Establishment and validation of a nomogram model for postoperative surgical site infection after transforaminal lumbar interbody fusion: A retrospective observational study

Abstract

BackgroundSurgical site infection is one of the serious complications of transforaminal lumbar interbody fusion surgery, and many factors affect its occurrence. MethodsA total of 1,277 patients who underwent transforaminal lumbar interbody fusion between 2018 and 2021 were enrolled in this study. Subsequently, 1,277 patients were randomly assigned to a training cohort (N = 958) and a validation cohort (N = 319) in a 3:1 ratio. We developed a nomogram according to the results of binary logistic regression analysis in the training cohort. The nomogram's predictive accuracy and discriminative ability were evaluated by calibration curve and receiver operating characteristic analysis. Decision curve analysis was performed to estimate the clinical value of our nomogram. ResultsIn univariate and multivariate analysis, smoking, diabetes, intraoperative blood loss, American Society of Anesthesiologists class ≥III, serum calcium, albumin, and serum glucose were identified as significant independent predictors. The nomogram was developed using these independent predictors, which showed good diagnostic accuracy for surgical site infection of the training and validation cohorts. The calibration curves for the 2 cohorts showed optimal agreement between nomogram prediction and actual observation. The decision curve analysis of the nomogram model showed the great clinical use of the nomogram. ConclusionThe nomogram based on smoking, diabetes, intraoperative blood loss, American Society of Anesthesiologists class, serum calcium, albumin, and serum glucose has the potential as a clinically useful predictive tool of surgical site infection after transforaminal lumbar interbody fusion surgery. It is helpful to visualize the risk factors of surgical site infection.