Machine learning-derived multivariate renal function trajectories in acute kidney injury in critically ill patients: a multicentre retrospective study.

Journal: Clinical kidney journal
Published Date:

Abstract

BACKGROUND: Acute kidney injury (AKI) exhibits considerable heterogeneity. The objective of the current study was to identify AKI subphenotypes in intensive care unit (ICU) patients using multivariate renal function trajectories.

Authors

  • Jiaxi Lin
    Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
  • Lihe Liu
    Department of Gastroenterology, The First Affiliated Hospital of Soochow University, # 899 Pinghai St., Suzhou, Jiangsu, 215006, China.
  • Shiqi Zhu
    Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China.
  • Jingwen Gao
    Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
  • Lu Liu
    College of Pharmacy, Harbin Medical University, Harbin, China.
  • Hao Hong
  • Yao Wei
    Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland.
  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Xiaolin Liu
    Department of Physics, Shanghai University of Electric Power, Shanghai 200090, China. Electronic address: xlliu@shiep.edu.cn.
  • Rui Li
    Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China.
  • Jinzhou Zhu
    Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China.

Keywords

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