中国人群脊柱外科手术部位感染预测模型构建验证

Construction and validation of predictive model for surgical site infections following spinal surgery in Chinese population

    摘要
  • 摘要:
    目的 开发适用于中国人群脊柱外科手术部位感染(SSIs)预测工具，为减少SSIs提供证据支持。
    方法 系统检索中英文数据库文献进行Meta分析获得影响因素的合并风险值，基于logistic回归模型构建风险预测评分工具。选择2021年北京某三甲医院进行脊柱手术并完成术后随访的患者数据验证工具预测效果。
    结果 中国人群脊柱SSIs预测模型为：Logit(P)=-3.47+0.63×(年龄≥60岁)+0.31×患者合并心血管疾病+0.69×类风湿关节炎+1.07×糖尿病+1.06×(手术时间＞3 h)+1.17×(术前白蛋白＜35 g/L)+0.71×既往脊柱手术史+0.67×有内植入物+0.73×输血。预测工具总分为92分，分值≥24.50分为高风险人群，预测曲线下面积为0.733，灵敏度为58.30%，特异性为79.60%。
    结论 建立的中国人群脊柱外科SSIs预测模型具有较好预测性能，可作为参考评估工具应用于临床。

     

    Abstract:
    OBJECTIVE To construct a predictive tool for surgical site infections (SSIs) in spinal surgery for Chinese population to provide evidence support for reducing SSIs.
    METHODS A systematic review of Chinese and English database literature was conducted for Meta-analysis to obtain pooled risk values for influencing factors, and a risk prediction scoring tool was constructed based on the logistic regression model. Patients who underwent spinal surgery and completed postoperative follow-up in a tertiary hospital in Beijing from Jan. to Dec. 2021 were selected to validate the predictive effect of the tool.
    RESULTS The predictive model for SSIs in Chinese spinal surgery patients was Logit(P)=-3.47+0.63(age ≥60 years)+0.31×(patient with cardiovascular disease)+0.69×(rheumatoid arthritis) + 1.07 × (diabetes) + 1.06 × (operation duration > 3 h) + 1.17 × (preoperative albumin < 35 g/L) + 0.71 × (history of spinal surgery) + 0.67 × (carrying internal implants) + 0.73 × (blood transfusion). The total score of the predictive tool was 92, with a cutoff score of ≥ 24.50 indicating high-risk individuals. The area under the curve was 0.733, with the sensitivity 58.30% and the specificity 79.60%.
    CONCLUSION The established predictive model for SSIs in Chinese spine surgery demonstrates good predictive performance and can be used as a reference assessment tool in clinical practice.

     

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