Phil McEwan

Warfarin treatment in patients with atrial fibrillation: Observing outcomes associated with varying levels of INR control

INTRODUCTION We aimed to determine the level of INR control associated with reduced stroke and mortality. MATERIAL AND METHODS The study used a retrospective cohort design using linked inpatient, haematology and mortality data from Cardiff and the Vale of Glamorgan, UK. Anonymised patients admitted with a diagnosis of non-valvular atrial fibrillation (NVAF) were defined as warfarin or non-warfarin treated by number of repeated International Normalised Ratio (INR) tests. Warfarin treated patients (>5 INR tests) categorised as at moderate or high risk of stroke (CHADS2 score > or = 2) with varying levels of INR control were compared to those who did not receive warfarin treatment using Cox proportional hazards models controlling for age, sex and CHADS2 score. Outcome measures were time to stroke and mortality. RESULTS 6,108 patients with NVAF were identified. 2,235 (36.6%) of these patients had five or more INR readings and of these 486 (21.7%) had CHADS2 score > or = 2. There was significant improvement in time to stroke event in those patients with INR control of greater than 70% of time in therapeutic range (2.0 to 3.0) compared with the non-warfarin treatment group. Overall survival was significantly improved for all warfarin treated groups with INR control of greater than 40% of time in range. CONCLUSIONS Patients with INR control of above 70% of time in range had a significantly reduced risk of stroke. Patient suitability for warfarin treatment should be continuously assessed based on their ability to maintain a consistently therapeutic INR.

Multivariate models of health-related utility and the fear of hypoglycaemia in people with diabetes

ABSTRACT Aim: The aim was to statistically model the degree of fear of hypoglycaemia experienced by people with diabetes, and then model the resulting change in health-related utility associated with differing severity and frequency of hypoglycaemia. Methods: The study used pooled data from two previous postal surveys among subjects with confirmed diabetes conducted in Cardiff, UK (n = 1305 responses). The fear of hypoglycaemia was characterised using the Hypoglycaemia Fear Survey (HFS [eight question worry sub-scale only]), and health-related utility using the EQ5Dindex. The data were then analysed using univariate and multivariate analysis. Results: Following detailed preliminary analysis, a two-stage approach was used since fear was important when estimating the EQ5Dindex. Fear was then modelled as a function of the severity and frequency of hypoglycaemia while controlling for other factors such as diabetes-related complications. Each severe hypoglycaemic event resulted in a change of 5.881 units on the HFS. One or more symptomatic hypoglycaemic events over the same period results in a corresponding change of 1.773 units on the HFS. A 1 unit increase on the HFS results in a 0.008 unit decrease on the EQ5Dindex. Conclusion: While controlling for other factors, the fear of hypoglycaemia was an important determinant of health-related utility. The magnitude of fear of hypoglycaemia was associated with the severity and frequency of hypoglycaemia. Hypoglycaemia was associated with a considerable decrement in health-related utility as a function of increased fear. Measures should be taken to minimise the severity and frequency of hypoglycaemia.

Conceptualizing a Model A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–2

The appropriate development of a model begins with understanding the problem that is being represented. The aim of this article is to provide a series of consensus-based best practices regarding the process of model conceptualization. For the purpose of this series of papers, the authors consider the development of models whose purpose is to inform medical decisions and health-related resource allocation questions. They specifically divide the conceptualization process into two distinct components: the conceptualization of the problem, which converts knowledge of the health care process or decision into a representation of the problem, followed by the conceptualization of the model itself, which matches the attributes and characteristics of a particular modeling type to the needs of the problem being represented. Recommendations are made regarding the structure of the modeling team, agreement on the statement of the problem, the structure, perspective and target population of the model, and the interventions and outcomes represented. Best practices relating to the specific characteristics of model structure, and which characteristics of the problem might be most easily represented in a specific modeling method, are presented. Each section contains a number of recommendations that were iterated among the authors, as well as the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

