ObjectiveTo systematically summarize the research progress in risk prediction models for postoperative anastomotic leakage in gastric cancer, and to explore the advantages and limitations of models constructed using traditional statistical methods and machine learning, thereby providing a theoretical basis for clinical precision prediction and early intervention. MethodBy analyzing domestic and international literature, the construction strategies of logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and machine learning models (support vector machine, random forest, deep learning) were systematically reviewed, and their predictive performance and clinical applicability were compared. ResultsThe traditional logistic regression and LASSO regression models performed excellently in terms of interpretability and in small-sample scenarios but were limited by linear assumptions. The machine learning models significantly enhanced predictive capabilities for complex data through non-linear modeling and automatic feature extraction, but required larger data scales and had higher demands for interpretability. ConclusionsDifferent prediction models have their own advantages and limitations; in practical clinical applications, they should be flexibly selected or complementarily applied based on specific scenarios. Current anastomotic leakage prediction models are evolving from single factor analysis to multi-modal dynamic integration. Future efforts should combine artificial intelligence and multi-center prospective clinical studies to validate, so advancing the development of precise and individualized anastomotic leakage predictive tools for patients after gastric cancer resection.
ObjectiveTo evaluate existing predictive models for surgical site infection (SSI) following colorectal cancer (CRC) surgery, aiming to provide a scientific basis for refining risk prediction models and developing clinically practical and widely applicable screening tools. MethodA comprehensive review of existing literature on predictive models for SSI following CRC surgery, both domestically and internationally, were conducted. ResultsThe determination of SSI following CRC surgery primarily relied on the Centers for Disease Control and Prevention standard of USA, which presented issues of consistency and accuracy. Various predictive models had been developed, including traditional statistical models and machine learning models, with 0.991 of an area under the operating characteristic curve of predictive model. However, most studies were based on retrospective and single-center data, which limited their applicability and accuracy. ConclusionsAlthough existing models provide strong support for predicting SSI following CRC surgery, there is a need for multi-center, prospective studies to enhance the generalizability and accuracy of these models. Additionally, future research should focus on improving model interpretability to better apply them in clinical practice, providing personalized risk assessments and intervention strategies for patients.
ObjectiveTo systematically review the research progress on risk factors and predictive models for postoperative pulmonary infection (POPI) in gastric cancer patients, aiming to provide a reference for the early identification of high-risk patients and the optimization of clinical interventions. MethodsBy reviewing relevant domestic and international studies in recent years, the key risk factors for POPI in gastric cancer were summarized. And the construction methodologies, efficacy, and clinical application value of the latest predictive models developed in the last three years were evaluated. ResultsIn addition to traditional risk factors, recent studies had further confirmed the significant predictive value of novel factors for POPI following gastric cancer surgery, including nutritional-immune-inflammatory markers (such as prognostic nutritional index, C-reactive protein to albumin ratio, C-reactive protein-albumin-lymphocyte index), preoperative frailty, sarcopenia, and specific surgical approaches (e.g., differences between totally laparoscopic and laparoscopically assisted gastrectomy). Regarding predictive models, nomogram models developed based on multivariate logistic regression analysis and risk scoring systems had demonstrated favorable performance in both internal and partial external validations, with the area under the receiver operating characteristic curve mostly ranging from 0.74 to 0.97. Notably, composite models that integrate nutritional and immune-inflammatory markers with frailty assessments had shown superior predictive accuracy and clinical applicability. ConclusionsThis review provides a novel predictive perspective based on emerging biomarkers and functional assessments for the early identification of high-risk populations of POPI following gastric cancer surgery. Future research should prioritize the validation and refinement of existing models through multicenter collaboration, ultimately transforming them into more effective clinical risk assessment tools to guide precision prevention.
Primary sarcopenia (PS) is an age-related degenerative disorder characterized by progressive loss of skeletal muscle mass and function. This review delineates three mechanisms whereby gut dysbiosis drives PS pathogenesis: decreased secondary bile acids inhibit farnesoid X receptor signaling, thereby attenuating muscle protein synthesis; disrupted short-chain fatty acid metabolism weakens free fatty acid receptor 2/adenosine monophosphate-activated protein kinase signaling, aggravating proteolysis and mitochondrial dysfunction; gut barrier impairment activates the endotoxin–Toll-like receptor 4-mediated inflammatory cascade, accelerating ubiquitin-proteasome system activation. Interventional evidence confirms that microbiota-targeted therapies (probiotics regulating bile acid metabolism and prebiotics enhancing short-chain fatty acid production) effectively improve muscle function. By synthesizing molecular evidence of the “gut-muscle axis”, this review offers theoretical references for developing PS prevention and treatment strategies.
Postoperative delirium (POD) is a common postoperative complication. Dysregulation of gut flora is involved in POD through mechanisms such as neuroinflammation, oxidative stress, deposition of β-amyloid, and aberrant production of metabolites of gut flora. Therefore, interventions to regulate gut flora, such as probiotics, prebiotics, and faecal microbiota transplantation, can alleviate cognitive dysfunction. This article reviews the mechanisms of gut flora in POD and its prevention and treatment strategies, with the aim of providing new ideas for the clinical prevention and treatment of POD.
