AI Article Synopsis

  • A study aimed to develop a risk score for predicting new-onset diabetes after kidney transplantation (NODAT) to aid in identifying subjects for intervention research.
  • The researchers analyzed clinical and lab data from 316 kidney transplant patients, focusing on seven pretransplant variables that showed potential for predicting NODAT.
  • The resulting risk score, based on these variables, was effective in identifying NODAT risk, performing comparably to more complex multivariate models.

Article Abstract

Objective: New-onset diabetes after kidney transplantation (NODAT) has adverse clinical and economic implications. A risk score for NODAT could help identify research subjects for intervention studies.

Research Design And Methods: We conducted a single-center retrospective cohort study using pretransplant clinical and laboratory measurements to construct a risk score for NODAT. NODAT was defined by hemoglobin A(1c) (HbA(1c)) ≥6.5%, fasting serum glucose ≥126 mg/dL, or prescribed therapy for diabetes within 1 year posttransplant. Three multivariate logistic regression models were constructed: 1) standard model, with both continuous and discrete variables; 2) dichotomous model, with continuous variables dichotomized at clinically relevant cut points; and 3) summary score defined as the sum of the points accrued using the terms from the dichotomous model.

Results: A total of 316 subjects had seven pretransplant variables with P < 0.10 in univariate logistic regression analyses (age, planned corticosteroid therapy posttransplant, prescription for gout medicine, BMI, fasting glucose and triglycerides, and family history of type 2 diabetes) that were selected for multivariate models. Areas under receiver operating curves for all three models were similar (0.72, 0.71, and 0.70). A simple risk score calculated as the sum of points from the seven variables performed as well as the other two models in identifying risk of NODAT.

Conclusions: A risk score computed from seven simple pretransplant variables can identify risk of NODAT.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3177751PMC
http://dx.doi.org/10.2337/dc11-0752DOI Listing

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