Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.

Journal: BMC endocrine disorders
PMID: 33947391

Abstract

INTRODUCTION: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and variability of HbA1c and lipids for adverse outcomes.

Authors

  • Sharen Lee
    Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China.
  • Jiandong Zhou
    School of Data Science, City University of Hong Kong, Hong Kong, China.
  • Wing Tak Wong
    School of Life Sciences, Chinese University of Hong Kong, Hong Kong, China.
  • Tong Liu
    Intensive Care Medical Center, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, People's Republic of China.
  • William K K Wu
    Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.
  • Ian Chi Kei Wong
    Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • Qingpeng Zhang
    Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.
  • Gary Tse
    Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, China.

Keywords

External Resources

Popular Topics
Recent Journals