BackgroundA prediction model for surgical site infection (SSI) after spine surgery was developed in 2014 by Lee et al. This model was developed to compute an individual estimate of the probability of SSI after spine surgery based on the patient’s comorbidity profile and invasiveness of surgery. Before any prediction model can be validly implemented in daily medical practice, it should be externally validated to assess how the prediction model performs in patients sampled independently from the derivation cohort.MethodsWe included 898 consecutive patients who underwent instrumented thoracolumbar spine surgery.To quantify overall performance using Nagelkerke’s R2 statistic, the discriminative ability was quantified as the area under the receiver operating characteristic curve (AUC). We computed the calibration slope of the calibration plot, to judge prediction accuracy.ResultsSixty patients developed an SSI. The overall performance of the prediction model in our population was poor: Nagelkerke’s R2 was 0.01. The AUC was 0.61 (95% confidence interval (CI) 0.54–0.68). The estimated slope of the calibration plot was 0.52.ConclusionsThe previously published prediction model showed poor performance in our academic external validation cohort. To predict SSI after instrumented thoracolumbar spine surgery for the present population, a better fitting prediction model should be developed.

The aim of this study was to develop and internally validate a multivariable model for accurate prediction of surgical site infection (SSI) after instrumented spine surgery using a large cohort of a Western European academic center. Method Data of potential predictor variables were collected in 898 adult patients who underwent instrumented posterior fusion of the thoracolumbar spine. We used logistic regression analysis to develop the prediction model for SSI. The ability to discriminate between those who developed SSI and those who did not was quantified as the area under the receiver operating characteristic curve (AUC). Model calibration was evaluated by visual inspection of the calibration plot and by computing the Hosmer and Lemeshow goodness-of-fit test. Results Sixty patients (6.7%) were diagnosed with an SSI. After backward stepwise elimination of predictor variables, we formulated a model in which an individual's risk of an SSI can be computed. Age, body mass index, ASA score, degenerative or revision surgery and NSAID use appeared to be independent predictor variables for the risk of SSI. The AUC was 0.72 (95% CI 0.65-0.79), indicating reasonable discriminative ability. Conclusions We developed and internally validated a prediction model for SSI after instrumented thoracolumbar spine surgery using predictor variables of standard clinical practice that showed reasonable discriminative ability and calibration. Identification of patients at risk for SSI allows for individualized patient risk assessment with better patient-specific counseling and may accelerate the implementation of multidisciplinary strategies for reduction of SSI.