed best (AUC: 0.872 with laboratory data and 0.817 without laboratory data). Performance of models plateaued when introduced 30 variables to each model except CART model. Among the top-10 variables across all methods were sweet flavor, urine glucose, age, heart rate, creatinine, waist circumference, uric acid, pulse pressure, insulin, and hypertension. New important risk factors (urinary indicators, sweet flavor) were not found in previous risk prediction methods, but determined by machine learning in our study. Through the results, machine learning methods showed competence in predicting risk of T2DM, leading to greater insights on disease risk factors with no priori assumption of causality.

Cost-effectiveness of Initiating an Insulin Pump in T1D Adults Using Continuous Glucose Monitoring Compared with Multiple Daily Insulin Injections: The DIAMOND Randomized Trial

BACKGROUND

The economic impact of both continuous glucose monitoring (CGM) and insulin pumps (continuous subcutaneous insulin infusion [CSII]) in type 1 diabetes (T1D) have been evaluated separately. However, the cost-effectiveness of adding CSII to existing CGM users has not yet been assessed.

OBJECTIVE

The aim of this study was to evaluate the societal cost-effectiveness of CSII versus continuing multiple daily injections (MDI) in adults with T1D already using CGM.

METHODS

In the second phase of the DIAMOND trial, 75 adults using CGM were randomized to either CGM+CSII or CGM+MDI (control) and surveyed at baseline and 28 weeks. We performed within-trial and lifetime cost-effectiveness analyses (CEAs) and estimated lifetime costs and quality-adjusted life-years (QALYs) via a modified Sheffield T1D model.

RESULTS

Within the trial, the CGM+CSII group had a significant reduction in quality of life from baseline (-0.02 ± 0.05 difference in difference [DiD]) compared with controls. Total per-person 28-week costs were $8,272 (CGM+CSII) versus $5,623 (CGM+MDI); the difference in costs was primarily attributable to pump use ($2,644). Pump users reduced insulin intake (-12.8 units DiD) but increased the use of daily number of test strips (+1.2 DiD). Pump users also increased time with glucose in range of 70 to 180 mg/dL but had a higher HbA1c (+0.13 DiD) and more nonsevere hypoglycemic events. In the lifetime CEA, CGM+CSII would increase total costs by $112,045 DiD, decrease QALYs by 0.71, and decrease life expectancy by 0.48 years.

CONCLUSIONS

Based on this single trial, initiating an insulin pump in adults with T1D already using CGM was associated with higher costs and reduced quality of life. Additional evidence regarding the clinical effects of adopting combinations of new technologies from trials and real-world populations is needed to confirm these findings.

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.

Prognostic tools for cardiovascular disease in patients with type 2 diabetes: A systematic review and meta-analysis of C-statistics

BACKGROUND

Diabetes is associated with an increased risk for cardiovascular diseases (CVD). Risk prediction models are tools widely used to identify individuals at particularly high-risk of adverse events. Many CVD risk prediction models have been developed but their accuracy and consistency vary.

OBJECTIVE

This study reviews the literature on available CVD risk prediction models specifically developed or validated in patients with diabetes and performs a meta-analysis of C-statistics to assess and compare their predictive performance.

METHODS

The online databases and manual reference checks of all identified relevant publications were searched.

RESULTS

Fifteen CVD prediction models developed for patients with diabetes and 11 models developed in a general population but later validated in diabetes patients were identified. Meta-analysis of C-statistics showed an overall pooled C-statistic of 0.67 and 0.64 for validated models developed in diabetes patients and in general populations respectively. This small difference in the C-statistic suggests that CVD risk prediction for diabetes patients depends little on the population the model was developed in (p = 0.068).

CONCLUSIONS

The discriminative ability of diabetes-specific CVD prediction models were modest. Improvements in the predictive ability of these models are required to understand both short and long-term risk before implementation into clinical practice.

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.

Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables

BACKGROUND

The present study aims to identify the patients at risk of type 2 diabetes (T2D). There is a body of literature that uses machine learning classification algorithms to predict development of T2D among patients. The current study compares the performance of these classification algorithms to identify patients who are at risk of developing T2D in short, medium and long terms. In addition, the list of predictor variables important for prediction for T2D progression is provided.

