Predictive modeling for incident and prevalent diabetes risk evaluation.

MedStar author(s):
Citation: Expert Review of Endocrinology & Metabolism. 10(3):277-284, 2015 May.PMID: 30298773Institution: Medstar Union Memorial HospitalForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: PubMed-not-MEDLINE -- Not indexedYear: 2015ISSN:
  • 1744-6651
Name of journal: Expert review of endocrinology & metabolismAbstract: 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.All authors: Echouffo-Tcheugui JB, Erasmus RT, Kengne AP, Masconi KL, Matsha TEFiscal year: FY2015Digital Object Identifier: Date added to catalog: 2018-11-02
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Journal Article MedStar Authors Catalog Article 30298773 Available 30298773

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.

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