The Stroke Prevention Policy Model: linking evidence and clinical decisions

Microsimulation Model Using Christiana Care Early Warning System (CEWS) to Evaluate Physiological Deterioration

While physiological warning signs prior to deterioration events during hospitalization have been widely studied, evaluating clinical interventions, such as rapid response team (RRT) activations, based on scoring systems remains an understudied area. Simulation of physiological deterioration patterns represented by scoring systems can facilitate testing different RRT policies without disturbing care processes. Christiana Care Early Warning System (CEWS) is a scoring system developed at the study hospital to detect the physiological warning signs and inform RRT activations. The objective of this study is to evaluate CEWS-triggered RRT policies based on patient demographics and policy structures. Using retrospective data derived from a subset of electronic health records between December 2015 and December 2016 (6000 patients), we developed a microsimulation model with integrated regression analysis to compare RRT policies on subpopulations defined by age, gender, and comorbidities to find score thresholds that result in the lowest percent of time spent above critical CEWS values. Policies that rely on average scores were more sensitive to threshold changes compared to policies that rely on current value and change in the CEWS. Policy using score threshold 10 provided the lowest percentage of time under the critical condition for majority of subpopulations. The proposed model is a novel framework to simulate individual deterioration patterns and systematically evaluate RRT policies based on their impact on health conditions. Our work highlights the importance of integration of data-driven models into personalized care and represents a significant opportunity to inform biomedical and health informatics research on designing and evaluating EWS-based clinical interventions.

A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention

Purpose. To conduct a cost-effectiveness analysis of HIV prevention when costs and effects cannot be measured directly. To quantify the total estimation of uncertainty due to sampling variability as well as inexact knowledge of HIV transmission parameters. Methods. The authors focus on estimating the incremental net health benefit (INHB) in a randomized trial of HIV prevention with intervention and control conditions. Using a Bernoulli model of HIV transmission, changes in the participants’ risk behaviors are converted into the number of HIV infections averted. A sampling model is used to account for variation in the behavior measurements. Bayes’s theorem and Monte Carlo methods are used to attain the stated objectives. Results. The authors obtained a positive mean INHB of 0.0008, indicating that advocacy training is just slightly favored over the control condition for men, assuming a $50,000 per quality-adjusted life year (QALY) threshold. To be confident of a positive INHB, the decision maker would need to spend more than $100,000 per QALY.

A model for undertaking effectiveness and cost-effectiveness analyses of primary preventive strategies in cardiovascular disease

BACKGROUND

Clinical trials generally provide strong evidence of the efficacy of cardiovascular preventive strategies, but poor evidence of their 'real-life' utility, in terms of effectiveness and cost-effectiveness.

DESIGN AND METHODS

The Cardiovascular Disease Prevention Model is presented, which represents a means of extrapolating the results of clinical trials to a broader, more relevant context. The model is configured as a decision-analysis tree, and underpinned by life-course analysis and Markov processes. Uncertainty and sensitivity analyses are undertaken by Monte Carlo simulation.

RESULTS

The results of effectiveness and cost-effectiveness analyses of a hypothetical preventive intervention are presented to demonstrate the outputs of the model. The potential impact and efficiency of the intervention are made obvious.

CONCLUSIONS

The Cardiovascular Disease Prevention Model offers a means to translate the results of trials of cardiovascular preventive interventions, in order to inform clinical and public health practice, as well as health policy.

Linked Sensitivity Analysis, Calibration, and Uncertainty Analysis Using a System Dynamics Model for Stroke Comparative Effectiveness Research

BACKGROUND

As health services researchers and decision makers tackle more difficult problems using simulation models, the number of parameters and the corresponding degree of uncertainty have increased. This often results in reduced confidence in such complex models to guide decision making.

OBJECTIVE

To demonstrate a systematic approach of linked sensitivity analysis, calibration, and uncertainty analysis to improve confidence in complex models.

METHODS

Four techniques were integrated and applied to a System Dynamics stroke model of US veterans, which was developed to inform systemwide intervention and research planning: Morris method (sensitivity analysis), multistart Powell hill-climbing algorithm and generalized likelihood uncertainty estimation (calibration), and Monte Carlo simulation (uncertainty analysis).

RESULTS

Of 60 uncertain parameters, sensitivity analysis identified 29 needing calibration, 7 that did not need calibration but significantly influenced key stroke outcomes, and 24 not influential to calibration or stroke outcomes that were fixed at their best guess values. One thousand alternative well-calibrated baselines were obtained to reflect calibration uncertainty and brought into uncertainty analysis. The initial stroke incidence rate among veterans was identified as the most influential uncertain parameter, for which further data should be collected. That said, accounting for current uncertainty, the analysis of 15 distinct prevention and treatment interventions provided a robust conclusion that hypertension control for all veterans would yield the largest gain in quality-adjusted life years.

CONCLUSIONS

For complex health care models, a mixed approach was applied to examine the uncertainty surrounding key stroke outcomes and the robustness of conclusions. We demonstrate that this rigorous approach can be practical and advocate for such analysis to promote understanding of the limits of certainty in applying models to current decisions and to guide future data collection.

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.

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.