METHODS

This study uses 10,911 records generated in 36 clinics from the 15th of November 2008-15th of November 2016. Syntactic minority oversampling and random under sampling were used to create a balanced dataset. The performance of Neural Networks, Support Vector Machines, Decision Tress and Logistic Regression to identify patients developing T2D in short, medium and long terms was compared. The measures were Area Under Curve, Sensitivity, Specificity, Matthew correlation coefficient and Mean Calibration Error. Through importance analysis and information fusion techniques the predictors of developing T2D were identified for short, medium and long-term risk analysis.

RESULTS

The findings show that the performance of analytics techniques depends on both period and purpose of prediction whether the prediction is to identify people who will not develop T2D or to determine at risk patients. Oversampling as opposed to under sampling improved performance. 16 predictors and their importance to determine patients at risk of T2D in short, medium and long terms were identified.

CONCLUSIONS

This study provides guidelines for an automated system to prompt patients for screening. Several predictors are reportable by patients, others can be examined by physicians or ordered for further lab examination, which offers a potential reduction of the burden placed upon the clinical settings.

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.

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification

INTRODUCTION

An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of the acute coronary syndrome (ACS) was developed and used in an experimental study.

METHODS

A combination of data, text, process mining techniques, and machine learning approaches for the analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for the simulation of patient flow was proposed. The performed analysis of EHRs for ACS patients enabled identification of several classes of clinical pathways (CPs) which were used to implement a more realistic simulation of the patient flow. The developed solution was implemented using Python libraries (SimPy, SciPy, and others).

RESULTS

The proposed approach enables more a realistic and detailed simulation of the patient flow within a group of related departments. An experimental study shows an improved simulation of patient length of stay for ACS patient flow obtained from EHRs in Almazov National Medical Research Centre in Saint Petersburg, Russia.

CONCLUSION

The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for the implementation of a simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others.

Cohort profile: The MULTI sTUdy Diabetes rEsearch (MULTITUDE) consortium

PURPOSE

Globally, the age-standardised prevalence of type 2 diabetes mellitus (T2DM) has nearly doubled from 1980 to 2014, rising from 4.7% to 8.5% with an estimated 422 million adults living with the chronic disease. The MULTI sTUdy Diabetes rEsearch (MULTITUDE) consortium was recently established to harmonise data from 17 independent cohort studies and clinical trials and to facilitate a better understanding of the determinants, risk factors and outcomes associated with T2DM.

PARTICIPANTS

Participants range in age from 3 to 88 years at baseline, including both individuals with and without T2DM. MULTITUDE is an individual-level pooled database of demographics, comorbidities, relevant medications, clinical laboratory values, cardiac health measures, and T2DM-associated events and outcomes across 45 US states and the District of Columbia.

FINDINGS TO DATE

Among the 135 156 ongoing participants included in the consortium, almost 25% (33 421) were diagnosed with T2DM at baseline. The average age of the participants was 54.3, while the average age of participants with diabetes was 64.2. Men (55.3%) and women (44.6%) were almost equally represented across the consortium. Non-whites accounted for 31.6% of the total participants and 40% of those diagnosed with T2DM. Fewer individuals with diabetes reported being regular smokers than their non-diabetic counterparts (40.3% vs 47.4%). Over 85% of those with diabetes were reported as either overweight or obese at baseline, compared with 60.7% of those without T2DM. We observed differences in all-cause mortality, overall and by T2DM status, between cohorts.

FUTURE PLANS

Given the wide variation in demographics and all-cause mortality in the cohorts, MULTITUDE consortium will be a unique resource for conducting research to determine: differences in the incidence and progression of T2DM; sequence of events or biomarkers prior to T2DM diagnosis; disease progression from T2DM to disease-related outcomes, complications and premature mortality; and to assess race/ethnicity differences in the above associations.

Comparison of clinical pharmacy specialists and primary care physicians in treatment of type 2 diabetes mellitus in rural Veterans Affairs facilities

PURPOSE

Results of a study to compare the impact of clinical pharmacy specialist (CPS) interventions with primary care physician (PCP) interventions in veterans with type 2 diabetes mellitus (T2D) in a rural setting are presented.

METHODS

A retrospective analysis was performed examining veterans diagnosed with T2D with a glycosylated hemoglobin (HbA_1c) of ≥8% receiving treatment at a rural community-based outpatient clinic associated with the Memphis Veterans Affairs Medical Center. Propensity score matching was used to create a 1:1 cohort of patients managed by physicians or clinical pharmacy specialists. Patients were evaluated as their own control and as compared cohorts. The primary outcome was the difference in HbA_1c. Secondary outcomes included changes in total cholesterol, low-density lipoprotein cholesterol, triglycerides, and body mass index.

