Diabetes and age increase surgical site infection risk in spinal surgery patients based on a multicenter cohort.

Surgical site infection (SSI) represents a prevalent postoperative complication associated with spinal surgery, contributing to increased morbidity and mortality rates. This study sought to identify key prognostic factors for SSI following spinal surgery and to develop a novel nomogram to predict SSI incidence. Retrospective data collection was conducted on patients who underwent spinal surgeries between 2017 and 2024. The cohort was stratified into two groups: those with infections (n = 59) and those without infections (n = 990). A nomogram was developed to predict the risk of SSI outcomes, utilizing results derived from univariate and multivariate regression analyses of factors influencing SSI after spinal surgery. Internal validation of the nomogram was conducted through Bootstrap analysis. A total of 1,049 patients were enrolled in the study. Variables identified as statistically significant through univariate regression analyses were incorporated into the multivariate regression model. The analysis revealed that age (odds ratio [OR]: 3.312, 95% confidence interval [CI]: 1.377–7.965), diabetes mellitus (OR: 3.698, 95% CI: 1.854–7.377), albumin levels (OR: 0.172, 95% CI: 0.091–0.326), operative time (OR: 2.003, 95% CI: 1.129–3.554), method of suture (OR: 0.459, 95% CI: 0.258–0.817), and blood loss (OR: 2.085, 95% CI: 1.183–3.674) were independent predictors. Based on these indicators, a nomogram model was developed. Routine bacterial cultures of surgical site secretions were performed in patients with suspected infections, revealing that Staphylococcus aureus was the most prevalent microorganism. The application of the nomogram in the validation cohort exhibited good discrimination ability, with a concordance index of 0.787 (95% CI, 0.718–0.856), and demonstrated good calibration. Decision curve analysis further confirmed the model’s superior clinical utility across a wide range of threshold probabilities. This study has developed a robust and valuable nomogram capable of accurately predicting the incidence of SSI following spinal surgery in patients. This tool is user-friendly and has the potential to aid clinicians in making informed clinical decisions tailored to individual patients.