Introduction: The relative contribution on outcomes of different factors in spine surgery remain controversial. In this study we attempted to create a predictive model of complications after spine surgery.
Methods: We performed a retrospective cohort study involving patients who underwent spine surgery from 2005-2010 and were registered in the American College of Surgeons National Quality Improvement Project (NSQIP) database. A model for outcome prediction based on individual patient characteristics was developed.
Results: In total, 13,660 patients underwent spine surgery. Of these 2719 patients underwent anterior approaches (19.9%), 565 patients underwent corpectomy (4.1%), and 1757 patients had a fusion procedure (12.9%). The respective 30-day postoperative risks were 0.05% for stroke, 0.2% for MI, 0.25% for death, 0.3% for infection, 1.37% for UTI, 0.6% for DVT, 0.29% for PE, and 3.15% for return to the OR. Multivariate analysis demonstrated that increasing age, more extensive operations (fusion, corpectomy), medical deconditioning (weight loss, dialysis, PVD, CAD, COPD, diabetes), increasing BMI, non-independent mobilization (preoperative neurologic deficit), and bleeding disorders were independently associated with a more than 3 days length of stay. A validated model for outcome prediction based on individual patient characteristics was developed. The accuracy of the model was estimated by the area under the receiver operating characteristic (ROC) curve, which was 0.95, 0.82, 0.87, 0.75, 0.74, 0.78, 0.76, 0.74 and 0.65 for postoperative risk of stroke, MI, death, infection, DVT, PE, UTI, length of stay of 3 days or longer, and return to the OR, respectively.
Conclusions: Our model can provide individualized estimates of the risks of post-operative complications based on pre-operative conditions, and can potentially be utilized as an adjunct in decision-making for spine surgery.
Patient Care: The use of large prospectively obtained patient databases to obtain overall rates of various complications after spine surgery that may serve as benchmarks for quality. Analysis of these datasets can reveal baseline surgical and patient characteristics that are predictors of outcome. Analysis of the relative effect of various baseline characteristics on post-operative outcomes can be used to create a predictive model of individualized patient outcomes that can aid decision making in spinal surgery.
Learning Objectives: By the conclusion of this session, participants should be able to:
1. Describe the use of large prospectively obtained patient databases to obtain overall rates of various complications after spine surgery that may serve as benchmarks for quality.
2. Discuss how the analysis of these data sets can reveal baseline surgical and patient characteristics that are predictors of outcome.
3. Discuss how the analysis of the relative effect of various baseline characteristics on post-operative outcomes can be used to create a predictive model of individualized patient outcomes that can aid decision making in spinal surgery.