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 summarize the research progress in risk prediction models for postoperative anastomotic leakage (AL) in gastric cancer, and to explore the advantages and limitations of models constructed using traditional statistical methods and machine learning (ML), thereby providing a theoretical basis for clinical precision prediction and early intervention. MethodBy analyzing domestic and international literature, the construction strategies of logistic regression, LASSO regression, and ML 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 ML 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 AL prediction models are evolving from single-factor analysis to multi-modal dynamic integration. Future efforts should combine artificial intelligence, multi-center prospective validation, and clinical studies to advance the development of precise and individualized predictive tools for patients.
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.