RESULTS

Data were collected from 124 patients (n = 62 CPS patients, n = 62 PCP patients). Baseline HbA_1c in the CPS and PCP groups were 10.2% ± 1.9% and 9.6% ± 1.6%, respectively. Postintervention HbA_1c in the CPS cohort was 7.5% ± 1.1% (range, 6-11.7%), indicating an absolute reduction of 2.7% (p < 0.001). Postintervention HbA_1c in the PCP cohort was 8.5% ± 1.5% (range, 5.4-12.6%), resulting in an absolute reduction of 1.1% (p < 0.001). The CPS intervention resulted in a greater mean HbA_1c absolute reduction of 1.6 percentage points compared to physician intervention (p < 0.001).

CONCLUSION

Compared with physician intervention, clinical pharmacy intervention in the treatment of T2D led to a greater mean HbA_1c reduction in patients receiving care through VA facilities in rural settings.

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.

Systematic review on the cost-effectiveness of self-management education programme for type 2 diabetes mellitus

OBJECTIVES

A review of cost-effectiveness studies on self-management education programmes for Type 2 diabetes mellitus.

METHODS

Cochrane, PubMed and PsycINFO databases were searched for papers published from January 2003 through September 2015. Further hand searching using the reference lists of included papers was carried out.

RESULTS

In total, 777 papers were identified and 12 papers were finally included. We found eight programmes whose effectiveness analyses were based on randomised controlled trials and whose costs were comprehensively estimated from the stated perspective. Among these eight, four studies showed a cost per unit reduction in clinical risk factors (HbA1c or BMI) of US$491 to US$7723 or cost per glycaemic symptom day avoided of US$39. In three studies the cost per QALY gained, as estimated from a life-time model, was less than US$50,000. However, one study found the programme was not cost-effective despite a gain in QALYs at the one-year follow up.

CONCLUSION

A small number of cost-effectiveness studies were identified with only eight of sufficiently good quality. The cost of a self-management education programme achieving reduction in clinical risk factors seems to be modest and is likely to be cost-effective in the long-term.

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.

Estimated Cost-Effectiveness, Cost Benefit, and Risk Reduction Associated with an Endocrinologist-Pharmacist Diabetes Intense Medical Management “Tune-Up” Clinic

BACKGROUND

In 2012 U.S. diabetes costs were estimated to be $245 billion, with $176 billion related to direct diabetes treatment and associated complications. Although a few studies have reported positive glycemic and economic benefits for diabetes patients treated under primary care physician (PCP)-pharmacist collaborative practice models, no studies have evaluated the cost-effectiveness of an endocrinologist-pharmacist collaborative practice model treating complex diabetes patients versus usual PCP care for similar patients.

OBJECTIVE

To estimate the cost-effectiveness and cost benefit of a collaborative endocrinologist-pharmacist Diabetes Intense Medical Management (DIMM) "Tune-Up" clinic for complex diabetes patients versus usual PCP care from 3 perspectives (clinic, health system, payer) and time frames.

METHODS

Data from a retrospective cohort study of adult patients with type 2 diabetes mellitus (T2DM) and glycosylated hemoglobin A1c (A1c) ≥ 8% who were referred to the DIMM clinic at the Veterans Affairs San Diego Health System were used for cost analyses against a comparator group of PCP patients meeting the same criteria. The DIMM clinic took more time with patients, compared with usual PCP visits. It provided personalized care in three 60-minute visits over 6 months, combining medication therapy management with patient-specific diabetes education, to achieve A1c treatment goals before discharge back to the PCP. Data for DIMM versus PCP patients were used to evaluate cost-effectiveness and cost benefit. Analyses included incremental cost-effectiveness ratios (ICERs) at 6 months, 3-year estimated total medical costs avoided and return on investment (ROI), absolute risk reduction of complications, resultant medical costs, and quality-adjusted life-years (QALYs) over 10 years.

RESULTS

Base case ICER results indicated that from the clinic perspective, the DIMM clinic costs $21 per additional percentage point of A1c improvement and $115-$164 per additional patient at target A1c goal level compared with the PCP group. From the health system perspective, medical cost avoidance due to improved A1c was $8,793 per DIMM patient versus $3,506 per PCP patient (P = 0.009), resulting in an ROI of $9.01 per dollar spent. From the payer perspective, DIMM patients had estimated lower total medical costs, a greater number of QALYs gained, and appreciable risk reductions for diabetes-related complications over 2-, 5- and 10-year time frames, indicating that the DIMM clinic was dominant. Sensitivity analyses indicated results were robust, and overall conclusions did not change appreciably when key parameters (including DIMM clinic effectiveness and cost) were varied within plausible ranges.

