Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease.

Journal: Renal failure
PMID: 40230189

Abstract

OBJECTIVE: Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model incorporating machine learning (ML) for assessing kidney fibrosis severity using biochemical markers.

Authors

  • Le-Hao Wu
    Department of Nephrology, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
  • Dan Zhao
    Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, China.
  • Jian-Ying Niu
    Department of Nephrology, Shanghai Fifth People's Hospital of Fudan University, Shanghai, China.
  • Qiu-Ling Fan
    Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110000, China.
  • Ai Peng
    Department of Nephrology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
  • Cheng-Gong Luo
    Department of Nephrology, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
  • Xiao-Qin Zhang
    College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China .
  • Tian Tang
    Department of Nephrology, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
  • Chen Yu
    Central Laboratory, Shanghai Clinical Center, Chinese Academy of Sciences/Central Laboratory, Shanghai Xuhui Central Hospital, Shanghai 200031, China.
  • Ying-Ying Zhang
    Center of Clinical and Translational Medicine, Shanghai Changhai Hospital, Shanghai 200433, China.

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