Development and validation of a clinical nomogram prediction model for surgical site infection following lumbar disc herniation surgery.

Surgical site infection (SSI) following lumbar disc herniation (LDH) surgery leads to prolonged hospital stays, increased costs and reoperations. Therefore, we aim to develop and validate a nomogram to predict the risk of SSI following LDH surgery, thereby helping spine surgeons design personalized prevention strategies and promote early recovery. Data from 647 patients with SSI who underwent LDH surgery at the First Affiliated Hospital of Air Force Medical University (AFMU) from 2020 to 2023 were collected. Ultimately, 241 patients with SSI were selected based on inclusion and exclusion criteria. Patients were randomly divided into training and validation sets with a ratio of 7:3. LASSO regression, univariate, and multivariate logistic regression were utilized to identify target variables and establish the prediction model, which was subsequently validated. Six factors-Age, Body Mass Index (BMI), Postoperative Suction Drainage (PSD), Gelatin Sponge (GS), None-Preoperative Antibiotic (NPTA), and Thrombin Time (TT)-were selected to construct the nomogram model. In the training set, the area under the curve (AUC) for the nomogram was 0.818 (95% CI 0.779-0.857). In the validation set, the AUC was 0.782 (95% CI 0.717-0.846). Calibration curves for both sets showed satisfactory agreement between predicted and actual SSI probabilities. Decision curve analysis indicated that the nomogram is clinically useful with a threshold range of 1-90%. The Clinical Impact Curve (CIC) demonstrated an acceptable cost-benefit ratio. The developed nomogram model effectively predicts the risk of SSI following LDH surgery, enabling spine surgeons to formulate more professional and rational clinical prevention strategies.