Validation of a Pretransplant Risk Score for New-Onset Diabetes After Kidney Transplantation

Abstract

OBJECTIVE Identification of patients at high risk for new-onset diabetes after kidney transplantation (NODAT) will facilitate clinical trials for its prevention. RESEARCH DESIGN AND METHODS We previously described a pretransplant predictive risk model for NODAT using seven pretransplant variables (age, planned use of maintenance corticosteroids, prescription for gout medicine, BMI, fasting glucose, fasting triglycerides, and family history of diabetes). We have now applied the initial model to a cohort of 474 transplant recipients from another center for validation. We performed two analyses in the validation cohort. The first was a standard model with variables derived from the original study. The second was a summary score model, in which the sum of dichotomized variables (all the variables dichotomized at clinically relevant cut points) was used to categorize, individuals into low (0–1), intermediate (2, 3), or high (4–7) risk groups. We also conducted a combined database analyses, merging the initial and validation cohorts (n = 792) to obtain better estimates for a prediction equation. RESULTS Although the frequency of several risk factors differed significantly between the two cohorts, the models performed similarly in each cohort. Using the summary score model, incidences of NODAT in low-risk, medium-risk, and high-risk groups in the initial cohort were 12, 29, and 56%, and in the validation cohort incidences were 11, 29, and 51%. CONCLUSIONS A pretransplant model for NODAT, including many type 2 diabetes risk factors, predicted NODAT in the validation cohort.