Systemic immune-inflammatory biomarkers combined with the CRP-albumin-lymphocyte index predict surgical site infection following posterior lumbar spinal fusion: a retrospective study using machine learning
The AISI, SII, CALLY and SLSI emerged as independent predictors of SSI following posterior lumbar spinal fusion. Our machine learning-derived nomogram demonstrated high discriminative accuracy and clinical applicability through dynamic risk stratification. Leveraging the SHAP methodology enhances model interpretability, thereby empowering healthcare providers to proactively mitigate SSI occurrences and enhance overall patient outcomes.
Authors
- Yuanming Chen (ORCID: https://orcid.org/0000-0001-7955-3350)
- Jiawei Liang (ORCID: https://orcid.org/0000-0003-0748-3183)
- Shengbin Huang (ORCID: https://orcid.org/0000-0003-4522-2769)
- Shengsheng Huang
- Jiayi Chen (ORCID: https://orcid.org/0009-0009-8164-5743)
- Qionghui Wu
- Yen‐Chuan Ou (ORCID: https://orcid.org/0000-0002-6080-7231)
- Zixiang Pang (ORCID: https://orcid.org/0009-0002-7367-0630)
Institutions
- Guangxi Medical University (CN)
- Guigang City People's Hospital (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-07-30
- DOI
- https://doi.org/10.3389/fmed.2025.1590248
- Citations
- 1
- Primary Topic
- Surgical site infection prevention
- Type
- article
- Field-Weighted Citation Impact
- 2.01