Integrating StEP-COMPAC definition and enhanced recovery after surgery status in a machine-learning-based model for postoperative pulmonary complications in laparoscopic hepatectomy
Based on the most recent definition, a machine learning model was employed to predict the risk of PPCs following laparoscopic hepatectomy. Logistic regression was identified as the best-performing model. ERAS implementation was associated with a reduction in the number of PPCs.
Authors
- Sibei Li (ORCID: https://orcid.org/0000-0003-0204-3584)
- Chaojin Chen (ORCID: https://orcid.org/0000-0001-5101-4101)
- Chuzhou Ma
- Hong Zhang
- Yaxin Lu (ORCID: https://orcid.org/0000-0001-6278-4347)
- Xiao Han (ORCID: https://orcid.org/0000-0001-8055-9987)
- Shaoli Zhou (ORCID: https://orcid.org/0000-0002-6604-0166)
- Zifeng Liu (ORCID: https://orcid.org/0000-0002-1392-8698)
Institutions
- Sun Yat-sen University (CN)
- Shantou Central Hospital (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- First Hospital of Lanzhou University (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Anaesthesia Critical Care & Pain Medicine
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1016/j.accpm.2024.101424
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- Guangdong Science and Technology Department