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- May 12, 2008 |
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CME on Diabetes is a website built to transmit top-level CME conferences given by international experts in endocrinology, insulin resistance, prediabetes, metabolic syndrome and type 2 diabetes. More than 2.6 million slides have been viewed since the website launch. Thank you for your continued support and commitment!
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"Statistical Models for Immediate Classification of Gluco-Metabolic State in Patients with Coronary Artery Disease"Prof. John Öhrvik (biography)
English - 2005-04-16 - 26 minutes
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Summary :
OBJECTIVES: Abnormal glucose regulation (AbnGR) has serious prognostic implications in patients with coronary artery disease (CAD). This study tried to develop classification criteria, based on easily available clinical data, useful in acute CAD and to limit the number of oral glucose tolerance tests (OGTT) needed for accurate characterization.
METHODS: CAD patients were enrolled at 110 centers in 25 countries. Fasting plasma glucose (FPG) and 2-h post load glucose were obtained in patients without known AbnGR. The gluco-metabolic status was classified according to WHO as normal, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) or diabetes and compared with ADA criteria (normal FPG <5.5 mmol/l). Classification methods, ordinal logistic regression and a single hidden layer neural network (NNET), were assessed to find rules to characterize the metabolic status.
RESULTS: OGTT was performed in 1 867 patients. They were classified as normal (870), IFG or IGT (678) and diabetes (319). Many patients with newly detected diabetes (86) and IGT (357) had been missed with the ADA criterion. The best classification algorithm for gluco-metabolic status, a NNET with three neurons in the hidden layer, using FPG, age and HDL-cholesterol as input variables reached a 96% diagnostic accuracy concerning specificity,i.e. almost everyone classified as having IFG, IGT or diabetes had AbnGR.
CONCLUSIONS: An OGTT should become a diagnostic routine for evaluation of total risk in all CAD patients classified as having NGR applying the classification algorithm based on the NNET.
Learning objectives :
After viewing this presentation, participants will be able to discuss:
- Characterization of glucose metabolic state using ordinal logistic regression;
- Characterization of glucose metabolic state using single hidden layer back propagation neural network;
- The clinical implications of the proposed statistical model;
- The need for an OGTT immediately after a cardiovascular event.
Bibliographic references :
Malgorzata Bartnik, Lars Rydén, Roberto Ferrari, Klas Malmberg, Kalevi Pyörälä, Maarten Simoons, Eberhard Standl, Jordi Soler-Soler, John Öhrvik on behalf of the Euro Heart Survey Investigators The prevalence of abnormal glucose regulation in patients with coronary artery disease across Europe: The Euro Heart Survey on diabetes and the heart Eur. Heart J., November 2004; 25: 1880 - 1890
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