探讨影响冠状动脉旁路移植术（CABG）后手术部位感染的主要危险因素，并构建定量列线图风险预测模型，提出针对性管理策略。

回顾性总结2015年5月至2019年5月于南京医科大学附属盐城市第三人民医院接受CABG患者共302例作为模型组，比较感染与未感染者临床资料（主要包括性别、年龄、基础疾病史、体质指数、美国麻醉师协会（ASA）评分、常规皮肤准备、围术期抗菌药物不合理使用、手术时间> 4 h、手术室探视和连续使用同一手术室），再经多因素Logistic回归分析筛选主要危险因素并构建列线图风险预测模型。纳入2019年6月至2020年12月共211例CABG患者作为验证组，接受感染管理策略。采用受试者工作曲线（ROC）分析列线图模型评估模型组与验证组感染发生的效能。

模型组共诊断11例患者发生感染（3.64%，11/302），单因素分析显示感染者年龄高于未感染者，且感染者基础疾病史（高血压和糖尿病）和肥胖者比例增高，ASA评分升高，常规皮肤准备方法、围术期抗菌药物不合理使用、手术时间> 4 h、手术室探视、连续使用同一手术室比例均较未感染者增高（P均< 0.05）。Logistic回归分析显示，高龄（OR = 1.58、95%CI：1.12~2.53、P = 0.011）、基础疾病史（OR = 2.63、95%CI：2.12~3.06、P = 0.001）、围术期抗菌药物不合理使用（OR = 2.01、95%CI：1.55~2.69、P = 0.002）、手术时间> 4 h（OR = 3.11、95%CI：2.68~3.59、P = 0.001）和手术室探视（OR = 1.24、95%CI：1.01~1.85、P = 0.024）均为CABG术后手术部位感染的主要危险因素。应用R软件根据主要危险因素（高龄、基础疾病、抗菌药物不合理使用、手术时间> 4 h、手术室探视）的权重（β值）进行定量赋值建立列线图模型。验证组共诊断感染者2例（0.95%，2/211），显著低于模型组（Fisher’s确切概率法，取单侧P = 0.047）。ROC分析显示，列线图模型预测模型组和验证组发生感染的准确性分别为0.895和0.864；Hosmer-Lemeshow检验显示拟合度良好。

CABG术后手术部位感染的发生与多个临床因素有关，如高龄、基础疾病史、围术期抗菌药物不合理使用、手术时间> 4 h和手术室探视，医护人员应充分认知并采取严格的感染管理措施以减少感染的发生。

To investigate the main risk factors of surgical site infection after coronary artery bypass grafting (CABG), and to construct a quantitative nomogram risk prediction model, and put forward targeted management strategies.

Total of 302 patients with CABG admitted to Yancheng Third People’s Hospital Affiliated to Nanjing Medical University from May 2015 to may 2019 were retrospectively summarized as the model group. The clinical data of infection and non-infection patients were compared, including gender, age, basic disease history, body mass index, American Society of Anesthesiologists (ASA) score, routine skin preparation, unreasonable use of perioperative antibiotics, operation time > 4 h, operating room visit and continuous use of the same operating room, and the main risk factors were screened by multivariate Logistic regression analysis, and the risk prediction model of nomogram was established. A total of 211 patients with CABG from June 2019 to December 2020 were enrolled as the validation group and accepted the infection management strategy. Finally, receiver operating curve (ROC) was used to evaluate the efficacy of nomogram model for infection in model group and validation group.

In model group, 11 patients with infection (3.64%, 11/302) were diagnosed. Univariate analysis showed that patients with infection were older, with higher proportions of basic disease history (hypertension and diabetes) and obesity, higher ASA score, higher rates of routine skin preparation, irrational use of antibiotics during perioperation, operation time > 4 h, operating room visitation and continuous use of the same operating room than non-infected patients, with significant differences (all P < 0.05). Logistic regression analysis showed that elder (OR = 1.58, 95%CI: 1.12-2.53, P = 0.011), basic disease history (OR = 2.63, 95%CI: 2.12-3.06, P = 0.001), unreasonable use of antibiotics during perioperation (OR = 2.01, 95%CI: 1.55-2.69, P = 0.002), operation time > 4 h (OR = 3.11, 95%CI: 2.68-3.59, P = 0.001) and operating room visitation (OR = 1.24, 95%CI: 1.01-1.85, P = 0.024) were the main risk factors of surgical site infection after CABG, all with significant differences. The nomogram model was established by R software according to Weight (β value) of the main risk factors (elder, basic diseases, unreasonable use of antibiotics, operation time > 4 h, operating room visitation). There were two patients with infection in validation group (0.95%, 2/211), which was significantly lower than that of model group (Fisher’s exact probability method, taking one side P = 0.047). ROC analysis showed that the accuracy of nomogram model in predicting infection in model group and validation group were 0.895 and 0.864, respectively. The Hosmer-Lemeshow test showed a good fit.

Surgical site infection after CABG is related to many clinical factors, such as elder, basic disease history, unreasonable use of antibiotics during perioperation, operation time > 4 h and operating room visitation. Medical staff should fully understand these risk factors and take strict infection management measures to reduce the occurrence of infection.