Development of a Prediction Model for Significant Adverse Outcome After Spine Surgery

Study Design: Retrospective cohort study. Objectives: Development, validation, and decision curve analysis of a novel tool (NZSpine) for modelling risk of complications within 30 days of spine surgery. Methods: Data was gathered retrospectively from medical records of patients who underwent spine surgery at a single tertiary centre between January 2019 and December 2020 (n = 488). Postoperative adverse events were classified objectively using the Comprehensive Complication Index (CCI). The model was derived using backward stepwise logistic regression. Validation was undertaken using bootstrap resampling. Discrimination was determined by calculating the area under the receiver operating characteristic (AUC). Calibration was assessed graphically. Clinical utility of the model was assessed using decision curve analysis (DCA). Performance measures were compared to an existing tool, SpineSage. Results: Overall complication rate was 34%. Modelling showed higher age, surgical invasiveness and preoperative anemia were most strongly predictive of any complication (OR = 1.03, 1.09, 2.1 respectively, P < .001), whereas the occurrence of a major complication (CCI >26) was most strongly associated with the presence of respiratory disease (OR = 2.82, P < .001). At validation, the model showed good discrimination with an AUC of .73 (.71 - .75) and excellent calibration. SpineSage had an AUC of .71, while DCA showed the novel model had greater expected benefit at all risk thresholds. Conclusion: NZSpine is a novel risk assessment tool for patients undergoing acute and elective spine surgery and may help inform clinicians and patients of their perioperative risk.

Automated summary

This study developed a novel tool called NZSpine to model the risk of complications within 30 days of spine surgery. Through retrospective analysis and validation, the model demonstrated good discrimination and calibration, and showed greater clinical utility compared to the existing tool SpineSage in decision curve analysis.