The SAHS is a prospective cohort study consisting of 3,682 individuals (62% Mexican American and 38% non-Hispanic white) followed for 7–8 years (4
). The SAHS predicting model is a multiple logistic regression model with incident diabetes as the dependent variable and a panel of baseline characteristics that are ordinarily available in a routine clinical setting as independent variables (2
). The ARIC predicting model is a similarly constructed logistic regression model (3
The Archimedes model is built from underlying anatomy and physiology and uses scores of ordinary and differential equations to represent metabolic pathways, occurrence and progression of diseases, signs and symptoms, treatments, and outcomes. A practical, free, readily available tool derived from the Archimedes model is the American Diabetes Association's Diabetes PHD (Personal Health Decisions; available at http://diabetes.org/diabetesPHD
). Diabetes PHD can simultaneously predict the risk of diabetes and numerous other outcomes, including the effects of a wide variety of treatments in many different populations (e.g., those with diabetes). It was used here to provide external validation of its prediction of the incidence of diabetes.
Among the 3,228 individuals in the SAHS who were nondiabetic at baseline, 295 developed diabetes over the 7–8 years of follow-up. All the required elements for the Archimedes risk estimation were available in the subjects selected for the present analyses. The present analyses were restricted to the recent cohort 2 of SAHS, which included 1,734 nondiabetic individuals, 195 of whom were diabetic at follow-up. Within the SAHS database, we selected 100 individuals at random, 50 of whom were diabetic at follow-up and 50 who remained free of diabetes at follow-up. This sample size would provide 80% power to detect an aROC significantly (P
< 0.05) greater than 0.70 (the low end of acceptable discrimination [5
]) if the true aROC was >0.80 and 90% power if the true aROC was 0.83 (benchmark values near that of other established models) (2,3).
The risk of developing diabetes for each individual was determined according to the years of follow-up for that individual (rounded to the nearest year), which ranged from 6–9 with a mean of 7.5. Data from each individual were entered into Diabetes PHD and the results obtained from the graphical output displayed on the computer screen. A second person confirmed the accuracy of the input and, in a random sample of 20 forms, also confirmed the output from Diabetes PHD.
We also estimated the risk of diabetes for the same 100 individuals using both the SAHS diabetes predicting model and the ARIC predicting model. The aROC's and CIs for all three models were computed and compared (6
). Finally, we computed the Spearman correlation coefficients between the risk estimates obtained from each pair of predicting models.