Comparative effectiveness and implementation research: directions for neurology

There is an enormous unmet need for knowledge about how new insights from discovery and translational research can yield measurable, population-level improvements in health and reduction in mortality among those having or at risk for neurological disease. Once several, well-conducted randomized controlled trials establish the efficacy of a given therapy, implementation research can generate new knowledge about barriers to uptake of the therapy into widespread clinical care, and what strategies are effective in overcoming those barriers and in addressing health disparities. Comparative effectiveness research aims to elucidate the relative value (including clinical benefit, clinical harms, and/or costs) of alternative efficacious management approaches to a neurological disorder, generally through direct comparisons, and may include comparisons of methodologies for implementation. Congress has recently appropriated resources and established an institute to prioritize funding for such research. Neurologists and neuroscientists should understand the scope and objectives of comparative effectiveness and implementation research, their range of methodological approaches (formal literature syntheses, randomized trials, observational studies, modeling), and existing research resources (centers for literature synthesis, registries, practice networks) relevant to research for neurological conditions, to close the well-documented evidence-to-practice gap. Future directions include building this research resource capacity, producing scientists trained to conduct rigorous comparative effectiveness and implementation research, and embracing innovative strategies to set research priorities in these areas.

Dynamic microsimulation models for health outcomes: a review

BACKGROUND

Microsimulation models (MSMs) for health outcomes simulate individual event histories associated with key components of a disease process; these simulated life histories can be aggregated to estimate population-level effects of treatment on disease outcomes and the comparative effectiveness of treatments. Although MSMs are used to address a wide range of research questions, methodological improvements in MSM approaches have been slowed by the lack of communication among modelers. In addition, there are few resources to guide individuals who may wish to use MSM projections to inform decisions.

METHODS

. This article presents an overview of microsimulation modeling, focusing on the development and application of MSMs for health policy questions. The authors discuss MSM goals, overall components of MSMs, methods for selecting MSM parameters to reproduce observed or expected results (calibration), methods for MSM checking (validation), and issues related to reporting and interpreting MSM findings(sensitivity analyses, reporting of variability, and model transparency).

CONCLUSIONS

. MSMs are increasingly being used to provide information to guide health policy decisions. This increased use brings with it the need for both better understanding of MSMs by policy researchers, and continued improvement in methods for developing and applying MSMs.

Model-based cost-effectiveness analyses for the treatment of acute stroke events: a review and summary of challenges

OBJECTIVE

To summarize the methodological approaches used in published decision-analytic models evaluating interventions for acute stroke treatment, to highlight key components of decision-analytic models of stroke treatment, and to discuss challenges for developing stroke decision models.

METHODS

A review of the published literature was performed using Medline, to identify studies involving mathematical decision models to evaluate interventions for acute stroke treatment. Articles were analyzed to determine key components of a stroke model and to note areas in which data are lacking.

RESULTS

We identified 13 published models of acute stroke treatment. These models typically possessed a short-term treatment module and a long-term post-treatment module. The following aspects of economic modeling were found to be relevant for developing a stroke model: modeling approach and health state; health state transition probabilities; estimation of short-term, long-term, and indirect costs; health state utilities; poststroke mortality; time horizon; model validation; and estimation of parameter uncertainty.

CONCLUSIONS

Data gaps have limited the development of economic models in stroke to date. In order to more accurately assess the long-term incremental impact of a new treatment of stroke, future research is needed to address these data gaps. We recommend that the complexity of models for examining the cost-effectiveness of an acute stroke treatment be kept to a minimum such that it can incorporate the currently available data without making a large number of assumptions around the data.

Costs of stroke using patient-level data: a critical review of the literature

BACKGROUND AND PURPOSE

With decision-analytic models becoming more popular to assess the cost-effectiveness of health care interventions, the need for robust estimates on the costs of cerebrovascular disease is paramount. This study reports the results from a literature review of the costs of cerebrovascular diseases, and assesses the quality of the published evidence against a set of defined criteria.

METHODS

A broad literature search was conducted. Those studies reporting mean/median costs of cerebrovascular diseases derived from patient-level data in a developed country setting were included. Data were abstracted using standardized reporting forms and assessed against 4 predefined criteria: use of adequate methodologies, use of a population-based study, inclusion of premorbid resource use, and reporting of costs by different patient subgroups.

RESULTS

A total of 120 cost studies were identified. The cost estimates of stroke were compared by taking into account the effects of inflation and price differentials between countries. Average costs of stroke ranged from $468 to $146 149. Differences in costs were also found within country, with estimates in the USA varying 20-fold. Although the costing methodologies used were generally appropriate, only 5 studies were based on population-based studies, which are the gold standard study design when comparing incidence, outcome, and costs.

CONCLUSIONS

This review showed large variations in the costs of stroke, mainly attributable to differences in the populations studied, methods, and cost categories included. The wide range of cost estimates could lead to selection bias in secondary health economic analyses, with authors including those costs that are more likely to produce the desired results.

Use of dynamic microsimulation to predict disease progression in patients with pneumonia-related sepsis

Introduction

Sepsis is the leading cause of death in critically ill patients and often affects individuals with community-acquired pneumonia. To overcome the limitations of earlier mathematical models used to describe sepsis and predict outcomes, we designed an empirically based Monte Carlo model that simulates the progression of sepsis in hospitalized patients over a 30-day period.

