Risk assessment is applied to many different clinical outcomes and in many distinct clinical domains. For instance, accurate prediction of procedural risk related to coronary artery revascularization, carotid stenting, or pacemaker implantation is important both to the patient deciding whether to undergo the procedure and to those interested in quality assessment of these procedures based on risk-adjusted outcomes. Similarly, accurate risk assessment of the short-term outcomes of acute illnesses such as acute myocardial infarction is important for both clinical care and quality improvement. Risk assessment is also central to the use of preventive therapies, such as prevention of embolic stroke in the setting of atrial fibrillation, prevention of the development of coronary artery disease in adults with cardiac risk factors, or the prevention of sudden death in patients with cardiomyopathy. Although the specific outcome of interest may be quite different in various clinical domains, the same basic principles and methods apply to evaluation of risk markers and risk assessment methods in each domain.
Assessment of cardiovascular risk in individuals is an integral part of clinical decision making, especially for increasing the rational use of pharmaceutical-, procedure-, or device-based therapies. These individually targeted interventions are complementary to public health activities that aim to reduce the overall population risk of cardiovascular disease by the promotion of healthy behaviors related to diet, exercise, and avoidance of smoking.
Evaluation of a new risk marker or risk assessment method starts with a sound study design and a representative at-risk population. Cohort studies in which participants are followed up over time and outcomes are ascertained prospectively provide the best information about prognosis. The design and reporting of risk marker assessment studies should conform to generally accepted standards of clinical research, such as the recent “Strengthening the Reporting of Observational Studies in Epidemiology” guidelines.4
The outcome measure must be defined carefully, measured accurately, and ascertained completely to provide a reliable basis for the evaluation of any risk marker or risk assessment tool. The number of outcome events available for analysis can be increased by use of a composite end point (eg, either death, myocardial infarction, or ischemic stroke). Composite end points may complicate the assessment of risk markers, however, if the marker is more predictive of 1 component of the composite end point (eg, myocardial infarction) than of the others (eg, stroke, death). The number of events available for analysis can also be increased by longer follow-up, but this is appropriate only if the marker is associated with both short-term and long-term risk. For instance, elevated biomarkers of myocardial necrosis predict a higher short-term risk in acute coronary syndrome but may not necessarily predict long-term risk (ie, between 6 months and 5 years of follow-up).
Risk is estimated on the basis of the number of outcome events over an interval of time and conventionally summarized either with a survival curve or by reporting the proportion of events over a fixed time interval of interest (eg, 30 days or 1 year). The statistical association of a risk marker with outcome can then be tested with logistic regression (for a short, fixed follow-up interval) or with the Cox proportional hazards model or a parametric survival model (for a range of follow-up intervals or for longer follow-up). These models generally assume that the presence of a risk factor increases risk in a proportional fashion, which can be assessed by measures of statistical association such as the odds ratio, risk ratio, or hazard ratio. Multivariable statistical models then measure the extent to which underlying average risk in the population is modified by standard demographic factors (eg, age, sex, and race), established risk markers (eg, smoking, blood pressure or lipid levels, diabetes), and the novel risk marker. The use of these statistical models highlights several very important issues about estimation of risk.
The most basic requirement for a novel risk marker is that the association between the marker and the outcome of interest be statistically significant when tested as a predictor of future events. This test requires that the putative risk marker has been measured in a cohort of subjects with a sufficient number of documented outcome events to allow a reliable analysis of risk relationships. Critically, the statistical power of risk assessment depends on the number of outcome events available for analysis, not the number of subjects studied or the length of follow-up.
The next requirement for a novel risk marker is that it improves risk prediction beyond established risk markers; that is, new markers should provide incremental prognostic information. For example, in the primary prevention of coronary disease, it is recommended that established cardiac risk factors be assessed in all at-risk individuals, because they are easily measured by inexpensive and readily available tests that form the basis of accepted risk management strategies (eg, lipid and blood pressure treatments). The goal in the evaluation of a new risk marker for use in primary prevention is therefore to answer the question, “Does the new marker add significant predictive information beyond that provided by established cardiac risk factors?” For example, a new marker that truly indicated an individual’s biological age might be a better marker of cardiovascular risk than the individual’s chronological age. But this new marker would have to be statistically associated with outcome after the effect of chronological age has been accounted for in the risk model, because measurement of chronological age is simple, inexpensive, and readily available. In other words, a novel marker of biological age must provide incremental prognostic information to be of clinical value. When statistical procedures are used to test for incremental prognostic information, the new factor should be tested for significance only after all established risk factors have already been included in the model. In the example above, the test of interest is whether biological age adds significantly to a model that already includes chronological age, not whether biological age is chosen before chronological age in a stepwise variable-selection process.
More outcome events are needed to provide adequate statistical power for the test of whether a new risk marker adds prognostic information to established risk factors in a multivariable model than for the test of whether the new marker provides prognostic information by itself. Although the exact number of events needed depends on the strength of each risk marker and the degree of correlation between them, it would be difficult to test the value of the addition of a new marker to an established marker with only a few outcome events (eg, only 20 to 30) in the data set. If there are too few events to provide adequate statistical power, it would be unwarranted to declare that a new risk marker has no independent predictive value.