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Predicting complications of spine surgery: external validation of three models

      Highlights

      • SpineSage and the Risk Assessment Tool from Ratliff et al. are well calibrated for spine surgery
      • The NSQIP Risk Calculator systemically underestimates the risk of complications after spine surgery
      • Decision curve analysis suggests that using SpineSage may help clinicians and patients determine whether to undertake surgery

      Abstract

      BACKGROUND CONTEXT

      Numerous prediction tools are available for estimating postoperative risk following spine surgery. External validation and comparison of these tools is critical prior to clinical use. No model for adverse events after spine surgery has undergone decision curve analysis.

      PURPOSE

      External validation, comparison, and decision curve analysis of 3 previously described models [SpineSage, Risk Assessment Tool (RAT), National Surgical Quality Improvement Program Risk Calculator (NSQIP)] for predicting 30-day postoperative complications after spine surgery

      STUDY DESIGN

      Retrospective cohort study.

      PATIENT SAMPLE

      Three hundred fifteen patients who underwent spine surgery at a tertiary academic surgical center in New Zealand between January 2019 and April 2020.

      OUTCOME MEASURES

      As defined by each risk prediction tool and objectively using the Comprehensive Complication Index.

      METHODS

      We retrospectively reviewed risk of postoperative complication was calculated for each patient according to the 3 models. Overall model fit, calibration, discrimination, and decision curve analysis for each model were assessed in line with the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines.

      RESULTS

      100 (35%) patients experienced complications. SpineSage and RAT were well calibrated, NSQIP systematically underestimated risk. Area under the curve was greatest for SpineSage (0.75) compared with the NSQIP (0.72) and the RAT (0.69). Decision curve analysis showed SpineSage resulted in greatest net benefit across all risk thresholds.

      CONCLUSIONS

      Of the models studied, SpineSage most accurately predicted risk and can be expected to perform better than a strategy of treating all patients if patient or surgeon deem complication risk >10% significant. NSQIP may not be suitable for the clinical use in our local population.

      Keywords

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