The hospital burden of disease associated with bone metastases and skeletal-related events in patients with breast cancer, lung cancer, or prostate cancer in Spain

POCKETT R.D., CASTELLANO D., MCEWAN P., OGLESBY A., BARBER B.L. & CHUNG K. (2010) European Journal of Cancer Care19, 755–760 The hospital burden of disease associated with bone metastases and skeletal-related events in patients with breast cancer, lung cancer, or prostate cancer in Spain Metastatic bone disease (MBD) is the most common cause of cancer pain and of serious skeletal-related events (SREs) reducing quality of life. Management of MBD involves a multimodal approach aimed at delaying the first SRE and reducing subsequent SREs. The objective of the study was to characterise the hospital burden of disease associated with MBD and SREs following breast, lung and prostate cancer in Spain. Patients admitted into a participating hospital, between 1 January 2003 and 31 December 2003, with one of the required cancers were identified and selected for inclusion into the study. The index admission to hospital, incidence of patients admitted and hospital length of stay were analysed. There were 28 162 patients identified with breast, lung and prostate cancer. The 3 year incidence rates of hospital admission due to MBD were 95 per 1000 for breast cancer, 156 per 1000 for lung cancer and 163 per 1000 for prostate cancer. For patients admitted following an SRE, the incidence rates were 211 per 1000 for breast cancer, 260 per 1000 for lung cancer and 150 per 1000 for prostate cancer. This study has shown that cancer patients consume progressively more hospital resources as MBD and subsequent SREs develop.

Conceptualizing a Model: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2

The appropriate development of a model begins with understanding the problem that is being represented. The aim of this article was to provide a series of consensus-based best practices regarding the process of model conceptualization. For the purpose of this series of articles, we consider the development of models whose purpose is to inform medical decisions and health-related resource allocation questions. We specifically divide the conceptualization process into two distinct components: the conceptualization of the problem, which converts knowledge of the health care process or decision into a representation of the problem, followed by the conceptualization of the model itself, which matches the attributes and characteristics of a particular modeling type with the needs of the problem being represented. Recommendations are made regarding the structure of the modeling team, agreement on the statement of the problem, the structure, perspective, and target population of the model, and the interventions and outcomes represented. Best practices relating to the specific characteristics of model structure and which characteristics of the problem might be most easily represented in a specific modeling method are presented. Each section contains a number of recommendations that were iterated among the authors, as well as among the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

The impact of timing and prioritization on the cost-effectiveness of birth cohort testing and treatment for hepatitis C virus in the United States†‡

Recent United States guidelines recommend one‐time birth cohort testing for hepatitis C infection in persons born between 1945 and 1965; this represents a major public health policy undertaking. The purpose of this study was to assess the role of treatment timing and prioritization on predicted cost‐effectiveness. The MONARCH hepatitis C lifetime simulation model was used in conjunction with a testing and treatment decision tree to estimate the cost‐effectiveness of birth cohort versus risk‐based testing incorporating information on age, fibrosis stage and treatment timing. The study used a 1945‐1965 birth cohort and included disease progression, testing and treatment‐related parameters. Scenario analysis was used to evaluate the impact of hepatitis C virus (HCV) prevalence, treatment eligibility, age, fibrosis stage and timing of treatment initiation on total costs, quality‐adjusted life years (QALYs), HCV‐related complications and cost‐effectiveness. The cost‐effectiveness of birth cohort versus risk‐based testing was

Evaluation of the costs and outcomes from changes in risk factors in type 2 diabetes using the Cardiff stochastic simulation cost-utility model (DiabForecaster)

28,602. Assuming 91% of the population is tested, at least 278,000 people need to be treated for birth cohort testing to maintain cost‐effectiveness. Prioritizing treatment toward those with more advanced fibrosis is associated with a decrease in total cost of

Validation of the IMS CORE Diabetes Model.

