Physicians often recommend mastectomy, a major surgical procedure, to some women without breast cancer, such as a young woman positive for BRCA2, but they advise a very minor procedure, excisional biopsy, for some women with active cancer, such as an older woman with comorbid conditions and carcinoma in situ. The reason is prediction: We predict that the BRCA2-positive woman is at high risk for dying of breast cancer, even though cancer has not been diagnosed; conversely, the older woman is at low risk for breast cancer morbidity or mortality.
We propose that thinking about disease in terms of risk prediction is often superior to thinking about disease in terms of diagnosis. The diagnostic approach to blood pressure is to divide the population into 2 groups, those with hypertension and those without hypertension, and then to treat one group but not the other. The prediction alternative is to use a statistical model to estimate the probability that a patient will have a clinically important event, such as a myocardial infarction, within a certain period, such as 10 years. Blood pressure would be one of the predictors in this particular model; others might include cholesterol, diabetes, age, sex, and smoking history. One could then compare the risk predictions at the patients’ current blood pressure and then assume some reduction in blood pressure associated with treatment. Typically, one might find that a younger man with few risk factors other than a systolic blood pressure of 145 mm Hg is at very low risk for a serious cardiac event, and his level of risk is barely affected by a change in blood pressure. Conversely, an older male smoker with high cholesterol and a similar blood pressure is at high risk, and he would have substantially decreased risk if blood pressure could be reduced.
What we are describing is the statistical model developed on the basis of the Framingham Heart Study (sometimes known as the Framingham Risk Calculator). This shows that prediction modeling can be implemented readily by using available data and technology. Indeed, many guidelines for the treatment of hyperlipidemia incorporate risk prediction, mandating more aggressive control of lipid levels in patients at predicted higher risk (4
Prediction models have 2 particular advantages over our standard way of thinking about diagnosis. First, traditional cut-points are invariable to patient preference. For example, we might think of using a higher-than-usual cut-off for a patient who had troublesome side effects from treatment. But how does one choose an appropriate value of, for example, blood pressure in light of the side effects? A prediction model provides probabilities of events, and a patient can weigh these according to his or her preferences: It makes sense to ask patients whether they would accept treatment for a 2% vs. a 4% absolute reduction in the risk for a cardiovascular event, but not whether 150 or 160 mm Hg is a more appropriate treatment threshold given poor drug tolerance.
Second, prediction models can incorporate multiple patient characteristics. A patient with high blood pressure benefits more from control of cholesterol than does a patient with normal blood pressure. For prostate cancer, the situation is reversed, with the patient at high risk for cardiovascular death less likely to benefit from prostatectomy, because he is more likely to die before his cancer progresses sufficiently to affect his survival or quality of life.
This point is likely to become more important with the development of molecular and genomic markers in the next few years. Physicians should presumably be more aggressive in treating blood pressure in a patient at high genomic risk for myocardial infarction and less aggressive if some other marker, such as one for inflammation, was favorable. It is difficult to know how to incorporate such markers into a diagnosis without making diagnostic sub-categories exponentially more complex as each new marker becomes available. Conversely, adding a new marker to a multivariable model makes little difference to the clinical consultation.
The risk prediction approach is not new to the practice of clinical medicine. Physicians have traditionally called on multiple variables to risk-stratify patients in the clinic, usually weighting each variable on the basis of their clinical experience and judgment. For example, a patient with a mildly elevated cholesterol level might be recommended dietary discretion and exercise, whereas the same patient might be prescribed a statin if there was a family history of heart disease. Moreover, many of the diseases discussed now include some measure of risk stratification, such as “prediabetes,” different “stages” of hypertension, and over-weight/obese/morbidly obese categories. The use of prediction models, however, adds a quantitative estimate to general risk groupings and to physicians’ informal processes of risk adjustment. The models guide the physician to the variables that should have the greatest influence on management choices. Furthermore, prediction models give physicians explicit information to use in shared decision making with patients, such as the risk for heart attack with or without treatment.