ArticleBiomedical engineering online2025
A nomogram prediction model for the risk of intra-abdominal infection after endoscopic full-thickness resection of gastric submucosal tumors.
Article in Biomedical engineering online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
backgroundThis study aimed to investigate the risk factors for complications of intra-abdominal infection (IAI) after endoscopic total resection of gastric submucosal tumors (GSMT) and to establish a nomogram prediction model for the occurrence of IAI.
methodsClinical data of patients with GSMT who underwent endoscopic full-thickness 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, least absolute shrinkage and selection operator (LASSO) 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.
resultsA 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, LASSO, and multivariate logistic regression analyses showed that age (OR = 1.208, 95% CI: 1.012-1.442), preoperative CAR (OR = 1.259, 95% CI: 1.106-1.433), and surgical time (OR = 1.099, 95% CI: 1.037-1.165) were independent risk factors for IAI following EFR (p < 0.05). A Nomogram model was established based on the above factors. The Hosmer-Lemeshow test value of this model was 3.256 (p = 0.741). The AUC value of the predictive model was 0.968 (95% CI: 0.915-1.000), with a C-index of 0.968 (95% CI: 0.915-1.000), revealing that the nomogram model had good accuracy and discrimination. Decision curve analysis (DCA) supported that the nomogram model provided the highest net benefit when the risk threshold ranged from 0.1 to 1.0.
conclusionsAge, preoperative CAR level, and surgical 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.
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