Development and Validation of a Machine Learning-Based Nomogram for Prediction of Unplanned Reoperation Postspinal Surgery Within 30 Days.

Journal: World neurosurgery
PMID: 39433251

Abstract

BACKGROUND: Unplanned reoperation postspinal surgery (URPS) leads to prolonged hospital stays, higher costs, decreased patient satisfaction, and adversely affects postoperative rehabilitation. This study aimed to develop and validate prediction models (nomograms) for early URPS risk factors using machine learning methods, aiding spine surgeons in designing prevention strategies, promoting early recovery, reducing complications, and improving patient satisfaction.

Authors

  • Hai-Yang Qiu
    Department of Orthopaedics, The First Affiliated Hospital of Air Force Medical University, Xi'an, Shanxi Province, China.
  • Chang-Bo Lu
    Department of Orthopaedics, The First Affiliated Hospital of Air Force Medical University, Xi'an, Shanxi Province, China.
  • Da-Ming Liu
    Department of Orthopaedics, The First Affiliated Hospital of Air Force Medical University, Xi'an, Shanxi Province, China.
  • Wei-Chen Dong
    Department of Orthopaedics, The First Affiliated Hospital of Air Force Medical University, Xi'an, Shanxi Province, China.
  • Chao Han
    School of Software Engineering, South China University of Technology, Guangzhou, P. R. China.
  • Jiao-Jiao Dai
    Department of Burns and Cutaneous Surgery, The First Affiliated Hospital of Air Force Medical University, Xi'an, Shanxi Province, China.
  • Zi-Xiang Wu
    Department of Orthopaedics, The First Affiliated Hospital of Air Force Medical University, Xi'an, Shanxi Province, China.
  • Wei Lei
    Department of Orthopaedics, Xijing Hospital, Air Force Medical University, Xi'an, 710032.
  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

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