Evaluating antimicrobial resistance and clinical outcomes in surgical ICU using a machine learning perspective: a retrospective observational study
Surgical ICU patients experienced high rates of MDR infections, prolonged antibiotic exposure, and elevated mortality. Machine learning, particularly XGBoost, showed promising potential in this exploratory context for early identification of high-risk patients, highlighting its role in guiding antimicrobial stewardship and empirical therapy in critical care settings, pending further validation.
Authors
- Sulmaz Ghahramani (ORCID: https://orcid.org/0000-0002-4161-5146)
- Shayan Yousufzai (ORCID: https://orcid.org/0009-0003-3283-769X)
- A. Ebrahimi (ORCID: https://orcid.org/0009-0005-8649-0833)
- Hossein Hosseini (ORCID: https://orcid.org/0000-0002-2018-5255)
- Abdolkarim Rahmanian (ORCID: https://orcid.org/0000-0001-5223-7083)
- Ahmad Saadat
- Simin Sharifi (ORCID: https://orcid.org/0000-0002-3020-3779)
- Hossein Roghani‐Shahraki (ORCID: https://orcid.org/0000-0001-9530-5744)
- Ali Tadayon (ORCID: https://orcid.org/0000-0002-1019-7105)
- Bisma Zulfiqar (ORCID: https://orcid.org/0000-0002-3508-5708)
- Sara Moradi (ORCID: https://orcid.org/0009-0004-9778-352X)
- Mohammad Karami (ORCID: https://orcid.org/0009-0009-7596-9426)
- Alireza Yousefi
- B. Ziaian
- Rojan Abdollahzade
- Setayesh Dehghaninazhvani
Institutions
- Shiraz University (IR)
- Shiraz University of Medical Sciences (IR)
- Islamic Azad University Medical Branch of Tehran (IR)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2025-10-28
- DOI
- https://doi.org/10.1186/s12879-025-11900-8
- Citations
- 2
- Primary Topic
- Surgical site infection prevention
- Type
- article
- Field-Weighted Citation Impact
- 4.01
Funders
- Shiraz University of Medical Sciences
- Shiraz University