Integrating StEP-COMPAC definition and enhanced recovery after surgery status in a machine-learning-based model for postoperative pulmonary complications in laparoscopic hepatectomy.

Journal: Anaesthesia, critical care & pain medicine
PMID: 39278548

Abstract

BACKGROUND: Postoperative pulmonary complications (PPCs) contribute to high mortality rates and impose significant financial burdens. In this study, a machine learning-based prediction model was developed to identify patients at high risk of developing PPCs following laparoscopic hepatectomy.

Authors

  • Sibei Li
    Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Yaxin Lu
    Department of Big Data and Artificial Intelligence, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
  • Hong Zhang
    Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
  • Chuzhou Ma
    Department of Anesthesiology, Shantou Central Hospital, Shantou, China.
  • Han Xiao
    Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Zifeng Liu
    Shandong Yankuang Group Changlong Cable Manufacturing Co., Ltd, Jining, China.
  • Shaoli Zhou
    Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China. 13610272308@139.com.
  • Chaojin Chen
    Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 10630, Guangdong, People's Republic of China.

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