PowerAI-Diabetes: Review of glycemic and lipid variability to predict cardiovascular events in Chinese diabetic population

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Lee, Sharen, Liu, Tong, Chung, Cheuk To, Reinhold, Johannes, Vassiliou, Vassilios S. and Tse, Gary (2024) PowerAI-Diabetes: Review of glycemic and lipid variability to predict cardiovascular events in Chinese diabetic population. NPJ Metabolic Health and Disease, 2 (1). ISSN 2948-2828

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Abstract

The aim of this study is to review the predictive value of visit-to-visit variability in glycaemic or lipid tests for forecasting major adverse cardiovascular events (MACE) in diabetes mellitus. Data from existing studies suggests that such variability is an independent predictor of adverse outcomes in this patient cohort. This understanding is then applied to the development of PowerAI-Diabetes, a Chinese-specific artificial intelligence-enhanced predictive model for predicting the risks of major adverse cardiovascular events and diabetic complications. The model integrates an amalgam of variables including demographics, laboratory and medication information to assess the risk of MACE. Future efforts should focus on the incorporation of treatment effects and non-traditional cardiovascular risk factors, such as social determinants of health variables, to improve the performance of predictive models.

Item Type: Article
Additional Information: Publisher Copyright: © The Author(s) 2024.
Uncontrolled Keywords: endocrinology, diabetes and metabolism,genetics,endocrinology ,/dk/atira/pure/subjectarea/asjc/2700/2712
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 27 Feb 2026 09:30
Last Modified: 27 Feb 2026 09:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/102087
DOI: 10.1038/s44324-024-00012-7

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