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Evaluation of Predictive Models for Complications following Spinal Surgery
Nicholas Dietz1, Mayur Sharma1, Ahmad Alhourani1
1Department of Neurosurgery, University of Louisville, Louisville, Kentucky, United States.
Summary
Predictive models for spinal surgery complications vary widely. This study identified 30 validated models, finding that those incorporating anatomical features showed higher accuracy for deformity cases, potentially improving patient outcomes.
Area of Science:
- Spine Surgery
- Predictive Modeling
- Complication Rates
Background:
- Spinal surgery complication rates are unpredictable due to patient, surgical, and hospital variability.
- Predictive models can aid surgeon decision-making and improve patient outcomes.
Purpose of the Study:
- To evaluate independently validated predictive models for complications in spinal surgery.
- Assess study design, model generation, accuracy, reliability, and utility of existing models.
Main Methods:
- Systematic review adhering to Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines.
- Searched PubMed and Ovid Medline databases using the Participants, Intervention, Comparison, Outcomes, Study Design (PICOS) framework.
- Included 18 articles detailing 30 validated predictive models for adult spinal surgery complications.
Main Results:
- Models analyzed covered degenerative conditions, deformity, trauma, and other spinal issues.
- Commonly included risk factors: age, body mass index, diabetes, sex, and smoking.
- Deformity models with radiographic/anatomical grading outperformed those with only demographics/comorbidities (AUC range: 0.37-1.0).
Conclusions:
- Identified 30 validated predictive models for spinal surgery complications across various conditions.
- Evidence-based models can enhance shared decision-making, rehabilitation, and reduce adverse events.
- Accurate models can inform best practices in spinal surgery.
