Derivation and external validation of mass spectrometry-based proteomic model using machine learning algorithms to predict plaque rupture in patients with acute coronary syndrome.

Journal: Clinica chimica acta; international journal of clinical chemistry
PMID: 39117035

Abstract

BACKGROUND: A poor prognosis is associated with atherosclerotic plaque rupture (PR) despite after conventional therapy for patients with acute coronary syndrome (ACS). Timely identification of PR improves the risk stratification and prognosis of ACS patients.

Authors

  • Jianing Wu
    School of Aeronautics and Astronautics, Sun Yat-Sen University, Guangzhou, 510006, P. R. China.
  • Ke Ma
    Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Research Institute of Criminal Science and Technology Shanghai 200083 China yangfyhit@sina.com +86 021 22028363 +86 021 22028362.
  • Jie Ma
    Respiratory Department, Beijing Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
  • Yulin Li
    Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China.
  • Yongkui Ren
    Department of Cardiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China. Electronic address: dlrenyongkui@163.com.

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