7.5 billion and 59,035 fewer HCV‐related complications. Total QALYs and complications avoided are maximized when treatment initiation occurs as soon as possible after testing. Conclusion: This study confirms that birth cohort testing is, on average, cost‐effective. However, this remains true only when enough tested and HCV‐positive subjects are treated to generate sufficient cost offsets and QALY gains. Given the practical and financial challenges associated with implementing birth cohort testing, the greatest return on investment is obtained when eligible patients are treated immediately and those with more advanced disease are prioritized. (HEPATOLOGY 2013)

Economic impact of severe and non-severe hypoglycemia in patients with Type 1 and Type 2 diabetes in the United States

ABSTRACT Aims: The aim of this study was to determine the mean costs and outcomes associated with modifiable risk factors in patients with type 2 diabetes and to determine equivalent changes to these risk factors in terms of financial costs and health outcomes. Methods: The Cardiff Stochastic Simulation Cost-Utility Model (DiabForecaster), which evolved from the Eastman model, was used to follow a cohort of 10 000 patients over 20 years. Results: Costs were affected most significantly by changes in the total cholesterol to HDL cholesterol (Total‐C:HDL‐C) ratio and in HbA1c. Unit increases in Total‐C:HDL‐C increased discounted costs by £200 per patient; for ratios > 8 units, unit increases led to cost increases of £300 per patient. Unit increases in HbA1c increased per patient discounted costs from £200 (5–6%) up to £2900 (10–11%). Similar patterns were observed for QALYs. Estimates of equivalence showed that a 1% reduction in HbA1c was equivalent to an 0.4 increment in QALYs, which was equivalent to a reduction of 44 mmHg in SBP, 18.2 mg/dL in HDL, 100 mg/dL in total cholesterol or 1.8 units of Total‐C:HDL‐C ratio. A 1% reduction in HbA1c was also equivalent to £108 less cost, which was equivalent to a 13.0 mmHg decrease in SBP or a 0.57 unit decrease in the Total‐C:HDL‐C ratio. Conclusions: This model provides reliable utility estimates for diabetic complications and may eliminate uncertainty in cost-effectiveness analyses of treatment. These data also provide a novel way of comparing the value of treatments that have multiple effects.

Structural frameworks and key model parameters in cost-effectiveness analyses for current and future treatments of chronic hepatitis C

BACKGROUND The IMS CORE Diabetes Model (CDM) is a widely published and validated simulation model applied in both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) analyses. Validation to external studies is an important part of demonstrating model credibility. OBJECTIVE Because the CDM is widely used to estimate long-term clinical outcomes in diabetes patients, the objective of this analysis was to validate the CDM to contemporary outcomes studies, including those with long-term follow-up periods. METHODS A total of 112 validation simulations were performed, stratified by study follow-up duration. For long-term results (≥15-year follow-up), simulation cohorts representing baseline Diabetes Control and Complications Trial (DCCT) and United Kingdom Prospective Diabetes Study (UKPDS) cohorts were generated and intensive and conventional treatment arms were defined in the CDM. Predicted versus observed macrovascular and microvascular complications and all-cause mortality were assessed using the coefficient of determination (R(2)) goodness-of-fit measure. RESULTS Across all validation studies, the CDM simulations produced an R(2) statistic of 0.90. For validation studies with a follow-up duration of less than 15 years, R(2) values of 0.90 and 0.88 were achieved for T1DM and T2DM respectively. In T1DM, validating against 30-year outcomes data (DCCT) resulted in an R(2) of 0.72. In T2DM, validating against 20-year outcomes data (UKPDS) resulted in an R(2) of 0.92. CONCLUSIONS This analysis supports the CDM as a credible tool for predicting the absolute number of clinical events in DCCT- and UKPDS-like populations. With increasing incidence of diabetes worldwide, the CDM is particularly important for health care decision makers, for whom the robust evaluation of health care policies is essential.