Objective To analyze the incidence and risk factors of surgical site infection (SSI) in patients after spinal tuberculosis surgery, and to establish a Nomogram model.

Methods Clinical materials of 280 patients diagnosed as spinal tuberculosis in Haian City People′s Hospital from January 2015 to June 2020 were retrospectively analyzed, and they were divided into training set (n=220) and validation set (n=60) according to a ratio of 4∶1. Based on the occurrence of SSI in one month after surgery, the training set was divided into SSI group (n=58) and non-SSI group (n=162), and 17 patients had SSI in the validation set. Clinical materials, blood biochemical indexes and surgical indexes were compared between SSI group and non-SSI group in the training set. LASSO and multivariate Logistic regression analysis were used to screen the best predictors, and then a Nomogram model was established.

Results Univariate analysis revealed that compared to patients in the non-SSI group, patients in the SSI group had older age, longer tuberculosis course and anti-tuberculosis treatment duration, increased number of affected segments andabscesses, higher grade of the American Spinal Injury Association (ASIA), elevated erythrocyte sedimentation rate, C-reactive protein (CRP) level and neutrophil-to-lymphocyte ratio (NLR), prolonged operation time and drainage time, increased incision length, and decreased albumin level, and all the differences were statistically significant (P < 0.05). LASSO regression screened out 6 independent predictors variables with no collinearity. Logistic regression model showed that lesion segments≥2 (OR=1.568, 95%CI, 1.321 to 1.896, P=0.001), abscess (OR=1.956, 95%CI, 1.632 to 2.358, P < 0.001), incision length ≥10 cm (OR=1.342, 95%CI, 1.124 to 1.776, P=0.003), drainage time ≥5 days (OR=1.745, 95%CI, 1.402 to 1.963, P < 0.001), NLR ≥3.5 (OR=2.023, 95%CI, 1.548 to 2.528, P < 0.001) and albumin < 35 g/L (OR=2.235, 95%CI, 1.754 to 2.569, P < 0.001) were strong predictors for occurrence of SSI. A Nomogram model was established by R software, with a total score of 220. Bootstrap method for internal validation calculated a consistency index (C-index) of 0.895 (95%CI, 0.842 to 0.943, P < 0.001), and the area under the curve (AUC) of the Nomogram model by the receiver operating characteristic (ROC) curve in predicting SSI of the training set was 0.885 (95%CI, 0.834 to 0.936, P < 0.001). External validation calculated the AUC was 0.839 (95%CI, 0.778 to 0.886, P < 0.001) for the validation set. The calibration curves for the training set and validation set demonstrated good alignment between the predicted probability of the Nomogram model and the actual incidence of SSI, and the decision curve showed that the Nomogram model for both the training set and validation set had good clinical utility.

Conclusion There is still a relatively high incidence of SSI after spinal tuberculosis surgery, and the main risk factors include the affected segment, abscess, incision length, drainage time, NLR and albumin. The Nomogram model constructed in this study has good predictive efficacy for occurrence of SSI.