Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.

Journal: BMC nephrology
PMID: 36536317

Abstract

BACKGROUND: Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods. We conducted this systematic review and meta-analysis to estimate the sensitivity and specificity of artificial intelligence for the prediction of acute kidney injury during the perioperative period.

Authors

  • Hanfei Zhang
    School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Amanda Y Wang
    The faculty of medicine and health sciences, Macquarie University, Sydney, NSW, Australia. Awang@georgeinstitute.org.au.
  • Shukun Wu
    School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Johnathan Ngo
    Concord Clinical School, University of Sydney, Sydney, Australia.
  • Yunlin Feng
    School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Xin He
    Department of Nephrology, The Affiliated Hospital of Guizhou Medical, Guizhou, China.
  • Yingfeng Zhang
  • Xingwei Wu
    School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. wuxw1998@126.com.
  • Daqing Hong
    School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. hongdaqing11@126.com.

Keywords

External Resources

Popular Topics
Recent Journals