CONCLUSIONS

The DIMM clinic endocrinologist-pharmacist collaborative practice model, in which the pharmacist spent more time providing personalized care, improved glycemic control at a minimal cost per additional A1c benefit gained and produced greater cost avoidance, appreciable ROI, reduction in long-term complication risk, and lower cost for a greater gain in QALYs. Overall, the DIMM clinic represents an advanced pharmacy practice model with proven clinical and economic benefits from multiple perspectives for patients with T2DM and high medication and comorbidity complexity.

DISCLOSURES

No outside funding supported this study. The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Preliminary versions of the study data were presented in abstract form at the American Pharmacists Association Annual Meeting & Exposition; March 27, 2015; San Diego, California, and the Academy of Managed Care Pharmacy Annual Meeting; April 21, 2016; San Francisco, California. Study concept and design were contributed by Hirsch, Bounthavong, and Edelman, along with Morello and Morreale. Arjmand, Ourth, Ha, Cadiz, and Zimmerman collected the data. Data interpretation was performed by Ha, Morreale, and Morello, along with Cadiz, Ourth, and Hirsch. The manuscript was written primarily by Hirsch and Zimmerman, along with Arjamand, Ourth, and Morello, and was revised by Hirsch and Cadiz, along with Bounthavong, Ha, Morreale, and Morello.

How Consistent is the Relationship between Improved Glucose Control and Modelled Health Outcomes for People with Type 2 Diabetes Mellitus? a Systematic Review

BACKGROUND

There are an increasing number of studies using simulation models to conduct cost-effectiveness analyses for type 2 diabetes mellitus.

OBJECTIVE

To evaluate the relationship between improvements in glycosylated haemoglobin (HbA_1c) and simulated health outcomes in type 2 diabetes cost-effectiveness studies.

METHODS

A systematic review was conducted on MEDLINE and EMBASE to collect cost-effectiveness studies using type 2 diabetes simulation models that reported modelled health outcomes of blood glucose-related interventions in terms of quality-adjusted life-years (QALYs) or life expectancy (LE). The data extracted included information used to characterise the study cohort, the intervention's treatment effects on risk factors and model outcomes. Linear regressions were used to test the relationship between the difference in HbA_1c (∆HbA_1c) and incremental QALYs (∆QALYs) or LE (∆LE) of intervention and control groups. The ratio between the ∆QALYs and ∆LE was calculated and a scatterplot between the ratio and ∆HbA_1c was used to explore the relationship between these two.

RESULTS

Seventy-six studies were included in this research, contributing to 124 pair of comparators. The pooled regressions indicated that the marginal effect of a 1% HbA_1c decrease in intervention resulted in an increase in life-time QALYs and LE of 0.371 (95% confidence interval 0.286-0.456) and 0.642 (95% CI 0.494-0.790), respectively. No evidence of heterogeneity between models was found. An inverse exponential relationship was found and fitted between the ratio (∆QALY/∆LE) and ∆HbA_1c.

CONCLUSION

There is a consistent relationship between ∆HbA_1c and ∆QALYs or ∆LE in cost-effectiveness analyses using type 2 diabetes simulation models. This relationship can be used as a diagnostic tool for decision makers.

Cost-effectiveness of diabetes screening initiated through a dental visit

OBJECTIVE

To analyse the cost-effectiveness of a screening programme and follow-up interventions for persons with dysglycemia who are identified during a dental visit.

METHODS

This study is a secondary analysis utilizing data from two relevant publications. Those studies identified persons with dysglycemia who were seen in a dental school clinic for routine dental care and determined compliance with a recommendation to seek medical care. The response site was 59.4%. The Archimedes disease simulation model was utilized to simulate the effect of a weight loss programme for identified subjects on several outcomes.

