Objective To investigate the characteristics of the pathogens causing bloodstream infection after general surgery in infant and young children patients, and to provide the references for disease treatment and nosocomial infection control. Methods The clinical and laboratory examination data after general surgery in infant and young children patients, who were admitted to our hospital from January 2012 to March 2017, were retrospectively collected. The pathogens and drug resistance were analyzed by SPSS 18.0 software. Results In this study, 109 cases were included, and 117 strains of the pathogens were isolated, including 53 isolates (45.3%) of gram negative bacteria, 41 isolates (35.0%) of gram positive bacteria, and 23 isolates (19.7%) of fungi. Escherichia coli (16/117, 13.7%), Enterococcus faecium (13/117, 11.1%), Candida parapsilosis (12/117, 10.3%), Klebsiella pneumoniae (9/117, 7.7%) and Enterococcus faecalis (8/117, 6.8%) were the top 5 species. Strains producing extended-spectrum beta-lactamase accounted for 87.5% of E. coli (14/16) and 44.4% (4/9) of K. pneumoniae isolates. Both E. faecium and E. faecalis were susceptible to vancomycin. C. parapsilosis showed the susceptibility to the antifungal agents. Conclusion Gram negative bacteria are predominant pathogens causing bloodstream infection after general surgery in infant and young children patients, and infection caused by resistant isolates should be prevented and controlled.
ObjectiveTo construct a machine learning (ML) model incorporating preoperative, intraoperative, and early postoperative variables to predict cardiac surgery-associated acute kidney injury (CSA-AKI), thereby providing decision support for early clinical warning and intervention. MethodsPatients who underwent cardiac surgery at Nanjing Medical University Affiliated Nanjing Hospital from 2020 to 2022 were included in this study. The cohort was randomly divided into a training set and a validation set at a ratio of 7:3. Five ML models were constructed, including random forest (RF), logistic regression (LR), gradient boosted decision tree (GBDT), extreme gradient boosting (XGBoost), and support vector machines (SVM). Model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), and the SHapley Additive exPlanations (SHAP) method was applied for model interpretation. ResultsA total of 3 679 patients were included, comprising 2 217 males and 1 462 females, with a median age of 64 (56, 70) years. Fifteen feature variables were incorporated: history of heart failure, age, body mass index, smoking history, creatinine, cardiopulmonary bypass time, total blood transfusion volume, total urine output, uric acid, hemoglobin, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score, European System for Cardiac Operative Risk Evaluation (EuroSCORE), postoperative white blood cell count, postoperative lactate, and postoperative lymphocyte count. Among the five ML models, LR, RF, GBDT, and XGBoost showed no statistical differences in performance within the validation set; however, considering the dimensions of performance, model robustness, and clinical interpretability, the LR model was deemed superior. SHAP analysis quantified the predictive weights of each variable (indicating that a history of heart failure and advanced age were high-risk features) and visually demonstrated the underlying causes of personalized risk for individual patients. Based on the optimized LR model, an online web-based risk calculator was successfully developed to output personalized probability of CSA-AKI occurrence. ConclusionA clinical prediction model for CSA-AKI risk is constructed using ML techniques, demonstrating favorable predictive performance and interpretability. This risk assessment tool can provide early warnings for clinicians and assist in optimizing postoperative treatment strategies, thereby reducing the risk of CSA-AKI occurrence.