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Risk Stratification: A Practical Guide For Clinicians FREE TO VIEW

Douglas W. Mapel, MD, MPH, Medical Director
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Lovelace Respiratory Research Institut, Albuquerque, NM

Chest. 2002;121(1):311-312. doi:10.1378/chest.121.1.311-b
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By Charles C. Miller III, Michael J. Reardon, and Hazim J. Safi. Cambridge, UK: Cambridge University Press, 2001; 174 pp; $37.95

As pressure is increased to control health-care costs while maintaining quality, more and more physicians will be held to measurable performance standards. Thoracic surgeons already are closely scrutinized for their complication rates, and one who incurs a high rate of adverse events runs the risk of external audits, denied reimbursement, and public humiliation. Fortunately, even most laypersons recognize that some patients have a higher risk of complications than others; factors such as advanced age, chronic illness, and disease severity obviously increase the probability of failure. Risk stratification is the science of describing the quality of medical care while adjusting for the prevalence of prognostically important risk factors in the treatment population, also known as the case-mix.

Dr. Miller, a statistician, and Drs. Reardon and Safi, both thoracic surgeons, have written this book to be an overview of the process of risk stratification. This is not limited to a discussion of statistical software or logistical regression modeling. The authors have done a good job of reviewing the major factors that are vital to the success of risk-stratification projects, including variable selection, data collection, and the selection of appropriate reference populations. They also demonstrate how risk stratification, which is focused on estimating event rates, differs from most clinical analyses, which are usually focused on treatment efficacy or disease causality. In risk stratification, it is the provider or the patient population that is being assessed, not the treatment. Almost all of the examples and figures in the text are drawn from the literature on coronary artery bypass complication rates, although the methods discussed are applicable to just about any intervention and medical outcome.

The authors attempted to “write a book that is both detailed and flexible enough to provide multiple levels of information for several types of reader.” Unfortunately, one who tries to be everything to everyone runs the risk of satisfying few. Readers who do not have a working knowledge of statistics and multivariate modeling techniques are likely to find much of the text difficult to grasp. On the other hand, for those who have experience with risk modeling, there is little discussion on advanced topics such as colinearity, censoring, and time-dependent data. Nevertheless, this book will serve as a very good overview on this topic for persons with some experience in clinical research, and a useful guide for those who wish to implement risk-stratification techniques in their quality-improvement programs.




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