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Data-Driven Clustering for Risk Stratification of Unfavorable Outcomes After Lumbar Fusion Surgery: A Development
Peng Cui1, Hu Tao1, Haojie Zhang1
1Department of Orthopedics, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.
Neurospine
|May 7, 2026
Summary
This study developed a data-driven risk model using cluster analysis to predict unfavorable surgical outcomes (USO) after lumbar fusion. The model accurately identifies patients at higher risk for USO, aiding personalized treatment strategies.
Area of Science:
- Spine surgery
- Medical informatics
- Machine learning in healthcare
Background:
- Lumbar fusion is key for degenerative diseases, but patient variability leads to poor outcomes.
- Unfavorable surgical outcomes (USO) affect a significant patient proportion.
- Accurate risk stratification can personalize treatment and identify at-risk patients.
Purpose of the Study:
- Develop a data-driven risk stratification model for USO after lumbar fusion.
- Utilize cluster analysis to classify patients based on USO risk.
- Identify key prognostic predictors for USO.
Main Methods:
- Enrolled 662 patients undergoing lumbar fusion for degenerative disease.
- Defined USO by clinical improvement and complications.
- Employed machine learning and K-prototypes clustering to identify risk factors and stratify patients.
Main Results:
- Identified 6 key predictors: frailty, depression, PI-LL match, surgical levels, FIM, and rFCSA.
- K-prototypes identified 3 distinct patient clusters.
- A LightGBM classifier achieved high predictive performance (AUC 0.951).
Conclusions:
- A data-driven clustering approach effectively stratifies USO risk after lumbar fusion.
- The developed model shows high predictive accuracy.
- Further validation in diverse cohorts is needed for clinical application.