Abstract：Objective This study aims to explore the clinical risk factors of incision infection after spinal surgery and the predictive efficacy of the prediction model. Methods In this retrospective case-control study, 521 patients with spinal surgery in Xi'an Honghui Hospital from January 2019 to January 2021 were recruited, including 294 males and 227 females, with age ranging from 14 to 85 (45.3±17.6) years. Among the 521 patients,75 underwent anterior spinal surgery, 402 underwent posterior spinal surgery, and 44 underwent combined anterior and posterior spinal surgery. According to the occurrence of postoperative incision infection, the patients were divided into infection group (45 cases) and non-infection group (476 cases). (1) The two groups of patients were compared in terms of clinical baseline data, surgical site, operation scope, operation method, operation time, blood loss, suture of incision, and operation indicators, such as in spine surgery, to determine factors influencing the infection of incision. Single-factor analysis and multiple-factor analysis after spinal surgery incision infection were conducted to identify independent risk factors (2) A nomogram model was constructed to predict the risk of incision infection after spinal surgery according to the risk factors. (3) The receiver operating characteristic curve(ROC) curve was used to evaluate the discrimination performance of the nomogram model, the calibration curve was used to evaluate the calibration performance of the nomogram model, and the decision curve was used to evaluate the predictive performance of the nomogram model. Results Among the 521 patients, 45 patients were finally confirmed to have postoperative incision infection, and the postoperative infection rate was 8.64%. Logistic regression analysis showed that age (P=0.005), history of diabetes (P=0.021), preoperative albumin level (P<0.001), operation time (P=0.017), and incision suture method (P=0.016) were independent risk factors for postoperative incision infection in patients undergoing spinal surgery. According to the risk factors of postoperative incision infection, the nomogram prediction model was constructed, and the AUC of the ROC curve was 0.754 (95% CI 0.678-0.829), the sensitivity was 71.1%, the specificity was 73.3%, and the diagnostic performance of the nomogram model was good. The slope of the calibration curve was close to 1, and the calibration performance of the nomogram model was good. The decision curve showed that the nomogram model had good clinical prediction performance. The Hosmer-Lemeshow test revealed no significant difference between the predicted value and the true value of the graph model (χ²=14.50, P=0.070). Conclusion Age, history of diabetes, preoperative albumin level, operation time, and incision suture method are independent risk factors for postoperative incision infection in patients undergoing spinal surgery. The nomogram prediction model constructed according to risk factors can provide personalized postoperative infection risk assessment method. It has good prediction performance and can provide reference for clinical prediction of postoperative incision infection in the spine.
孙杨, 单乐群, 曾文, 郝定均. 脊柱手术后切口感染的危险因素分析及预测模型的构建[J]. 中华解剖与临床杂志, 2023, 28(5): 327-331.
Sun Yang, Shan Lequn, Zeng Wen, Hao Dingjun. Analysis of risk factors and construction of prediction model for wound infection after spinal surgery. Chinese Journal of Anatomy and Clinics, 2023, 28(5): 327-331.
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2023, Vol. 28 
