Literature DB >> 22407958

Survey of diabetes risk assessment tools: concepts, structure and performance.

Thitaporn Thoopputra1, David Newby, Jennifer Schneider, Shu Chuen Li.   

Abstract

The objective of this study is to review the effectiveness and limitations of existing diabetes risk screening tools to assess the need for further developing of such tools. An electronic search of the EMBASE, MEDLINE, and Cochrane library supplemented by a manual search was performed from 1995-2010. The search retrieved a total of 2168 articles reporting diabetes risk assessment tools which, after culling, produced 41 tools developed in 22 countries, with the majority (n = 26) developed in North America and Europe. All are short questionnaires of 2-16 questions incorporating common variables including age, gender, waist circumference, BMI, family history of diabetes, history of hypertension or antihypertensive medications. While scoring format and cut-offs point are diverse between questionnaires, overall accuracy value range of 40-97%, 24-86% and 62-87% were reported for sensitivity, specificity and receiver operating characteristic curve respectively. In summary, there is a trend of increasing availability of diabetes prediction tools with the existing risk assessment tools being generally a short questionnaire aiming for ease of use in clinical practice. The overall performance of existing tools showed moderate to high accuracy in their predictive performance. However, further detailed comparison of existing questionnaires is needed to evaluate whether they can serve adequately as diabetes risk assessment tool in clinical practice.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2012        PMID: 22407958     DOI: 10.1002/dmrr.2296

Source DB:  PubMed          Journal:  Diabetes Metab Res Rev        ISSN: 1520-7552            Impact factor:   4.876


  5 in total

1.  Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults.

Authors:  Xiao-Lu Xiong; Rong-Xin Zhang; Yan Bi; Wei-Hong Zhou; Yun Yu; Da-Long Zhu
Journal:  Curr Med Sci       Date:  2019-07-25

2.  Comparison of 3 risk factor-based screening tools for the identification of prediabetes.

Authors:  Courtney E Gamston; Anna N Kirby; Richard A Hansen; David T Redden; Heather P Whitley; Courtney Hanson; Kimberly B Lloyd
Journal:  J Am Pharm Assoc (2003)       Date:  2019-12-27

3.  Predicting youth diabetes risk using NHANES data and machine learning.

Authors:  Nita Vangeepuram; Bian Liu; Po-Hsiang Chiu; Linhua Wang; Gaurav Pandey
Journal:  Sci Rep       Date:  2021-05-27       Impact factor: 4.379

4.  A risk assessment model for type 2 diabetes in Chinese.

Authors:  Senlin Luo; Longfei Han; Ping Zeng; Feng Chen; Limin Pan; Shu Wang; Tiemei Zhang
Journal:  PLoS One       Date:  2014-08-07       Impact factor: 3.240

5.  Development and Validation of a Simple Risk Score for Undiagnosed Type 2 Diabetes in a Resource-Constrained Setting.

Authors:  Antonio Bernabe-Ortiz; Liam Smeeth; Robert H Gilman; Jose R Sanchez-Abanto; William Checkley; J Jaime Miranda; Cronicas Cohort Study Group
Journal:  J Diabetes Res       Date:  2016-09-04       Impact factor: 4.011

  5 in total

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