Methods

The model simulates changing health over time, as represented by the Sepsis-related Organ Failure Assessment (SOFA) score, as a function of a patient's previous health state and length of hospital stay. We used data from patients enrolled in the GenIMS (Genetic and Inflammatory Markers of Sepsis) study to calibrate the model, and tested the model's ability to predict deaths, discharges, and daily SOFA scores over time using different algorithms to estimate the natural history of sepsis. We evaluated the stability of the methods using bootstrap sampling techniques.

Results

Of the 1,888 patients originally enrolled, most were elderly (mean age 67.77 years) and white (80.72%). About half (47.98%) were female. Most were relatively ill, with a mean Acute Physiology and Chronic Health Evaluation III score of 56 and Pneumonia Severity Index score of 73.5. The model's estimates of the daily pattern of deaths, discharges, and SOFA scores over time were not statistically different from the actual pattern when information about how long patients had been ill was included in the model (P = 0.91 to 0.98 for discharges; P = 0.26 to 0.68 for deaths). However, model estimates of these patterns were different from the actual pattern when the model did not include data on the duration of illness (P < 0.001 for discharges; P = 0.001 to 0.040 for deaths). Model results were stable to bootstrap validation.

Conclusion

An empiric simulation model of sepsis can predict complex longitudinal patterns in the progression of sepsis, most accurately by models that contain data representing both organ-system levels of and duration of illness. This work supports the incorporation into mathematical models of disease of the clinical intuition that the history of disease in an individual matters, and represents an advance over several prior simulation models that assume a constant rate of disease progression.

The cost-effectiveness and cost-benefit of screening and brief intervention for unhealthy alcohol use in medical settings

Economic evaluation can be a valuable tool for assessing the efficiency and value of health care programs. To examine the literature on the economic evaluation of alcohol screening and brief intervention in medical settings, relevant studies were identified in the MEDLINE database (1966 through November 2006) and by hand-searching the references of identified articles and relevant journals. The 15 identified studies used a range of economic evaluation methods, including cost analysis, cost-benefit, cost-effectiveness, and cost-utility. Nearly all of the studies supported the use of alcohol screening and brief intervention. The studies that prospectively collected cost and effect data and/or conformed closely to methodological guidelines demonstrated a strong economic benefit of alcohol screening and brief intervention when compared to usual care. Overall, the reviewed studies support alcohol SBI in medical settings as a wise use of health care resources and illustrate the usefulness of economic evaluation for assessing alcohol prevention and treatment programs.

The contrast and convergence of Bayesian and frequentist statistical approaches in pharmacoeconomic analysis

The application of Bayesian statistical analyses has been facilitated in recent years by methodological advances and an increasing complexity necessitated within research. Substantial debate has historically accompanied this analytic approach relative to the frequentist method, which is the predominant statistical ideology employed in clinical studies. While the essence of the debate between the two branches of statistics centres on differences in the use of prior information and the definition of probability, the ramifications involve the breadth of research design, analysis and interpretation. The purpose of this paper is to discuss the application of frequentist and Bayesian statistics in the pharmacoeconomic assessment of healthcare technology. A description of both paradigms is offered in the context of potential advantages and disadvantages, and applications within pharmacoeconomics are briefly addressed. Additional considerations are presented to stimulate further development and to direct appropriate applications of each method such that the integrity and robustness of scientific inference be strengthened.

Preventing another event: role of the hospitalist in discharge stroke prevention

Only 1 of 8 stroke patients is managed exclusively by a neurologist. Furthermore, many stroke patients harbor other vascular comorbidities and are also at risk for developing general medical complications that can lead to death following stroke. With the growing hospitalist system, it is quite clear that hospitalists are, and will increasingly be, an integral part of the care team for many hospitalized stroke patients. Because prevention remains the mainstay of treatment for ischemic stroke and TIA, it would be useful for practicing hospitalists to know the scientific evidence behind recommended therapeutic approaches to reducing vascular risk following stroke, as well as strategies for bridging the prevailing evidence-practice gap for hospitalized stroke patients, which this review article presents.

Combining information from multiple data sources to create multivariable risk models: illustration and preliminary assessment of a new method

A common practice of metanalysis is combining the results of numerous studies on the effects of a risk factor on a disease outcome. If several of these composite relative risks are estimated from the medical literature for a specific disease, they cannot be combined in a multivariate risk model, as is often done in individual studies, because methods are not available to overcome the issues of risk factor colinearity and heterogeneity of the different cohorts. We propose a solution to these problems for general linear regression of continuous outcomes using a simple example of combining two independent variables from two sources in estimating a joint outcome. We demonstrate that when explicitly modifying the underlying data characteristics (correlation coefficients, standard deviations, and univariate betas) over a wide range, the predicted outcomes remain reasonable estimates of empirically derived outcomes (gold standard). This method shows the most promise in situations where the primary interest is in generating predicted values as when identifying a high-risk group of individuals. The resulting partial regression coefficients are less robust than the predicted values.

Development and application of Model of Resource Utilization, Costs, and Outcomes for Stroke (MORUCOS): an Australian economic model for stroke

OBJECTIVES

To outline the development, structure, data assumptions, and application of an Australian economic model for stroke (Model of Resource Utilization, Costs, and Outcomes for Stroke [MORUCOS]).

