Prediction of prognosis in patients with systemic sclerosis based on a machine-learning model.

Journal: Clinical rheumatology
Published Date: Jun 28, 2024

Abstract

OBJECTIVE: The clinical manifestations of systemic sclerosis (SSc) are highly variable, resulting in varied outcomes and complications. Diverse fibrosis of the skin and internal organs, vasculopathy, and dysregulated immune system lead to poor and varied prognoses in patients with SSc subtypes. Therefore, this study aimed to develop a personalized tool for predicting the prognosis of patients with SSc.

Authors

  • Yan Zheng
    School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi'an, 710072, China.
  • Wei Jin
    Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, PR China; Institute of Cardiovascular Diseases, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, PR China. Electronic address: jinwei1125@126.com.
  • Zhaohui Zheng
  • Kui Zhang
    Department of Neurology, Mudanjiang Second People's Hospital, Mudanjiang 157013, Heilongjiang, China.
  • JunFeng Jia
    Department of Clinical Immunology, PLA Specialized Research Institute of Rheumatology & Immunology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
  • Cong Lei
    Department of Clinical Immunology, Xijing Hospital, Fourth Military Medical University, Shaanxi Province, No. 15 Changle West Road, Xi'an, 710032, People's Republic of China.
  • Weitao Wang
    Department of Clinical Immunology, Xijing Hospital, Fourth Military Medical University, Shaanxi Province, No. 15 Changle West Road, Xi'an, 710032, People's Republic of China.
  • Ping Zhu
    Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510100, China.

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