Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study.

Journal: Cardiovascular diabetology
PMID: 39920715

Abstract

BACKGROUND: Cardiovascular complications are major concerns for Chinese patients with type 2 diabetes. Accurately predicting these risks remains challenging due to limitations in traditional risk models. We aimed to develop a dynamic prediction model using machine learning and longitudinal trajectories of cardiovascular risk factors to improve prediction accuracy.

Authors

  • Qi Huang
    State Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural Universitygrid.35155.37, Wuhan, China.
  • Xiantong Zou
    Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
  • Zhouhui Lian
    Wangxuan Institute of Computer Technology (WICT), Peking University, Beijing, 100044, China.
  • Xianghai Zhou
    Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
  • Xueyao Han
    Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
  • Yingying Luo
    Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
  • Shuohua Chen
    Health Care Center, Kailuan Medical Group, Tangshan, China.
  • Yanxiu Wang
    Department of Cardiology, Kailuan Hospital, Tangshan, China.
  • Shouling Wu
    Department of Cardiology, Kailuan Hospital, Tangshan, China. Electronic address: drwusl@163.com.
  • Linong Ji
    Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.

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