Development, validation and economic evaluation of a machine learning algorithm for predicting the probability of kidney damage in patients with hyperuricaemia: protocol for a retrospective study.

Journal: BMJ open
PMID: 39613447

Abstract

INTRODUCTION: Accurate identification of the risk factors is essential for the effective prevention of hyperuricaemia (HUA)-related kidney damage. Previous studies have established the efficacy of machine learning (ML) methodologies in predicting kidney damage due to other chronic diseases. Nevertheless, a scarcity of precise and clinically applicable prediction models exists for assessing the risk of HUA-related kidney damage. This study aims to accurately predict the risk of developing HUA-related kidney damage using a ML algorithm, which is based on a retrospective database.

Authors

  • Zhengyao Hou
    Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Yong Yang
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Bo Deng
  • Guangjie Gao
    Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Mengting Li
    Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China.
  • Xinyu Liu
    Institute of Medical Technology, Peking University Health Science Center, Beijing, China.
  • Huan Chang
    Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
  • Hao Shen
  • Linke Zou
    Department of Pharmacy, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Jinqi Li
    Department of Orthopaedic Trauma, Beijing Jishuitan Hospital, Beijing, 100035, P. R. China.
  • Xingwei Wu
    School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. wuxw1998@126.com.

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