Heliyon (Nov 2024)
A nomogram prediction model for the risk of intra-abdominal infection after endoscopic full-thick resection of gastric submucosal tumors
Abstract
Background: This study aimed to investigate the risk factors for complication of intra-abdominal infection (IAI) after endoscopic full-thickness resection of gastric submucosal tumors (GSMT) and to establish a nomogram prediction model for the occurrence of IAI. Methods: Clinical data of patients with GSMT who underwent endoscopic full-thick resection (EFR) from January 2018 to July 2023 were retrospectively analyzed. The patients were divided into IAI and non-IAI groups according to whether IAI occurred during postoperative hospitalization. Univariate and multivariate logistic regression analyses were performed on the relevant clinical data of patients in the two groups to screen the independent influencing factors for the occurrence of IAI. The nomogram model was constructed based on the independent influencing factors. Model discrimination was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The consistency of model-predicted risk with actual risk was evaluated using the Hosmer-Lemeshow goodness-of-fit test. The clinical performance of the nomogram model was evaluated using decision curve analysis. Results: A total of 240 GSMT patients who underwent EFR procedures were finally included in this study, including 14 patients (5.83 %) in the IAI group and 226 patients in the non-IAI group. Univariate and multivariate logistic regression analyses showed that age (OR = 1.283, 95 % CI = 1.029–1.600), preoperative albumin (OR = 0.575, 95 % CI = 0.395–0.837), duration of operation (OR = 1.222, 95 % CI = 1.060–1.409), and hospitalization time (OR = 4.089, 95 % CI = 1.190–14.043) were independent influencing factors for the incidence of IAI in GSMT patients undergoing EFR surgery (P < 0.05). A Nomogram model was established based on the above factors. The Hosmer ⁃ Lemeshow test value of this model was 4.230 (P = 0.836). The AUC value of the predictive model was 0.992 (95 % CI: 0.983 to 1.000), with a C-index of 0.992 (95 % CI: 0.983–1.000), indicating that the nomogram model had good accuracy and discrimination. Decision curve analysis showed that the nomogram model had a good predictive performance. Conclusions: Age, preoperative albumin, duration of operation, and hospitalization time were independent influences on the occurrence of IAI in GSMT patients undergoing EFR surgery. A nomogram model based on these factors had a high predictive efficacy and may provide a guiding intervention for the prevention of postoperative IAI in GSMT patients.
