The rational and efficient application of effective treatment to those who derive the most benefit is an inherent attribute of high-quality health care.1 Yet how are physicians to accomplish this efficient and effective use of treatment within the frenetic pace of patient care? Accomplishing these aims requires adherence to a basic tenet of clinical epidemiology, that for an intervention with a given relative risk reduction, the absolute benefits are greatest in those with the greatest underlying risk.2 For example, if the use of statin therapy in patients with coronary artery disease results in a 30% relative risk reduction in death and the underlying risk of mortality is 20%, then the absolute risk reduction is 6 deaths for each 100 patients treated and the number of patients who need to be treated to save 1 life is 17. Conversely, if the underlying mortality rate is 2%, then the number of patients who need to be treated to save a life is 167. Obviously, much more aggressive treatment is warranted in higher-risk patients, since fewer need to be treated to save a life. Moreover, because all therapies are associated with some degree of risk, the benefits are more likely to outweigh those risks in the patients with the greatest underlying potential to benefit from treatment. Among the most established applications of this clinical logic is the use of bypass surgery, for which patients with the greatest risk for death (eg, those with left main coronary disease or triple-vessel disease with left ventricular dysfunction) are recommended for treatment.3- 5 Consequently, outcomes researchers have developed numerous techniques for risk stratification to assist clinicians in identifying high-risk patients for whom more aggressive use of treatments can be considered.
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