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The Stroke Prevention Policy Model: Linking Evidence and Clinical Decisions

David B. Matchar, MD; Gregory P. Samsa, PhD; J. Rosser Matthews, PhD; Marek Ancukiewicz, PhD; Giovanni Parmigiani, PhD; Vic Hasselblad, PhD; Phillip A. Wolf, MD; Ralph B. D'Agostino, PhD; and Joseph Lipscomb, PhD
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From Duke University and the Duke University School of Medicine, Durham, North Carolina; and Massachusetts General Hospital, Boston University School of Medicine, and Boston Medical Center, Boston, Massachusetts. Note: This article is one of a series of articles comprising an Annals of Internal Medicine supplement entitled “Measuring Quality, Outcomes, and Cost of Care Using Large Databases: The Sixth Regenstrief Conference.” To see a complete list of the articles included in this supplement, please view its Table of Contents. Grant Support: By grant from the Agency for Health Care and Research, contract #282-81-0028; Stroke Prevention Patient Outcome Research Team (PORT) National Institute of Health/Heart, Lung, and Blood Institute, contract #NO1-HC-38038; and National Institute of Neurological Disorders and Stroke, grant #2-RO1-NS-17950-16. Requests for Reprints: David B. Matchar, MD, Center for Clinical Health Policy Research, Duke University, First Union Tower, Suite 230, 2200 West Main Street, Durham, NC 27705. Current Author Addresses: Drs. Matchar, Matthews, and Hasselblad: Center for Clinical Health Policy Research, Duke University, First Union Tower, Suite 230, 2200 West Main Street, Durham, NC 27705. Dr. Samsa: Division of Biometry, Department of Community and Family Medicine, Duke University Medical Center, First Union Tower, Suite 230, 2200 West Main Street, Durham, NC 27705. Dr. Ancukiewicz: Division of Radiation Oncology, Department of Medicine, Massachusetts General Hospital, Founders Building #516, 55 Fruit Street, Boston, MA 02114. Dr. Parmigiani: Institute of Statistics and Decision Sciences, Duke University, Box 90251, Durham, NC 27708. Dr. Wolf: Section of Preventive Medicine and Epidemiology, Evans Memorial Department of Clinical Research and the Department of Medicine, Boston Medical Center, 715 Albany Street, Room B608, Boston, MA 02118. Dr. D'Agostino: Department of Mathematics, Boston University College of Arts and Sciences, 111 Cummington Street, Boston, MA 02215. Dr. Lipscomb: Sanford Institute of Public Policy, Duke University, Box 90245, 212 Sanford Institute Building, Durham, NC 27708.

Copyright ©2004 by the American College of Physicians

Ann Intern Med. 1997;127(8_Part_2):704-711. doi:10.7326/0003-4819-127-8_Part_2-199710151-00054
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Simulation models that support decision and cost-effectiveness analysis can further the goals of evidence-based medicine by facilitating the synthesis of information from several sources into a single comprehensive structure. The Stroke Prevention Policy Model (SPPM) performs this function for the clinical and policy questions that surround stroke prevention. This paper first describes the basic structure and functions of the SPPM, concentrating on the role of large databases (broadly defined as any database that contains many patients, regardless of study design) in providing the SPPM inputs. Next, recognizing that the use of modeling continues to be a source of some controversy in the medical community, it discusses the philosophical underpinnings of the SPPM. Finally, it discusses conclusions in the context of both stroke prevention and other complex medical decisions. We conclude that despite the difficulties in developing comprehensive models (for example, the length and complexity of model development and validation processes, the proprietary nature of data sources, and the necessity for developing new software), the benefits of such models exceed the costs of continuing to rely on more conventional methods. Although they should not replace the clinician in decision making, comprehensive models based on the best available evidence from large databases can support decision making in medicine.


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Figure 1.
Structure of the Stroke Prevention Policy Model (SPPM).

The link between inputs (clinical trials, epidemiologic studies, administrative data, surveys of patient preferences, and expert opinion) and outputs (survival, quality-adjusted survival, and cost) is shown.

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Figure 2.
Matrix of cumulative probability distributions.

Transitions to the same health state (for example, IS to IS) indicate a repeated event. Patients are eligible for transitions between any of these health states; however, patients cannot return to the ASY state after experiencing an event relevant (by definition) to the Stroke Prevention Policy Model. In addition, patients cannot make a transition from TIA to TIA (based on the clinical determination that TIA is a transient event). Patients are eligible for all transitions to and from HS. However, because of data limitations (that is, relatively few HS events in the data sources-only 15% of strokes are HS) and because the focus of the SPPM is on IS rather than on HS, the occurrence of HS is treated as statistically independent of other transitions in the SPPM. In other words, all transitions to HS events use the cumulative probability distribution of ASY to TIA. All transitions from HS use the cumulative probability distribution based on the preceding state. ASY = asymptomatic; DTH = death (from any cause); HS = hemorrhagic stroke; IS = ischemic stroke; MI = myocardial infarction; and TIA = transient ischemic attack.

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