Nomogram of intra-abdominal infection after surgery in patients with gastric cancer: A retrospective study

A nutrition-based nomogram for predicting intra-abdominal infection after D2 radical gastrectomy for gastric cancer

BACKGROUND

This study aims to construct a nutrition-based nomogram for predicting the risk of intra-abdominal infection (IAI) after D2 radical gastrectomy for gastric cancer (GC).

METHODS

We retrospectively analyzed the clinical data of 404 individuals who received D2 radical gastrectomy for GC. Four preoperative nutrition-related indicators, the nutritional risk screening (NRS) 2002 score, albumin (ALB), prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score, were collected and calculated. Multivariate logistic regression analysis was utilized to screen the independent risk factors for IAI following D2 radical gastrectomy for GC. The area under the receiver operating characteristics (ROC) curve (AUROC) was computed. A nomogram was established to forecast postoperative IAI using the independent risk factors.

RESULTS

The NRS2002 score, ALB, PNI, CONUT score, fasting blood glucose (FBG), American Society of Anesthesiologists (ASA) score, type of resection, multi-visceral resection, perioperative blood transfusion, and the tumor, node, metastasis (TNM) stage were significantly associated with postoperative IAI. Considering the collinearity between these nutrition-related variables, four multivariate logistic regression analyses were separately performed, and four independent nutrition-based models were constructed. Of these, the best one was the model based on the three indicators of NRS2002 score, FBG, and multi-visceral resection, which had an AUROC of 0.744 (0.657-0.830), with a specificity of 75.6% and a sensitivity of 62.9%. Further, a nomogram was constructed to estimate the probability of IAI following D2 radical gastrectomy. The internal validation was carried out using the bootstrap method with self-help repeated sampling 1000 times, and the concordance index (c-index) was determined at 0.742 (95% CI = 0.739-0.745). The calibration curve revealed that the predictive results of the nomogram were in excellent concordance with the actual observations. The decision curve analysis (DCA) indicates that the nomogram has excellent clinical benefit.

CONCLUSION

The nomogram constructed based on NRS2002 score, FBG, and multi-visceral resection has good predictive capacity for the incidence of IAI following D2 radical gastrectomy and provides a reference value for clinicians to assess the risk of IAI occurrence.

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

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.

A predictive model for intraabdominal infection after radical gastrectomy in elderly patients

Gastric cancer (GC) is one of the most common malignant tumors worldwide and the fourth leading cause of cancer-related deaths, with a relatively high incidence among the elderly population. Surgical resection is the mainstay treatment for GC and is currently the only cure. However, the incidence of postoperative intraabdominal infections remains high and seriously affects the prognosis. This study aimed to explore the risk factors for intraabdominal infections after radical gastrectomy in elderly patients and to establish and validate a risk prediction model. We collected the clinical data of 322 GC patients, who underwent radical gastrectomy at the General Surgery Department of China Medical University Dandong Central Hospital from January 2016 to January 2023. The patients were divided into an infected group (n = 27) and a noninfected group (n = 295) according to whether intraabdominal infections occurred postoperatively. A nomogram risk prediction model for the occurrence of postoperative intraabdominal infections was developed. All patients were randomized into a training set (n = 225) and a validation set (n = 97) in a 7:3 ratio, and the model was internally validated. Of the 322 patients, 27 (8.3%) experienced postoperative intraabdominal infections. Single-factor analysis revealed associations of intraabdominal infection with body mass index, glucose, hemoglobin, albumin, and other factors. The multifactorial analysis confirmed that body mass index, glucose, hemoglobin, albumin, surgical duration, and bleeding volume were independent risk factors for intraabdominal infections. The nomogram constructed based on these factors demonstrated excellent performance in both the training and validation sets. A nomogram model was developed and validated to predict the risk of intraabdominal infection after radical gastrectomy. The model has a good predictive performance, which could help clinicians prevent the occurrence of intraabdominal infections after radical gastrectomy in elderly patients.