Commentary on “Developing a national strategy to prevent dementia: Leon Thal Symposium 2009.” Dementia risk indices: A framework for identifying individuals with a high dementia risk

There is growing consensus in the field of Alzheimer's disease (AD) research that it may be difficult to develop drugs that can reverse the neuronal damage caused by the disease once symptoms are severe enough to be diagnosable. Therefore, the goal has shifted toward identification of individuals earlier in the disease process, when symptoms are very mild, as well as identification of asymptomatic, high-risk individuals so that they can be targeted for prevention and early intervention. In other fields, risk indices are often used to identify individuals who are asymptomatic but high-risk. Risk indices, also known as prognostic models, are tools that are used to predict the likelihood that an individual will experience a given event within a given time frame. Usually, this is accomplished by combining information from several different individual risk factors into a summary score. The prognostic accuracy of risk indices is typically assessed based on the area under the receiver operating characteristic (ROC) curve, also known as the c statistic [1]. The ROC curve is a graph of the true-positive rate (sensitivity) by the false-positive rate (1-specificity), and it is related to the relative probability that in all possible pairs of subjects in which one had the outcome and one didn't, the one with the outcome would receive a higher risk score. The c statistic may range from 0 to 1, with 0.5 reflecting predictive accuracy no better than chance and 1 reflecting perfect discrimination. The Framingham Heart Index is one of the most commonly used risk indices. It uses a combination of age, sex, blood pressure, cholesterol, and smoking status to identify individuals who are currently free of overt coronary heart disease but, based on their risk factor profile, have a high risk of experiencing a major coronary event within 10 years (c statistic, 0.79) [2,3]. Similarly, the Breast Cancer Risk Assessment Tool uses information about a woman's age, race, reproductive history, family history of breast cancer, and biopsy history to predict her risk of developing invasive breast cancer within five years (c statistic, 0.58) [4,5]. Risk indices also are available to predict risk of mortality [6,7], disability [8], nursing home placement [9], and diabetes [10,11], among many other conditions and disorders.