Document Detail


Decision trees as a simple-to-use and reliable tool to identify individuals with impaired glucose metabolism or type 2 diabetes mellitus.
MedLine Citation:
PMID:  20693184     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
OBJECTIVE: The prevalence of unknown impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or type 2 diabetes mellitus (T2DM) is high. Numerous studies demonstrated that IFG, IGT, or T2DM are associated with increased cardiovascular risk, therefore an improved identification strategy would be desirable. The objective of this study was to create a simple and reliable tool to identify individuals with impaired glucose metabolism (IGM). DESIGN AND METHODS: A cohort of 1737 individuals (1055 controls, 682 with previously unknown IGM) was screened by 75 g oral glucose tolerance test (OGTT). Supervised machine learning was used to automatically generate decision trees to identify individuals with IGM. To evaluate the accuracy of identification, a tenfold cross-validation was performed. Resulting trees were subsequently re-evaluated in a second, independent cohort of 1998 individuals (1253 controls, 745 unknown IGM). RESULTS: A clinical decision tree included age and systolic blood pressure (sensitivity 89.3%, specificity 37.4%, and positive predictive value (PPV) 48.0%), while a tree based on clinical and laboratory data included fasting glucose and systolic blood pressure (sensitivity 89.7%, specificity 54.6%, and PPV 56.2%). The inclusion of additional parameters did not improve test quality. The external validation approach confirmed the presented decision trees. CONCLUSION: We proposed a simple tool to identify individuals with existing IGM. From a practical perspective, fasting blood glucose and blood pressure measurements should be regularly measured in all individuals presenting in outpatient clinics. An OGTT appears to be useful only if the subjects are older than 48 years or show abnormalities in fasting glucose or blood pressure.
Authors:
Manuela Hische; Olga Luis-Dominguez; Andreas F H Pfeiffer; Peter E Schwarz; Joachim Selbig; Joachim Spranger
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2010-08-06
Journal Detail:
Title:  European journal of endocrinology / European Federation of Endocrine Societies     Volume:  163     ISSN:  1479-683X     ISO Abbreviation:  Eur. J. Endocrinol.     Publication Date:  2010 Oct 
Date Detail:
Created Date:  2010-09-17     Completed Date:  2010-10-06     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9423848     Medline TA:  Eur J Endocrinol     Country:  England    
Other Details:
Languages:  eng     Pagination:  565-71     Citation Subset:  IM    
Affiliation:
Department of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, Berlin, Germany.
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MeSH Terms
Descriptor/Qualifier:
Adult
Aged
Artificial Intelligence*
Cross-Sectional Studies
Decision Support Techniques*
Diabetes Mellitus, Type 2 / diagnosis*
Female
Glucose Intolerance / diagnosis*
Humans
Male
Middle Aged

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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