Risk Factors, Risk Prediction, and the Apolipoprotein BApolipoprotein A-I Ratio

In this issue, van der Steeg and colleagues report the results of a nested casecontrol study demonstrating that the apolipoprotein Bapolipoprotein A-I (apo Bapo A-I) ratio is a risk factor for future coronary events, independent of low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, and other traditional risk factors (1). The study also suggests that the apo Bapo A-I ratio negligibly improves overall risk prediction compared with conventional coronary risk factors and the Framingham risk score. This latter finding contradicts an emerging literature that enthusiastically endorses the apo Bapo A-I ratio as an improved measure of risk (25). It is a good reminder that individual risk factors for important diseases are rarely useful for risk prediction and clinical decision making. We review the reasons why. The risk factor approach to medicine emerged in an era when chronic diseases replaced infectious diseases as the largest burden to the public's health (6, 7); the term risk factor first appeared in the medical literature in Annals of Internal Medicine in 1961 (8). Whereas infectious diseases have only 1 necessary external causal agent, chronic medical diseases have many contributing causes and result from complex interactions between genetic factors and environmental exposures over years. Risk factors are measures of these many contributors. Identification of new risk factors is useful because however weak the association, they may point to novel causal pathways, to vulnerable subgroups, or to new targets for intervention. If risk factors are widely distributed in the population, they may still contribute to a significant number of cases of disease, and new and established risk factors together may determine the risk for disease. Any clinician knows, however, that an individual risk factor (such as an elevated low-density lipoprotein cholesterol level) does little to distinguish patients who will develop an adverse outcome (such as cardiovascular disease) from those who will not. Predicting risk in this way requires the integration of information from multiple risk factors, given the multifactorial nature of chronic disease. The same statistical regression equations used to identify risk factors can be used to provide a summary measure of risk conveyed by multiple risk factors. For clinical purposes, a regression equation can be used to predict the probability of disease for specific patients (9), or it can be broken into its components and translated into points, so that an individual patient gains or loses points on the basis of the presence of risk or protective factors (10). The overall risk can then be summarized as a risk score, the sum of the points across many exposures. It is often sufficient for clinical practice if risk prediction models or scores assign participants to broad risk categories (for example, high, medium, and low risk), so that more aggressive interventions can be targeted to the highest-risk patients, who are more likely to derive a larger absolute benefit and to have a larger riskbenefit ratio. Any single risk factor or combination of factors can be used to create a risk prediction equation or score, but the predictions may not necessarily be accurate. To be accurate, the equation must be able to distinguish patients who will and will not develop the adverse outcome (that is, it must have good discrimination) and its predicted probabilities should closely match observed probabilities (that is, it must be well calibrated) (11, 12). Discrimination measures how accurately the risk prediction equation or score distinguishes people who will develop disease from those who will not. For the equation or score to have good discrimination, it should classify patients who do go on to develop the disease as having a higher risk for disease than those who do not. The overall ability to distinguish people without disease from those with disease is typically described by the concordance or c-statistic, which is equivalent to the area under the receiver-operator characteristic curve (AUC). The c-statistic and the AUC represent the probability that for a randomly chosen pair of participants, 1 of whom who will develop the disease (case-patient) and 1 of whom will not (noncase-patient), the case-patient will have a higher predicted risk than the noncase-patient. The c-statistic or AUC for the Framingham risk equation is typically greater than 70% (13, 14). Calibration refers to how close the probabilities predicted by the risk equation are to the observed probabilities of disease. Calibration is typically evaluated by using a measure that summarizes how closely the predicted and the observed risks agree within each decile of predicted risk (the HosmerLemeshow statistic) (15). High values of the statistic with low accompanying P values (close to 0) occur when observed and predicted probabilities are far apart in many of the deciles of risk, indicating poor calibration. Because it is common to want to predict the risks in populations with overall probabilities of disease that are very different from the population in which the prediction equations or scores were developed, researchers must often recalibrate the equation or score to match the overall observed probabilities of disease in the target population. The Framingham risk equation, for instance, has been successfully used in diverse populations worldwide, but because baseline risk for coronary artery disease almost always differs by population and affects predicted risk, the equation requires recalibration by population to be accurate (13, 14). Recalibration does not affect the discrimination of a risk prediction equation. The selection of risk factors to be included in a risk prediction equation is often controversial, and it involves some arbitrary decisions about what level of improvement in discrimination and calibration is clinically relevant. In addition, the availability of assays to measure novel risk factors, the costs of those assays, and general considerations of parsimony influence the final choice of risk factors. Once a risk prediction equation is widely validated, however, the key question is how much the addition of a new risk factor (or the substitution of an established risk factor with a new one) improves prediction (16). The change in c-statistic, as a measure of how discrimination changes, provides one indication of that improvement. When van der Steeg and colleagues (1) substituted the apo Bapo A-I ratio for the total cholesterolhigh-density lipoprotein cholesterol ratio in their risk prediction models, the AUC changed from 0.670 to 0.673, a negligible difference in discrimination. It is well established that the association between the exposure and the outcome needs to be moderately strong (odds ratio> 3.0) for the addition of a new risk factor to meaningfully improve overall discrimination (17, 18). In the epidemiology of chronic disease, risk factors are rarely associated with such high odds ratios. For that reason, most new risk factors add little value to existing risk prediction equations. An alternative, more clinically accessible way to assess the value of adding a new risk factor to an existing risk equation or score is to assess the degree to which such an addition more accurately reclassifies patients within risk categories compared with established risk factors (19, 20). As with discrimination, the assessment of how many patients need to be reclassified for the novel risk factor to be deemed incrementally useful also involves subjective judgment. Although van der Steeg and colleagues showed that substituting the apo Bapo A-I ratio for the total cholesterolhigh-density lipoprotein cholesterol ratio reclassified about 10% of their sample, about 40% of reclassified case-patients were inaccurately reclassified into lower risk categories, and about 50% of reclassified noncase-patients were inaccurately reclassified into higher risk categories. Admittedly, the meaning of these inaccurate reclassifications is unclear. It is difficult to translate these categories into absolute risk categories because of the nested casecontrol design, and it is therefore difficult to say how many patients' risk management would have changed because of reclassification. In summary, van der Steeg and colleagues show that the apo Bapo A-I ratio does not improve overall prediction as measured by the AUC in a general population. It is still possible, as the authors acknowledge, that measurement of this ratio is important in certain subgroups. Thus, their findings do not negate the possibility that the ratio may have a future clinical role to play in patient subsets, or that apolipoprotein levels may play an important role in the etiology of cardiovascular disease (5). They remind us, however, that newer risk factors do not often add clinically relevant value to established risk measuresnotwithstanding frequent claims in medical journals and the lay media of exciting new risk factor discoveriesand that the added value of any new risk factor must be rigorously demonstrated before widespread testing for the factor can be recommended for routine clinical practice. When testing for novel risk factors is adopted in the absence of such a demonstration, patients undergo tests they may not need. Misclassification to a higher risk category may make patients anxious and subject them to counseling and treatment they do not need, or conversely, patients may be falsely reassured by misclassification to a lower risk category and not receive counseling and treatment that they do need. In addition, health care costs grow. In simpler terms, risk factor proliferation puts patients and clinicians at risk for confusion, and demonstration of improved prediction is one means of prevention.