ObjectiveTo assess whether pre-operative use of infliximab (IFX) will increase the risk of post-operative infectious complications in patients with inflammatory bowel disease (IBD). MethodsPubmed, Web of Science, CBM, CNKI and Wanfang database were searched for all the trials that investigated the effects of infliximab on postoperative infectious complication rates in patients with IBD between January 1990 and April 2013. Two reviewers independently screened the literature according to the inclusion and exclusion criteria, extracted data and assessed the quality of the included studies. Then, meta-analysis was performed using RevMan 5.1 software. ResultsTotally, 14 cohort studies were finally included in the review. There was no significant difference on infectious complications [RR=0.99, 95%CI (0.47, 2.07), P=0.97] between IFX groups and control groups with ulcerative colitis. The same results were found in patients with Crohn's disease on infectious complications [RR=1.32, 95%CI (0.87, 1.98), P=0.19]. ConclusionPre-operative infliximab use is safe and does not increase the risk of post-operative infectious complications in patients with IBD.
ObjectiveTo investigate the risk factors affecting the occurrence of infectious complications after radical gastrectomy for gastric cancer, and to establish a risk prediction Nomogram model. MethodsThe clinicopathologic data of 429 primary gastric cancer patients who underwent radical resection for gastric cancer at the Second Department of General Surgery of Shaanxi Provincial People’s Hospital between January 2018 and December 2020 were retrospectively collected to explore the influencing factors of infectious complications using multivariate logistic regression analyses, and to construct a prediction model based on the results of the multivariate analysis, and then to further validate the differentiation, consistency, and clinical utility of the model. ResultsOf the 429 patients, infectious complications occurred in 86 cases (20.05%), including 53 cases (12.35%) of pulmonary infections, 16 cases (3.73%) of abdominal infections, 7 cases (1.63%) of incision infections, and 10 cases (2.33%) of urinary tract infections. The results of multivariate logistic analysis showed that low prognostic nutritional index [OR=0.951, 95%CI (0.905, 0.999), P=0.044], long surgery time [OR=1.274, 95%CI (1.069, 1.518), P=0.007], American Society of Anesthesiologists physical status classification (ASA) grade Ⅲ–Ⅳ [OR=9.607, 95%CI (4.484, 20.584), P＜0.001] and alcohol use [OR=3.116, 95%CI (1.696, 5.726), P＜0.001] were independent risk factors for the occurrence of infectious complications, and a Nomogram model was established based on these factors, with an area under the ROC of 0.802 [95%CI (0.746, 0.858)]; the calibration curves showed that the probability of occurrence of infectious complications after radical gastrectomy predicted by the Nomogram was in good agreement with the actual results; the decision curve analysis showed that the Nomogram model could obtain clinical benefits in a wide range of thresholds and had good practicality.ConclusionsClinicians need to pay attention to the perioperative management of gastric cancer patients, fully assess the patients’ own conditions through the prediction model established by prognostic nutritional index, surgery time, ASA grade and alcohol use, and take targeted interventions for the patients with higher risks, in order to reduce the risk of postoperative infectious complications.
Objective To explore the application of combined optimized machine learning algorithm for predicting the risk model of postoperative infectious complications of gastric cancer and to compare the accuracy with other algorithms, so as to find reliable biomarkers for early diagnosis of postoperative infection of gastric cancer. Methods The clinical data of 420 patients with gastric cancer at the Third Affiliated Hospital of Anhui Medical University from May 2018 to April 2023 were retrospectively analyzed and the patients were randomly divided into training set and validation set. Univariate analysis was used to determine the risk factors of postoperative infectious complications. Six conventional machine learning models are constructed using the training set: linear regression, random forest, SVM, BP, LGBM, XGBoost, and MGA-XGBoost model. The validation set was used to evaluate the seven models through evaluation indicators such as ACC, precision, ROC and AUC. Results Postoperative infectious complications were significantly correlated with age, operation time, diabetes, extent of resection, combined resection, stage, preoperative albumin, perioperative blood transfusion, preoperative PNI, LCR and LMR. Among the seven machine learning models, the MGA-XGBoost model performed best. Among the seven machine learning models, the MGA-XGBoost model performed best, with AUC of 0.936, ACC of 0.889, recall of 0.6, F1-score of 0.682, and precision of 0.79 on the validation set. Diabetes had the greatest influence on the internal structure of the model. Conclusion This study proves that the MGA-XGBoost model incorporating comprehensive inflammation indicators can predict postoperative infectious complications in patients with gastric cancer.