METHODS

The model has a linked spreadsheet format with four modules to describe the disease burden and treatment pathways, estimate prevalence-based and incidence-based costs, and derive life expectancy and quality of life consequences. The model uses patient-level, community-based, stroke cohort data and macro-level simulations. An interventions module allows options for change to be consistently evaluated by modifying aspects of the other modules. To date, model validation has included sensitivity testing, face validity, and peer review. Further validation of technical and predictive accuracy is needed. The generic pathway model was assessed by comparison with a stroke subtypes (ischemic, hemorrhagic, or undetermined) approach and used to determine the relative cost-effectiveness of four interventions.

RESULTS

The generic pathway model produced lower costs compared with a subtypes version (total average first-year costs/case AUD$ 15,117 versus AUD$ 17,786, respectively). Optimal evidence-based uptake of anticoagulation therapy for primary and secondary stroke prevention and intravenous thrombolytic therapy within 3 hours of stroke were more cost-effective than current practice (base year, 1997).

CONCLUSIONS

MORUCOS is transparent and flexible in describing Australian stroke care and can effectively be used to systematically evaluate a range of different interventions. Adjusting results to account for stroke subtypes, as they influence cost estimates, could enhance the generic model.

Direct costs of glaucoma and severity of the disease: a multinational long term study of resource utilisation in Europe

BACKGROUND

Resource utilisation and direct costs associated with glaucoma progression in Europe are unknown. As population progressively ages, the economic impact of the disease will increase.

METHODS

From a total of 1655 consecutive cases, the records of 194 patients were selected and stratified by disease severity. Record selection was based on diagnoses of primary open angle glaucoma, glaucoma suspect, ocular hypertension, or normal tension glaucoma; 5 years minimum follow up were required. Glaucoma severity was assessed using a six stage glaucoma staging system based on static threshold visual field parameters. Resource utilisation data were abstracted from the charts and unit costs were applied to estimate direct costs to the payer. Resource utilisation and estimated direct cost of treatment, per person year, were calculated.

RESULTS

A statistically significant increasing linear trend (p = 0.018) in direct cost as disease severity worsened was demonstrated. The direct cost of treatment increased by an estimated 86 for each incremental step ranging from 455 euro per person year for stage 0 to 969 euro per person year for stage 4 disease. Medication costs ranged from 42% to 56% of total direct cost for all stages of disease.

CONCLUSIONS

These results demonstrate for the first time in Europe that resource utilisation and direct medical costs of glaucoma management increase with worsening disease severity. Based on these findings, managing glaucoma and effectively delaying disease progression would be expected to significantly reduce the economic burden of this disease. These data are relevant to general practitioners and healthcare administrators who have a direct influence on the distribution of resources.

Cost-effectiveness of antiplatelet agents in secondary stroke prevention: the limits of certainty

Which of the available antiplatelet therapies should be preferred for secondary prevention of recurrent ischemic stroke has been contentious.

OBJECTIVE

We applied the Duke Stroke Policy Model (DSPM) to reconsider this issue, paying particular attention to the degree of uncertainty in the estimates of their efficacy. The DSPM is a continuous-time simulation model of stroke development and outcome.

METHODS

We modified the inputs to reflect the cost of the drugs aspirin (ASA), extended release dipyridamole/aspirin (DP/A) and clopidogrel (CLO), as well as their relative risk in preventing subsequent ischemic stroke in comparison with placebo (PBO). These relative risks were derived from published reports from the second European Stroke Prevention Study (ESPS-2) and Clopidogrel Versus Aspiring in Patients at Risk of Ischemic Events studies. Precision was addressed by applying bootstrapping to the above estimates of relative risk. The target population was 70-year-old men with nondisabling stroke. The outcome measures were quality-adjusted life-years (QALYs), costs, and costs per QALY.

RESULTS

Results of Base Case Analysis: In large part because of its modest drug cost, ASA was cost-effective in comparison with PBO. DP/A tended to have improved outcomes, but at increased costs. CLO was dominated in the base case.

RESULTS OF SENSITIVITY ANALYSIS

ASA and DP/A cannot be differentiated on a statistical basis alone. In probabilistic sensitivity analysis, CLO was rarely preferred.

CONCLUSIONS

Either DP/A or ASA appear to be a good value in comparison with no treatment, but there is no clear winner between the two. In the absence of a definitive randomized trial, simulation modeling can help clarify the trade-offs between the various antiplatelet agents, but not beyond the constraints imposed by the imprecision in the estimates that can be obtained from the current evidence base.