RESULTS

Two scenarios for weight loss programmes were considered: a 10% permanent loss in body weight and a 10% loss that decays over time. Both diabetes and prediabetes were analysed. The decay path costs $21 243 per quality adjusted life year (QALY) with 3 years required to achieve the weight reduction. This cost decreases to $6655 if only 1 year is needed to achieve the weight goal. Without decay, the cost per QALY is $15 873 with 20 years of intervention, vs $647 per QALY with 10 years of intervention. For individuals with type 2 diabetes mellitus, the cost per QALY is $48 604 to $56 207 depending on adherence. With the addition of oral medication (a sulfonylurea), the cost is three times higher.

CONCLUSIONS

Under the conditions described here, identification of persons with dysglycemia in the dental office for initiating prediabetic care is a cost-effective means of identifying and treating affected individuals.

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.

Clarifying the debate on population-based screening for breast cancer with mammography: A systematic review of randomized controlled trials on mammography with Bayesian meta-analysis and causal model

BACKGROUND

The recent controversy about using mammography to screen for breast cancer based on randomized controlled trials over 3 decades in Western countries has not only eclipsed the paradigm of evidence-based medicine, but also puts health decision-makers in countries where breast cancer screening is still being considered in a dilemma to adopt or abandon such a well-established screening modality.

METHODS

We reanalyzed the empirical data from the Health Insurance Plan trial in 1963 to the UK age trial in 1991 and their follow-up data published until 2015. We first performed Bayesian conjugated meta-analyses on the heterogeneity of attendance rate, sensitivity, and over-detection and their impacts on advanced stage breast cancer and death from breast cancer across trials using Bayesian Poisson fixed- and random-effect regression model. Bayesian meta-analysis of causal model was then developed to assess a cascade of causal relationships regarding the impact of both attendance and sensitivity on 2 main outcomes.

RESULTS

The causes of heterogeneity responsible for the disparities across the trials were clearly manifested in 3 components. The attendance rate ranged from 61.3% to 90.4%. The sensitivity estimates show substantial variation from 57.26% to 87.97% but improved with time from 64% in 1963 to 82% in 1980 when Bayesian conjugated meta-analysis was conducted in chronological order. The percentage of over-detection shows a wide range from 0% to 28%, adjusting for long lead-time. The impacts of the attendance rate and sensitivity on the 2 main outcomes were statistically significant. Causal inference made by linking these causal relationships with emphasis on the heterogeneity of the attendance rate and sensitivity accounted for the variation in the reduction of advanced breast cancer (none-30%) and of mortality (none-31%). We estimated a 33% (95% CI: 24-42%) and 13% (95% CI: 6-20%) breast cancer mortality reduction for the best scenario (90% attendance rate and 95% sensitivity) and the poor scenario (30% attendance rate and 55% sensitivity), respectively.

CONCLUSION

Elucidating the scenarios from high to low performance and learning from the experiences of these trials helps screening policy-makers contemplate on how to avoid errors made in ineffective studies and emulate the effective studies to save women lives.

Cost-effectiveness of a risk-based secondary screening programme of type 2 diabetes

OBJECTIVE

The objective of this study was to develop a long-term economic model for type 2 diabetes to describe the entire spectrum of the disease over a wide range of healthcare programmes. The model evaluates a public health, risk-based screening programme in a country specific setting.

METHODS

The lifespan of persons and important phases of the disease and related interventions are recorded in a Markov model, which first simulates the effect of screening, then replicates important complications of diabetes, follows the progression of individuals through physiological variables and finally calculates outcomes in monetary and naturalistic units.

RESULTS

The introduction of the screening programme nearly doubled the proportion of diagnosed patients at the age of 50 and prolonged life expectancy. Three-yearly screening gained 0.0229 quality adjusted life years for an additional €83 per person compared with no screening and resulted an incremental cost-effectiveness ratio of €3630/quality adjusted life years.

CONCLUSION

From the economic perspective introduction of the 3-yearly screening programme is justifiable and it provides a good value for money. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Personalized support for chronic conditions. A novel approach for enhancing self-management and improving lifestyle

OBJECTIVE

Solutions for improving management of chronic conditions are under the attention of healthcare systems, due to the increasing prevalence caused by demographic change and better survival, and the relevant impact on healthcare expenditures. The objective of this study was to propose a comprehensive architecture of a mHealth system aimed at boosting the active and informed participation of patients in their care process, while at the same time overcoming the current technical and psychological/clinical issues highlighted by the existing literature.

