A prediction model of surgical site infection after instrumented thoracolumbar spine surgery in adults.

Purpose: 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.

External validation of a prediction model for surgical site infection after thoracolumbar spine surgery in a Western European cohort.

Background: A 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.