Validation of Prediction of Diabetes by the Archimedes Model and Comparison With Other Predicting Models

Abstract

OBJECTIVE—To validate the ability of the Archimedes model to accurately predict the risk of developing diabetes in individuals. RESEARCH DESIGN AND METHODS—Subjects were randomly selected from the San Antonio Heart Study population. The area under the receiver operating characteristic (aROC) curve derived from the Archimedes model was calculated and also compared with the aROCs from two published multiple logistic regression models designed to estimate diabetes risk. RESULTS—The aROC for the Archimedes model was 0.818 (95% CI 0.739–0.899) compared with aROCs of 0.869 (0.801–0.936) and 0.870 (0.802–0.937) for the two logistic regression models, respectively. Risk estimates from the logistic models were highly correlated with the estimates derived from the Archimedes model. CONCLUSIONS—The Archimedes model predicts individual diabetes risk with a high level of sensitivity and specificity, comparable with that of models designed specifically for that purpose. Unlike the latter models, Archimedes also predicts the risk of numerous other health outcomes.