结直肠癌患者术后并发切口感染风险预测模型的构建OA

Objective To analyze the potential risk of postoperative incision infection in patients with colorectal cancer,to construct a risk prediction model for postoperative incision infection,and to evaluate its predictive value for postoperative incision infection.Methods A retrospective analysis was performed on 194 patients with colorectal cancer who underwent surgical treatment in the Department of General Surgery,the Fourth Affiliated Hospital of Soochow University from January 2021 to December 2024.According to the incidence of incision infection,they were divided into uninfected group(n=154)and infected group(n=40).Univariate and multivariate Logistic regression analysis was used to identify the key indicators affecting postoperative incision infection,so as to construct a prediction model.The ROC curve and AUC were used to verify the predictive efficacy of the model.Results There were significant differences in BMI,comorbid diabetes mellitus,comorbid anemia,preoperative chemoradiotherapy history,postoperative ALB level,type of operation,ASA score of anesthesia,duration of operation,length of incision,perioperative blood transfusion,stoma and anastomotic fistula between the infected group and the uninfected group(P<0.05).Multivariate analysis showed that BMI,comorbid diabetes mellitus,preoperative chemoradiotherapy history,and stoma were independent risk factors for postoperative incision infection in patients with colorectal cancer(P<0.05).The prediction model of postoperative incision infection:P=-4.708+0.886×BMI+1.156×diabetes mellitus+1.065×preoperative chemoradiotherapy history+1.163×stoma;the AUC of the prediction model was 0.936(P=0.000,95%CI:0.901-0.972),and the best cut-off value was-2.026.Conclusion BMI,comorbid diabetes mellitus,preoperative chemoradiotherapy history,and stoma status were identified as the primary independent risk factors for postoperative incisional infection.The application of a predictive model for postoperative incisional infection risk in colorectal cancer patients provides a scientific basis for clinical decision-making,accurately identifies high-risk patient groups,and guides the development of individualized prevention strategies.This approach effectively reduces the risk of postoperative incisional infection,thereby significantly improving patient recovery outcomes.