Bethel, M. A, Hyland, K. A, Chacra, A. R, Deedwania, P., Fulcher, G. R, Holman, R. R, Jenssen, T., Levitt, N. S, McMurray, J. J, Boutati, E., Thomas, L., Sun, J. & Haffner, SM. (2017). Updated risk factors should be used to predict development of diabetes. Journal of Diabetes and its Complications,31(5), 859-863. United States: Elsevier Inc.. Retrieved from https://doi.org/10.1016/j.jdiacomp.2017.02.012
Aims: Predicting incident diabetes could inform treatment strategies for diabetes prevention, but the incremental benefit of recalculating risk using updated risk factors is unknown. We used baseline and 1-year data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) Trial to compare diabetes risk prediction using historical or updated clinical information. Methods: Among non-diabetic participants reaching 1 year of follow-up in NAVIGATOR, we compared the performance of the published baseline diabetes risk model with a “landmark” model incorporating risk factors updated at the 1-year time point. The C-statistic was used to compare model discrimination and reclassification analyses to demonstrate the relative accuracy of diabetes prediction. Results: A total of 7527 participants remained non-diabetic at 1 year, and 2375 developed diabetes during a median of 4 years of follow-up. The C-statistic for the landmark model was higher (0.73 [95% CI 0.72–0.74]) than for the baseline model (0.67 [95% CI 0.66–0.68]). The landmark model improved classification to modest ( < 20%), moderate (20%–40%), and high ( > 40%) 4-year risk, with a net reclassification index of 0.14 (95% CI 0.10–0.16) and an integrated discrimination index of 0.01 (95% CI 0.003–0.013). Conclusions: Using historical clinical values to calculate diabetes risk reduces the accuracy of prediction. Diabetes risk calculations should be routinely updated to inform discussions about diabetes prevention at both the patient and population health levels.
Mary MacKillop Institute for Health Research
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