Introduction: In the current era of quality reporting and pay for performance, neurosurgeons must develop models to identify patients at high risk of complications. We sought to identify risk factors for complications in spine surgery and to develop a score predictive of complications.

Methods: We examined spinal surgeries from the American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database. 22,430 cases were identified based on CPT.

Results: The overall complication rate for the cohort was 9.9%. The most common complications were post-operative bleeding requiring transfusion (4.1%), non-wound infections (3.1%), and wound-related infections (2.2%). Multivariate regression analysis identified twenty factors associated with complications. Assigning one point for the presence of each factor a risk model was developed. The range of scores for the cohort was 0-13 with a median score of 4. Complication rates for a risk score of 0-4 was 3.7% and for scores 5-13 was 18.5%. The risk model robustly predicted complication rates, with complication rate of 1.2% for score of 0 (n=412, 1.8% of total) and 63.6% and 100% for scores of 12 and 13 (n=22 patients, 0.1% of total cohort) respectively (p < .001). The risk score also correlated strongly with total length of stay, mortality, and total work relative value units (wRVU) for the case.

Conclusions: Patient-specific risk factors including co-morbidities are strongly associated with surgical complications, length of stay, cost of care, and mortality in spine surgery and can be used to develop risk models that are highly predictive of complications.

Patient Care: Identify factors that increase the risk of complications in spine surgery.

Learning Objectives: 1. Identify factors associated with complications in spine surgery 2. Use regression analysis to develop a risk model for complications in spinal surgery 3. Highlight the value of clinical registries to develop risk models and improve outcomes in spinal surgery

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