文献类型：期刊文献

英文题名：Development and validation of a clinical diagnostic model for surgical site infection after surgery in patients with gastric cancer

作者：Peng, Yiyun[1];Ma, Yuqi[1];Yang, Guoyuan[1];Huang, Yalong[1,3];Lin, Hao[2];Ma, Xiaolong[1];Yu, Yongjiang;Ma, Yuntao[4]

第一作者：Peng, Yiyun

通信作者：Ma, YT[1]

机构：[1]Gansu Univ Chinese Med, Clin Sch 1, Lanzhou, Peoples R China;[2]Lanzhou Univ, Clin Med Coll 1, Lanzhou, Peoples R China;[3]Lanzhou Univ, Gen Surg Dept, Hosp 1, Lanzhou, Peoples R China;[4]Gansu Prov Peoples Hosp, Gen Surg Clin Med Ctr, Weiyuan Rd, Lanzhou 730000, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Peoples Hosp, Gen Surg Clin Med Ctr, Weiyuan Rd, Lanzhou 730000, Peoples R China.

年份：2024

卷号：13

期号：9

起止页码：4659

外文期刊名：TRANSLATIONAL CANCER RESEARCH

收录：;Scopus(收录号：2-s2.0-85205428964);WOS:【SCI-EXPANDED(收录号:WOS:001339999900003)】；

语种：英文

外文关键词：Logistic regression; gastric cancer; nomogram; predictive modeling; surgical site infection (SSI)

摘要：Background: Surgical site infection (SSI) is a common and serious complication following gastric cancer surgery, often linked to patient age, surgery duration, and the surgical approach taken. Accurate prediction and personalized mitigation of SSI risk are crucial for improving surgical outcomes. While prior studies have focused on SSI rates after open and laparoscopic gastric cancer surgeries, it is important to also consider robot-assisted procedures. This study aims to develop a predictive model for SSI after radical gastric cancer surgery, validate it through external testing, and provide a reliable tool for clinical use. Methods: Data from 763 postoperative gastric cancer patients were analyzed, with 601 in the training set from Gansu Provincial People's Hospital and 162 in the validation set from The First Hospital of Lanzhou University. All available variables were considered as potential predictors, and factors influencing SSI post- surgery were identified using logistic regression. A nomogram model was then created for precise SSI risk prediction. Results: Among the 763 gastric cancer patients, 10.9% experienced postoperative SSI. Significant differences were noted in the American Society of Anesthesiologists (ASA) physical status classification system classification, preoperative albumin levels, surgical approach, and reconstruction techniques between groups. Age, surgery duration, surgical approach, total gastrectomy, and tumor diameter were identified as significant predictors of SSI. The nomogram model showed high predictive accuracy, with concordance index (C-index) values of 0.834 in the training set and 0.798 in the validation set. Calibration plots and decision curve analysis (DCA) further validated the model's performance. Conclusions: This study identified five key predictors of postoperative SSI in gastric cancer and developed a nomogram model to enhance SSI prediction. These findings have important implications for preventing SSI in gastric cancer surgeries.