Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.

Journal: Renal failure
PMID:

Abstract

BACKGROUND: Cardiac valve calcification (CVC) is common in dialysis patients and associated with increased cardiovascular risk. However, early screening has been limited by cost concerns. This study aimed to develop and validate a machine learning model to enhance early detection of CVC.

Authors

  • Xiaoxu Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Yinfang Li
    Department of Pediatric, The Second Affiliated Hospital of Nanjing Medical University, School of Pediatric, Nanjing Medical University, Nanjing, P.R. China.
  • Zixin Cao
    Department of Stomatology, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang Ouhai District, Wenzhou, Zhejiang, 325000, People's Republic of China.
  • Yunuo Li
    Department of Nephrology, Zhongda Hospital, Southeast University School of Medicine, Nanjing, P.R. China.
  • Jingyuan Cao
    Department of Nephrology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, P.R. China.
  • Yao Wang
    Department of Gastrointestinal Surgery, Zhongshan People's Hospital, Zhongshan, Guangdong, China.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.
  • Jing Zheng
    Shandong Institute for Food and Drug Control, Jinan 250101, China.
  • Siqi Peng
    Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangdong, 510000, China.
  • Wen Shi
    Sino-Jan Joint Lab of Natural Health Products Research, School of Traditional Chinese Medicines, China Pharmaceutical University, Nanjing 210009, China; Department of Chinese Medicine Resources, School of Traditional Chinese Medicines, China Pharmaceutical University, Nanjing 210009, China.
  • Qianqian Wu
    School of Medicine, Jiangsu University, Zhenjiang 212013, China.
  • Junlan Yang
    Department of Nephrology, Zhongda Hospital, Southeast University School of Medicine, Nanjing, P.R. China.
  • Yaping Fang
    Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, No. 1 Shizishan Street, Hongshan District, Wuhan 430070, China.
  • Aiqing Zhang
    Department of Pediatric, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, P.R. China.
  • Xiaoliang Zhang
    Department of Information, The First Affiliated Hospital of Nanjing Medical University & Jiangsu Province Hospital, Nanjing, Jiangsu, China; Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.