The Impact of Patient Self-Testing of Prothrombin Time for Managing Anticoagulation: Rationale and Design of VA Cooperative Study #481—The Home INR Study (THINRS)

BACKGROUND

Anticoagulation (AC) with warfarin reduces the risk of thromboembolism (TE) in a variety of applications, yet despite compelling evidence of the value and importance of high quality AC, warfarin remains underused, and dosing is often suboptimal. Approaches to improve AC quality include (1) an AC service (ACS), which allows the physician to delegate day-to-day details of AC management to another provider dedicated to AC care, and (2) incorporating into the treatment plan patient self-testing (PST) under which, after completing a training program, patients perform their own blood testing (typically, using a finger-stick blood analyzer), have dosage adjustments guided by a standard protocol, and forward test results, dosing and other information to the provider. Studies have suggested that PST can improve the quality of AC and perhaps lower TE and bleed rates. The purpose of Department of Veterans Affairs (VA) Cooperative Studies Program (CSP) #481, "The Home INR Study" (THINRS) is to compare AC management with frequent PST using a home monitoring device to high quality AC management (HQACM) implemented by an ACS with conventional monitoring of prothrombin time by international normalized ratio (INR) on major health outcomes. PST in THINRS involves use of an INR monitoring device that is FDA approved for home use.

STUDY DESIGN

Sites are VA Medical Centers where the ACS has an active roster of more than 400 patients. THINRS includes patients with atrial fibrillation (AF) and/or mechanical heart valve (MHV) expected to be anticoagulated indefinitely. THINRS has two parts. In Part 1, candidates for PST are evaluated for 2 to 4 weeks for their ability to use home monitoring devices. In Part 2, individuals capable of performing PST are randomized to (1) HQACM with testing every 4 weeks and as indicated for out of range values, medication/clinical changes, or (2) PST with testing every week and as indicated for out of range values, medication/clinical changes. The primary outcome measure is event rates, defined as the percent of patients who have a stroke, major bleed, or die. Secondary outcomes include total time in range (TTR), other events (myocardial infarction (MI), non-stroke TE, minor bleeds), competence and compliance with PST, satisfaction with AC, AC associated quality of life (QOL), and cost-effectiveness. To assess the effect of PST frequency on TTR and other outcomes, at selected sites patients randomized to perform PST are assigned one of three test frequencies (weekly, twice weekly, or once every four weeks).

Validation of a decision model for preventive pharmacological strategies in postmenopausal women

BACKGROUND

Benefits and risks of a combined hormone replacement therapy (HRT) based on randomized clinical trial emerged on various disease endpoints in 2002. The Women's Health Initiative (WHI) provides an important health answer for healthy postmenopausal women, such as do not use combined HRT to prevent chronic disease, because of the elevated risk of coronary artery disease (CHD), stroke and venous thromboembolism. In March 2004, the NIH stopped the drugs in the estrogen-alone trial after finding an increase risk of stroke and no effect, neither an increase or a decrease, on risk of CHD after an average of 7 years in the trial. On the other hand, raloxifene, which does not seem to significantly increase the risk of cardiovascular events and could retain skeletal benefits without stimulating endometrial and breast tissue, requires decision-makers since no current data on these disease clinical endpoints have been published.

OBJECTIVE

To construct a multi-disease model based on patient-specific risk factor profiles, and to validate the multi-disease model with several tools of internal and external validities.

METHODS

A Markov state model was developed. The risks of these various diseases (including coronary artery disease, stroke, hip fracture and breast cancer) are derived from published hazards proportional models which take into account significant risk factors. Canadian-specific rates and data sources for these transition probabilities are derived from published studies and Canadian Health Statistics. The validation of our model were based on several tools of internal and external validities, such as Canadian life expectancy, population-based incidence rate of diseases, clinical trials and other published life expectancy models.

RESULTS

First, presumably, small changes in the lifetime probability of dying support the hypothesis that the disease states operate in a largely independent fashion. For instance, the difference in the probability of dying from a particular disease by the complete elimination of a selected disease, such as CHD, stroke or breast cancer, ranged from 0.2 to 2.2% of difference in the lifetime probability of dying of these diseases. Second, we demonstrated that the model adequately predicted the Canadian population lifetable and disease-incidence rates from population-based data among women from 45 to 75 years old. The predictions of the model were cross-checked from non-source data, such as predicted outcomes versus observed outcomes from results of clinical trials. Predicted relative risks of CHD event, breast cancer and hip fracture fell in the reported 95% confidence interval of clinical trials. Finally, predicted treatment benefits are comparable with those of published life expectancy models.

CONCLUSIONS

The results of the study demonstrated that this multi-disease model, including coronary artery disease, stroke, hip fracture and breast cancer, is a valid model to predict the impact on life expectancy or number of events prevented for preventive pharmacological interventions.

Case Scenarios to Assess Australian General Practitioners’ Understanding of Stroke Diagnosis, Management, and Prevention

Background and Purpose— Stroke represents the third-leading cause of death in Western society. Prompt and appropriate intervention for those with stroke or at risk of stroke is highly dependent on general practitioners’ (GPs’) knowledge and referral practices.

Methods— We randomly selected 490 eligible GPs from New South Wales, Australia, to complete our self-administered questionnaire. Case scenarios were used to assess GPs’ knowledge of transient ischemic attack/ stroke risk factors, stroke prevention strategies, and management of asymptomatic and symptomatic patients.

Results— We received 296 completed questionnaires (60% response rate). Nearly all GPs (286, 96.6%) strongly agreed or agreed that stroke is a medical emergency. Most were aware that management by multidisciplinary teams improves outcomes (strongly agree or agree, 279; 94.3%). GPs endorsed the effectiveness of aspirin and warfarin in reducing stroke morbidity. GPs also were aware of the benefit of carotid endarterectomy (CEA) for symptomatic patients with >80% carotid stenosis but were less aware of the value of CEA for symptomatic patients with moderate stenosis. Vascular surgeon was the specialist of choice for referral of patients with high-grade carotid stenosis. Few GPs reported having seen the Cochrane Collaboration reviews of CEA for symptomatic (3.0%) and asymptomatic (1.7%) patients.

