The Stroke Prevention Policy Model: Linking Evidence and Clinical Decisions

Traditionally, the art of medicine has consisted of physicians using their expert and largely tacit knowledge [1] (culled from years of personal experience) to tailor diagnosis and therapy to the specific needs of individual patients. In this tradition, the use of external evidence to guide medical practice is discouraged because it might undermine the inherently private and unique relationship between physician and patient. Taken to the extreme, this perspective is clearly outdated. Indeed, one of the few points of general agreement in contemporary debates on health care is that the practice of medicine should be evidence based. Evidence-Based Medicine and Large Databases The challenge is to define the meaning of evidence-based medicine. To the extent that evidence based means that clinical encounters should be supported by scientific conclusions based on data as much as possible, the rationale for evidence-based medicine is self-evident. However, disagreements about the specifics are abundant. In particular, the clinical and health policy communities lack consensus about what types of evidence are relevant to decision making, how to properly evaluate and interpret various bits of evidence, and how to translate evidence into plans of action (such as recommendations and guidelines). In this context, the large database has emerged as an attractive but controversial source of information. In typical applications, the term large database is synonymous with databases from administrative files (such as Medicare claims), in which the original purpose of data collection was not evaluation of clinical issues. The appeal of these data is that they are readily available and relatively easy to use. Persons who advocate the use of these data anticipate that their value will be revealed through extensive statistical analyses (for example, regression modeling) that can lead to inferences about the relative efficacy, cost, and patterns of use of various clinical interventions [2]. The approach is seductively simple. Yet administrative data are subject to various and well-catalogued sources of bias [3], and their use as a source of information for clinical practice is viewed with legitimate concern. Given these problems with administrative data, it would be productive to broaden the meaning of large databases to include any database that contains many patients, regardless of the underlying study design. This broader definition includes Medicare claims files, large cohort studies, and meta-analyses of randomized trials that include many patients in aggregate. This definition is more consistent with the goals of evidence-based medicine. However, adaptation of the definition stretches the use of analytic tools typically encountered in database analysis. Rather than analyzing a file with a simple structure (one record for each patient) from a single source, researchers would be faced with various information sources that could have different variables and structures and could have been obtained from different types of study design. After our view of what data should be considered as evidence has been expanded, the next challenging step (addressed in this article) is to identify a means by which the various data can be effectively used. Our response to this challenge is to develop a comprehensive decision model that combines information from disparate sources into a single structure. Such an analytic approach to decision making invites criticism from investigators who believe that not all decision models are created equal and that some decision models resort to oversimplification and unrealistic assumptions. This criticism is fair because an information tool with the power of decision modeling carries substantial dangers. Certainly, model building should always be done in a systematic fashion, with special consideration given to such issues as consistency with underlying clinical data, documentation, reproducibility, and model validation. A Comprehensive Approach Applied to Decisions in Stroke Prevention Every year, approximately 500 000 persons in the United States have a stroke and 150 000 persons die as a result. Stroke is the third leading cause of death in the United States and is the leading cause of serious disability. At an annual cost of at least $30 billion to $40 billion, stroke is expensive [4, 5]. Moreover, stroke is a widely feared event: more than 40% of respondents to a recent survey rated a hypothetical major stroke to be a worse outcome than death [6]. Because strokes predominately affect elderly persons, the clinical and public health importance of stroke can be expected to grow as the number of elderly persons continues to increase. One of the goals of the Patient Outcomes Research Team for the Secondary and Tertiary Prevention of Stroke (Stroke PORT) is to evaluate the cost-effectiveness of surgical and medical interventions that prevent stroke. We have organized tasks related to this goal around a comprehensive model of stroke development and outcome: the Stroke Prevention Policy Model (SPPM) [7-11] (Figure 1). This model uses various data as inputs, including epidemiologic studies, randomized clinical trials, administrative data, and patient interviews. Many of these inputs represent large databases. The SPPM outputs include estimates of the health and economic outcomes of alternate stroke prevention practices. By linking clinical evidence (in particular, from population-based studies, claims data, and large randomized trials) to the health outcomes of individual persons, the SPPM is intended to improve the health care community's understanding of the available prevention options, thereby focusing on and improving the ability of clinicians, patients, and policy makers to make informed decisions about stroke prevention. Figure 1. Structure of the Stroke Prevention Policy Model (SPPM). The focus of this article is the role of large databases (as broadly defined) in the development and operation of a comprehensive decision-modeling effort-the SPPM. Our article consists of three sections. First, we describe the SPPM in terms of its basic structure and function. Because this article focuses on the application of large databases in the context of the SPPM effort rather than on the SPPM itself, this description is illustrative rather than comprehensive (more comprehensive descriptions are published elsewhere [8, 9]). Next, recognizing that the use of modeling continues to be a source of controversy within the medical community, we discuss the philosophical underpinnings of the SPPM. Finally, we consider how some of the lessons learned from the SPPM might be applied to simulation-based decision modeling for similarly complex issues. Description of the Stroke Prevention Policy Model Component Models The overall SPPM incorporates component models for the natural history of cerebrovascular disease (natural history model), implications of prevention strategies (intervention model), patient preferences (utility model), and costs (cost model). The natural history model (the backbone of the SPPM) describes the development of cerebrovascular disease in the absence of specific preventive interventions. The intervention model describes how these interventions modify the natural history of stroke. By linking the natural history model (as possibly modified by information about interventions) with the patient preference and cost models, the SPPM can be used to evaluate the cost-effectiveness of various intervention strategies. Natural History Model Structure The SPPM is a semi-Markov simulation model used to support decision and cost-effectiveness analyses. Its fundamental elements consist of health states and events, whereby events represent transitions between health states. Because of its technical complexity, the SPPM operates by simulating the natural history of a large cohort of patients. The health-related history of an individual patient is random (that is, it is based on the simulation's generation of random numbers) yet follows underlying transition probabilities. As the size of the simulated cohort increases, the relative effects of chance being caused by the simulation are reduced and the natural history of patients can be described with any desired degree of precision. To compare an intervention with standard practice, we simulate the experience of a large cohort of patients using the natural history model, modify the parameters of the natural history using the intervention model, and then repeat the simulation. We use a similar strategy to compare two interventions. Events The SPPM takes into account the following clinical events: transient ischemic attack, ischemic stroke, hemorrhagic stroke, myocardial infarction, complications of treatment for disorders other than stroke or myocardial infarction (for example, gastrointestinal bleeding as a complication of anticoagulation), and death (which can be the result of stroke, myocardial infarction, or other causes). Patients who survive at least 30 days after a stroke are randomly assigned a stroke-related disability level defined by the Rankin score [12]. Patients who die of a stroke within 30 days are assigned a Rankin score of 5 (indicating the highest possible stroke-related disability). The health states that represent fundamental elements of the model can be defined from these events in various ways. For example, a patient who has had both a transient ischemic attack and an ischemic stroke might be defined as being in the transient ischemic attack and ischemic stroke state. In contrast, the same patient might be defined as being in the transient ischemic attack state and then undergoing a transition to the ischemic stroke state. For convenience, we adopt the latter convention and focus on the following codes for the health states: ASY (asymptomatic), TIA (transient ischemic attack), IS (ischemic stroke), HS (hemorrhagic stroke), MI (myocardial infarction), and DT