Predictive modeling for incident and prevalent diabetes risk evaluation

Katya L. Masconi, Justin Basile Echouffo-Tcheugui, Tandi E. Matsha, Rajiv T. Erasmus, Andre Pascal Kengne

Research output: Contribution to journalReview articlepeer-review

Abstract

With half of individuals with diabetes undiagnosed worldwide and a projected 55% increase of the population with diabetes by 2035, the identification of undiagnosed and high-risk individuals is imperative. Multivariable diabetes risk prediction models have gained popularity during the past two decades. These have been shown to predict incident or prevalent diabetes through a simple and affordable risk scoring system accurately. Their development requires cohort or cross-sectional type studies with a variable combination, number and definition of included risk factors, with their performance chiefly measured by discrimination and calibration. Models can be used in clinical and public health settings. However, the impact of their use on outcomes in real-world settings needs to be evaluated before widespread implementation.

Original languageEnglish (US)
Pages (from-to)277-284
Number of pages8
JournalExpert Review of Endocrinology and Metabolism
Volume10
Issue number3
DOIs
StatePublished - May 1 2015
Externally publishedYes

Keywords

  • diabetes
  • incident
  • prevalent
  • risk prediction
  • screening

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism

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