Conclusions— GPs were well apprised of the evidence to support CEA for symptomatic patients with high-grade carotid stenosis. Our findings, however, invite more purposeful and effective education of GPs about stroke prevention, diagnosis, and management if optimal outcomes are to be realized.

Measuring uncertainty in complex decision analysis models

Prediction models used in support of clinical and health policy decision making often need to consider the course of a disease over an extended period of time, and draw evidence from a broad knowledge base, including epidemiologic cohort and case control studies, randomized clinical trials, expert opinions, and more. This paper is a brief introduction to these complex decision models, their relation to Bayesian decision theory, and the tools typically used to describe the uncertainties involved. Concepts are illustrated throughout via a simplified tutorial.

Decision analytical economic modelling within a Bayesian framework: application to prophylactic antibiotics use for caesarean section

Economic evaluation of health care interventions based on decision analytic modelling can generate valuable information for health policy decision makers. However, the usefulness of the results obtained depends on the quality of the data input into the model; that is, the accuracy of the estimates for the costs, effectiveness, and transition probabilities between the different health states of the model. The aim of this paper is to review the use of Bayesian decision models in economic evaluation and to demonstrate how the individual components required for decision analytical modelling (i.e., systematic review incorporating meta-analyses, estimation of transition probabilities, evaluation of the model, and sensitivity analysis) may be addressed simultaneously in one coherent Bayesian model evaluated using Markov Chain Monte Carlo simulation implemented in the specialist Bayesian statistics software WinBUGS. To illustrate the method described, a simple probabilistic decision model is developed to evaluate the cost implications of using prophylactic antibiotics in caesarean section to reduce the incidence of wound infection. The advantages of using the Bayesian statistical approach outlined compared to the conventional classical approaches to decision analysis include the ability to: (i) perform all necessary analyses, including all intermediate analyses (e.g., meta-analyses) required to derive model parameters, in a single coherent model; (ii) incorporate expert opinion either directly or regarding the relative credibility of different data sources; (iii) use the actual posterior distributions for parameters of interest (opposed to making distributional assumptions necessary for the classical formulation); and (iv) incorporate uncertainty for all model parameters.

UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine

BACKGROUND AND PURPOSE

People with type 2 diabetes are at elevated risk of stroke compared with those without diabetes. Relative risks have been examined in earlier work, but there is no readily available method for predicting the absolute risk of stroke in a diabetic individual. We developed mathematical models to estimate the risk of a first stroke using data from 4549 newly diagnosed type 2 diabetic patients enrolled in the UK Prospective Diabetes Study.

METHODS

During 30 700 person-years of follow-up, 188 first strokes (52 fatal) occurred. Model fitting was carried out by maximum likelihood estimation using the Newton-Raphson method. Diagnostic plots were used to compare survival probabilities calculated by the model with those calculated using nonparametric methods.

RESULTS

Variables included in the final model were duration of diabetes, age, sex, smoking, systolic blood pressure, total cholesterol to high-density lipoprotein cholesterol ratio and presence of atrial fibrillation. Not included in the model were body mass index, hemoglobin A1c, ethnicity, and ex-smoking status. The use of the model is illustrated with a hypothetical study power calculation.

CONCLUSIONS

This model forecasts the absolute risk of a first stroke in people with type 2 diabetes using variables readily available in routine clinical practice.

Development of a decision-analytic model of stroke care in the United States and Europe

OBJECTIVE

Stroke places a huge burden on society in terms of premature death, disability, and costs of care. Increasingly, the cost-effectiveness of new interventions needs to be demonstrated before their widespread implementation. Clinical trials are unable to measure the long-term impact of such new interventions in stroke care, and a modeling approach is necessary. The Stroke Outcome Model has been developed in four countries: France, Germany, the United Kingdom, and the United States as a flexible tool for this purpose.

METHOD

The decision-analytic model represents the management of acute stroke and long-term care and prevention of recurrence for stroke survivors. The latter consists of semi-Markov state-transition processes, with health states defined by therapy, disability, and occurrence of further stroke. Sources of clinical data include trials, meta-analyses, and prospective cohort studies such as the Oxfordshire Community Stroke Project and the Northern Manhattan Stroke Study. Resource use data were obtained from published sources and expert clinician panels. Outcome measures used were strokes averted, life years, and quality-adjusted life-years gained.

RESULTS

The model has been used to undertake economic analyses of antiplatelet therapy for the prevention of recurrent strokes, and of stroke unit care and thrombolytic therapy in acute stroke. From a health- and social-care perspective, new interventions were found to be cost saving or to provide health benefits at modest additional cost. Results were sensitive to the cost perspective, time horizon, baseline risk of stroke recurrence, and choice of effectiveness measure.

CONCLUSION

The development of this model highlights the need for improved information on prognosis and resources used by stroke survivors and the importance of differentiating between economically distinct end points such as death, disabled survival and nondisabled survival, which may be combined as outcomes in clinical trials.

Cost-effectiveness of ancrod treatment of acute ischaemic stroke: results from the Stroke Treatment with Ancrod Trial (STAT)

RATIONALE, AIMS AND OBJECTIVES

This paper describes a recent randomized controlled trial in which 42% of patients receiving ancrod attained a favourable outcome in comparison with 34% of controls. Although the above effect size corresponds to a number needed to treat (to achieve a favourable outcome) of approximately 13, intuition does not necessarily suggest what would be the overall impact of a treatment with this level of efficacy.

METHODS

The objective was to evaluate the cost-effectiveness of ancrod. Cost-effectiveness analysis of data from the Stroke Treatment with Ancrod Trial (STAT) trial was carried out. The participants were 495 patients with data on functional status at the conclusion of follow-up. Short-term results were based upon utilization and quality of life observed during the trial; these were merged with expected long-term results obtained through simulation using the Stroke Policy Model. The main outcome measure was incremental cost-effectiveness ratio.

RESULTS

Ancrod treatment resulted in both better quality-adjusted life expectancy and lower medical costs than placebo as supported by sensitivity analysis. The cost differential was primarily attributable to the long-term implications of ancrod's role in reducing disability.

CONCLUSIONS

If ancrod is even modestly effective, it will probably be cost-effective (and, indeed, cost-saving) as well. The net population-level impact of even modestly effective stroke treatments can be substantial.

Evidence-based medicine as Bayesian decision-making

We review two recent trends: the emergence of evidence-based medicine and the growing use of Bayesian statistics in medical applications. Evidence-based medicine requires an integrated assessment of the available evidence, and associated uncertainty, but there is also an emphasis on decision-making, for individual patients, or at other points in the health-care system. This demands consideration of the values and costs associated with potential outcomes. We argue that the natural statistical framework for evidence-based medicine is a Bayesian approach to decision-making that incorporates an integrated summary of the available evidence and associated uncertainty with assessment of utilities. We outline a practical agenda for further development.

Stroke treatment economic model (STEM): predicting long-term costs from functional status

BACKGROUND AND PURPOSE

Stroke is a debilitating disease with long-term social and economic consequences. As new therapies for acute ischemic stroke are forthcoming, there is an increasing need to understand their long-term economic implications. To address this need, a stroke economic model was created.

METHODS

The model consists of 3 modules. A short-term module incorporates short-term clinical trial data. A long-term module composed of several Markov submodels predicts patient transitions among various locations over time. The modules are connected via a bridge component that groups the survivors at the end of the short-term module according to their functional status and location. Examples of analyses that can be conducted with this model are provided with the use of data from 2 international trials. For illustration, UK unit costs were estimated.

RESULTS

With the trial data in the short-term module, the short-term management cost is estimated to be pound8326 (US $13,649 [USD]). Hospital stay was the major cost driver. By the end of the trials, there was a pronounced difference in the distribution of patient locations between functional groups. It is predicted in the long-term module that the subsequent cost amounts to pound75 985 (124,564 USD) for a major and pound27,995 (45,893 USD) for a minor stroke.

CONCLUSIONS

Linking functional recovery at the end of short-term treatment with patients' treatment and residential locations allows this model to estimate the long-term economic impact of stroke interventions. Using patient location instead of the more common natural history as the model foundation allows quantification of the long-term impact to become data driven and hence increases confidence in the results.

A systematic review of cost-effectiveness research of stroke evaluation and treatment

BACKGROUND AND PURPOSE

This work was undertaken to review research addressing the cost-effectiveness of stroke-related diagnostic, preventive, or therapeutic interventions.

METHODS

We performed searches of MEDLINE, Excerpta Medica online, HealthSTAR, and Sciences Citation Index Expanded and examined the reference lists of the studies and reviews obtained. From these, we selected studies that reported an incremental analysis of cost per effect, in which the effect measure was life-years or quality-adjusted life-years. We abstracted data from each study using a standardized reporting form. Twenty-six articles met the eligibility criteria and were included in the review.

RESULTS

The methodological quality of the articles reviewed has improved compared with previously reported. Many stroke evaluation and treatment policies may result in benefits to health that are considered worth their cost. Some interventions were considered cost-ineffective (anticoagulation in low-risk nonvalvular atrial fibrillation and surveillance with duplex ultrasound after endarterectomy). Different studies addressing the cost-effectiveness of screening asymptomatic carotid stenosis resulted in strikingly divergent conclusions, from being cost-effective to being detrimental. Other studies omitted important costs that, if included, would likely have had profound impact on their cost-effectiveness estimates.

CONCLUSIONS

Given the divergent conclusions drawn from studies addressing similar questions, it may be premature to use the results of cost-effectiveness research in developing stroke policy and practice guidelines. Successful implementation of such evaluations in the care of patients with stroke will depend on further standardization of methodology and critical appraisal of reported findings.

