Machine learning prediction of extended length of stay following endoscopic decompression for lumbar spinal stenosis: A retrospective cohort study
This study establishes the first ML-driven framework for eLOS prediction in endoscopic LSS surgery, emphasizing age-related comorbidities over procedural factors. The integration of economic and clinical data enables actionable risk mitigation, supporting value-based care initiatives. Future multicenter studies should validate these models across diverse healthcare systems.
Authors
- Chaoyang Qu
- Fuqiang Tan (ORCID: https://orcid.org/0000-0002-0194-958X)
- Bin Li (ORCID: https://orcid.org/0000-0002-7088-8274)
- Xin Shu (ORCID: https://orcid.org/0000-0001-5626-8645)
- Peng Xu (ORCID: https://orcid.org/0000-0002-1440-6509)
Institutions
- People's Hospital of Bishan District (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- First People's Hospital of Chongqing (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251361658
- Primary Topic
- Spine and Intervertebral Disc Pathology
- Type
- article
- Field-Weighted Citation Impact
- 0.00