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Conference Paper: Simple non-laboratory-based and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus

TitleSimple non-laboratory-based and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus
Authors
Wong, CKHSiu, SCWan, YFJiao, FYu, EYTFung, CSCWong, KWLam, CLK
KeywordsMedical sciences
Endocrinology
Issue Date2014
PublisherElsevier Ireland Ltd. The Journal's web site is located at http://www.elsevier.com/locate/diabres
Citation
The 10th International Diabetes Federation-Western Pacific Regions Congress (IDF-WPR) and the 6th Asian Association for the Study of Diabetes (AASD) Scientific Meeting, Singapore, 21–24 November 2014. Diabetes Research and Clinical Practice, 2014, v. 106 suppl. 1, p. S103, abstract no. PO114 How to Cite?
DOI: http://dx.doi.org/10.1016/S0168-8227(14)70408-5
AbstractBACKGROUND: Early detection for undiagnosed diabetes mellitus (DM), through routine screening periodically, is critical to prevent or delay severe diabetes-related complications. In order to classify high-risk subjects for DM screening, risk algorithms for undiagnosed DM detection have been richly developed and validated in diverse populations and health care settings. However, the majority of risk algorithms developed within Chinese population were developed and validated in low income setting. Furthermore, there are no nomograms for the use in detecting undiagnosed DM, of which are simple-to-use graphical tool to guide decision-making in both routine clinical practice and community setting. The purpose of this study was to develop simple a nomogram to predict the risk of undiagnosed DM for use in asymptomatic general population, based on non-laboratory-based ...
DescriptionThis journal suppl. entitled: Abstracts of the 10th International Diabetes Federation–Western Pacific Region Congress and the 6th AASD Scientific Meeting
Persistent Identifierhttp://hdl.handle.net/10722/206897
ISSN
0168-8227
2021 Impact Factor: 8.180
2020 SCImago Journal Rankings: 1.605
ISI Accession Number ID
WOS:000361124300203

 

DC FieldValueLanguage
dc.contributor.authorWong, CKH-
dc.contributor.authorSiu, SC-
dc.contributor.authorWan, YF-
dc.contributor.authorJiao, F-
dc.contributor.authorYu, EYT-
dc.contributor.authorFung, CSC-
dc.contributor.authorWong, KW-
dc.contributor.authorLam, CLK-
dc.date.accessioned2014-12-02T12:00:41Z-
dc.date.available2014-12-02T12:00:41Z-
dc.date.issued2014-
dc.identifier.citationThe 10th International Diabetes Federation-Western Pacific Regions Congress (IDF-WPR) and the 6th Asian Association for the Study of Diabetes (AASD) Scientific Meeting, Singapore, 21–24 November 2014. Diabetes Research and Clinical Practice, 2014, v. 106 suppl. 1, p. S103, abstract no. PO114-
dc.identifier.issn0168-8227-
dc.identifier.urihttp://hdl.handle.net/10722/206897-
dc.descriptionThis journal suppl. entitled: Abstracts of the 10th International Diabetes Federation–Western Pacific Region Congress and the 6th AASD Scientific Meeting-
dc.description.abstractBACKGROUND: Early detection for undiagnosed diabetes mellitus (DM), through routine screening periodically, is critical to prevent or delay severe diabetes-related complications. In order to classify high-risk subjects for DM screening, risk algorithms for undiagnosed DM detection have been richly developed and validated in diverse populations and health care settings. However, the majority of risk algorithms developed within Chinese population were developed and validated in low income setting. Furthermore, there are no nomograms for the use in detecting undiagnosed DM, of which are simple-to-use graphical tool to guide decision-making in both routine clinical practice and community setting. The purpose of this study was to develop simple a nomogram to predict the risk of undiagnosed DM for use in asymptomatic general population, based on non-laboratory-based ...-
dc.languageeng-
dc.publisherElsevier Ireland Ltd. The Journal's web site is located at http://www.elsevier.com/locate/diabres-
dc.relation.ispartofDiabetes Research and Clinical Practice-
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Diabetes Research and Clinical Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Diabetes Research and Clinical Practice, vol. 106 suppl. 1, Nov. 2014. DOI: 10.1016/S0168-8227(14)70408-5-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectMedical sciences-
dc.subjectEndocrinology-
dc.titleSimple non-laboratory-based and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus-
dc.typeConference_Paper-
dc.identifier.emailWong, CKH: carlosho@hku.hk-
dc.identifier.emailWan, YF: yfwan@hku.hk-
dc.identifier.emailYu, EYT: ytyu@hku.hk-
dc.identifier.emailFung, CSC: cfsc@hku.hk-
dc.identifier.emailLam, CLK: clklam@hku.hk-
dc.identifier.authorityWong, CKH=rp01931-
dc.identifier.authorityYu, EYT=rp01693-
dc.identifier.authorityFung, CSC=rp01330-
dc.identifier.authorityLam, CLK=rp00350-
dc.description.naturepostprint-
dc.identifier.doi10.1016/S0168-8227(14)70408-5-
dc.identifier.hkuros241667-
dc.identifier.hkuros241668-
dc.identifier.volume106-
dc.identifier.issuesuppl. 1-
dc.identifier.spageS103, abstract no. PO114-
dc.identifier.epageS103, abstract no. PO114-
dc.identifier.isiWOS:000361124300203-
dc.publisher.placeIreland-
dc.identifier.issnl0168-8227-

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