An analysis of optimal resource allocation for prevention of infection with human immunodeficiency virus (HIV) in injection drug users and non-users

Millions of dollars are spent annually to prevent infection with human immunodeficiency virus (HIV) without a thorough understanding of the most effective way to allocate these resources. The authors' objective was to determine the allocation of new resources among prevention programs targeted to a population of injection drug users (IDUs) and a population of non-injection drug users (non-IDUs) that would minimize the total number of incident cases of HIV infection over a given time horizon. They developed a dynamic model of HIV transmission in IDUs and non-IDUs and estimated the relationship between prevention program expenditures and reductions in HIV transmission. They evaluated three prevention programs: HIV testing with routine counseling, HIV testing with intensive counseling, and HIV testing and counseling linked to methadone maintenance programs. They modeled a low-risk IDU population (5% HIV prevalence) and a moderate-risk IDU population (10% HIV prevalence). For different available budgets, they determined the allocation of resources among the prevention programs and populations that would minimize the number of new cases of HIV infection over a five-year period, as well as the incremental value of additional prevention funds. The study framework provides a quantitative, systematic approach to funding programs to prevent HIV infection that accounts for HIV transmission dynamics, population size, and the costs and effectiveness of the interventions in reducing HIV transmission. The approach is general and can be used to evaluate a broader group of prevention programs and risk populations. This framework thus could enable policy makers and clinicians to identify a portfolio of programs that provide, collectively, the most benefit for a given budget.

Practice guidelines and tort reform: the legal system confronts the technocratic wish

Recent scholarly writing has argued that the advent of managed care within the health policy arena can be seen as a contemporary manifestation of a broader set of concerns focusing on how objective decision procedures become politically legitimated--what one recent commentator has characterized as a faith in the technocratic wish. In the 1990s, this faith in objective decision procedures has manifested itself through the emergence of outcomes assessment and the development of practice guidelines. Notably, a few states have sought to couple the practice guidelines movement with tort reform by enacting demonstration projects permitting physicians to introduce evidence that they followed practice guidelines as an affirmative defense. In this article, I argue that even though the introduction of practice guidelines may promote the policy objective of cost-effectiveness in the delivery of health care services, their use to establish culpability in actual cases may be more difficult because the structure of legal reasoning focuses on the particular facts in the case at hand rather than appealing to abstract decision procedures. By highlighting the potential difficulties of invoking practice guidelines in the adjudication of actual malpractice cases, I demonstrate how a process of ongoing political negotiation will be necessary if the technocratic faith in practice guidelines is to become justified in reality.

Performing cost-effectiveness analysis by integrating randomized trial data with a comprehensive decision model: application to treatment of acute ischemic stroke

A recent national panel on cost-effectiveness in health and medicine has recommended that cost-effectiveness analysis (CEA) of randomized controlled trials (RCTs) should reflect the effect of treatments on long-term outcomes. Because the follow-up period of RCTs tends to be relatively short, long-term implications of treatments must be assessed using other sources. We used a comprehensive simulation model of the natural history of stroke to estimate long-term outcomes after a hypothetical RCT of an acute stroke treatment. The RCT generates estimates of short-term quality-adjusted survival and cost and also the pattern of disability at the conclusion of follow-up. The simulation model incorporates the effect of disability on long-term outcomes, thus supporting a comprehensive CEA. Treatments that produce relatively modest improvements in the pattern of outcomes after ischemic stroke are likely to be cost-effective. This conclusion was robust to modifying the assumptions underlying the analysis. More effective treatments in the acute phase immediately following stroke would generate significant public health benefits, even if these treatments have a high price and result in relatively small reductions in disability. Simulation-based modeling can provide the critical link between a treatment's short-term effects and its long-term implications and can thus support comprehensive CEA.

Shortcomings of the randomized controlled trial: a view from the boondocks

The randomized controlled trial (RCT), despite its well-known limitations, continues to be regarded as a gold standard in determining whether an intervention does more harm than good. Some recent evidence suggests that it tends to overvalue the modalities it tests. Moreover, the accuracy with which the disorder under consideration is diagnosed can be critical to the performance of a new intervention designed for it. When technological progress allows us to diagnose milder instances, some therapies, possibly useful in dire circumstances, will appear ineffective if most of a trial population is at low risk. Human individuality makes it impossible to duplicate a RCT. As a result, Popper's criterion of falsifiability may not be met and so the carrying out of a large-scale therapeutic experiment may not be a scientific activity. Finally, it is doubtful whether group probabilities derived from RCTs can be safely applied to individuals. These and other reservations concerning the applicability of the RCT to clinical practice are discussed.

Utilities for major stroke: results from a survey of preferences among persons at increased risk for stroke

BACKGROUND

Patient beliefs, values, and preferences are crucial to decisions involving health care. In a large sample of persons at increased risk for stroke, we examined attitudes toward hypothetical major stroke.

METHODS AND RESULTS

Respondents were obtained from the Academic Medical Center Consortium (n = 621), the Cardiovascular Health Study (n = 321 ), and United Health Care (n = 319). Preferences were primarily assessed by using the time trade off (TTO). Although major stroke is generally considered an undesirable event (mean TTO = 0.30), responses were varied: although 45% of respondents considered major stroke to be a worse outcome than death, 15% were willing to trade off little or no survival to avoid a major stroke.

CONCLUSIONS

Providers should speak directly with patients about beliefs, values, and preferences. Stroke-related interventions, even those with a high price or less than dramatic clinical benefits, are likely to be cost-effective if they prevent an outcome (major stroke) that is so undesirable.