METHODS

After having studied the current challenges outlined in the literature, both in terms of technological and human requirements, we focused our attention on some specific psychological aspects with a view to providing patients with a comprehensive and personalized solution. Our approach has been reinforced through the results of a preliminary assessment we conducted on 22 patients with chronic conditions. The main goal of such an assessment was to provide a preliminary understanding of their needs in a real context, both in terms of self-awareness and of their predisposition toward the use of IT solutions.

RESULTS

According to the specific needs and features, such as mindfulness and gamification, which were identified through the literature and the preliminary assessment, we designed a comprehensive open architecture able to provide a tailor-made solution linked to specific individuals' needs.

CONCLUSION

The present study represents the preliminary step towards the development of a solution aimed at enhancing patients' actual perception and encouraging self-management and self-awareness for a better lifestyle. Future work regards further identification of pathology-related needs and requirements through focus groups including all stakeholders in order to describe the architecture and functionality in greater detail.

Cost-effectiveness of SHINE: A Telephone Translation of the Diabetes Prevention Program

BACKGROUND

The Support, Health Information, Nutrition, and Exercise (SHINE) trial recently showed that a telephone adaptation of the Diabetes Prevention Program (DPP) lifestyle intervention was effective in reducing weight among patients with metabolic syndrome. The aim of this study is to determine whether a conference call (CC) adaptation was cost effective relative to an individual call (IC) adaptation of the DPP lifestyle intervention in the primary care setting.

METHODS

We performed a stochastic cost-effectiveness analysis alongside a clinical trial comparing two telephone adaptations of the DPP lifestyle intervention. The primary outcomes were incremental cost-effectiveness ratios estimated for weight loss, body mass index (BMI), waist circumference, and quality-adjusted life years (QALYs). Costs were estimated from the perspective of society and included direct medical costs, indirect costs, and intervention costs.

RESULTS

After one year, participants receiving the CC intervention accumulated fewer costs ($2,831 vs. $2,933) than the IC group, lost more weight (6.2 kg vs. 5.1 kg), had greater reduction in BMI (2.1 vs. 1.9), and had greater reduction in waist circumference (6.5 cm vs. 5.9 cm). However, participants in the CC group had fewer QALYs than those in the IC group (0.635 vs. 0.646). The incremental cost-effectiveness ratio for CC vs. IC was $9,250/QALY, with a 48% probability of being cost-effective at a willingness-to-pay of $100,000/QALY.

CONCLUSIONS

CC delivery of the DPP was cost effective relative to IC delivery in the first year in terms of cost per clinical measure (weight lost, BMI, and waist circumference) but not in terms of cost per QALY, most likely because of the short time horizon.

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.

Predicting cardiovascular risk in type 2 diabetes: the heterogeneity challenges

Type 2 diabetes mellitus has reached epidemic proportions around the world, and the increase in cardiovascular risk attributable to diabetes estimated to range from 2- to 4-fold poses grave public health concern. Though in some contexts type 2 diabetes has been equated with coronary heart disease equivalent risk, there is considerable evidence that incremental cardiovascular risk does not uniformly affect all people with type 2 diabetes. This heterogeneity in cardiovascular risk is multifactorial and only partially understood but is a key consideration for our understanding of the nexus of diabetes and cardiovascular disease and for the development of optimal and individualized cardiovascular risk reduction strategies. This review provides a brief synopsis of the concept of cardiovascular risk heterogeneity in diabetes, including epidemiologic evidence, discussion of established and potential determinants of heterogeneity, and clinical, research, and regulatory implications.

Type 2 Diabetes Mellitus Trajectories and Associated Risks

Disease progression models, statistical models that assess a patient's risk of diabetes progression, are popular tools in clinical practice for prevention and management of chronic conditions. Most, if not all, models currently in use are based on gold standard clinical trial data. The relatively small sample size available from clinical trial limits these models only considering the patient's state at the time of the assessment and ignoring the trajectory, the sequence of events, that led up to the state. Recent advances in the adoption of electronic health record (EHR) systems and the large sample size they contain have paved the way to build disease progression models that can take trajectories into account, leading to increasingly accurate and personalized assessment. To address these problems, we present a novel method to observe trajectories directly. We demonstrate the effectiveness of the proposed method by studying type 2 diabetes mellitus (T2DM) trajectories. Specifically, using EHR data for a large population-based cohort, we identified a typical trajectory that most people follow, which is a sequence of diseases from hyperlipidemia (HLD) to hypertension (HTN), impaired fasting glucose (IFG), and T2DM. In addition, we also show that patients who follow different trajectories can face significantly increased or decreased risk.

Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network

Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS.