Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

Journal: British journal of anaesthesia
PMID: 38413342

Abstract

BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery.

Authors

  • Hyun-Kyu Yoon
    Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Korea.
  • Hyun Joo Kim
    Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Yi-Jun Kim
    Institute of Convergence Medicine, Ewha Womans University Mokdong Hospital, Seoul, South Korea.
  • Hyeonhoon Lee
    Department of Clinical Korean Medicine, Graduate School, Kyung Hee University, Seoul, Republic of Korea.
  • Bo Rim Kim
    Department of Anesthesiology and Pain Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea.
  • Hyongmin Oh
    Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
  • Hee-Pyoung Park
  • Hyung-Chul Lee
    From the Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea.

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