Exploring the influencing factors of abdominal aortic calcification events in chronic kidney disease (CKD) and non-CKD patients based on interpretable machine learning methods.

Journal: International urology and nephrology
Published Date:

Abstract

BACKGROUND: Calcification is prevalent in CKD patients, with abdominal aortic calcification (AAC) being a strong predictor of coronary calcification. We aimed to identify key calcification factors in CKD and non-CKD populations using machine learning models.

Authors

  • Haowen Lin
    Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong, 510000, China.
  • Xiaoying Dong
    School of Medicine South, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), China University of Technology, Southern Medical University, Guangdong, 510000, China.
  • Yuhe Yin
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Qingqing Gao
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Siqi Peng
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Zewen Zhao
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Sijia Li
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Renwei Huang
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Yiming Tao
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Sichun Wen
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Bohou Li
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Qiong Wu
    Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, P. R. China.
  • Ting Lin
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Hao Dai
    Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL.
  • Feng Wen
    Department of Nephrology, Renal Research Institute, Hunan Key Lab of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University Changsha 410011, Hunan, China.
  • Zhuo Li
    Biostatistics Unit, Mayo Clinic, Jacksonville, FL, United States.
  • Lixia Xu
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Jianchao Ma
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Zhonglin Feng
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Shuangxin Liu
    Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong, 510000, China. 13543456446@139.com.

Keywords

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