Neonatal sepsis (NS) remains a major cause of morbidity and mortality in neonates, but data on the etiology and antibiotic susceptibility patterns of pathogens are limited. The aim of this study was to analyze the clinical characteristics, risk factors, and the antibiotic susceptibility patterns of pathogenic microbes associated with NS at a tertiary children's hospital in Shanghai, China. Episodes of blood culture-proven sepsis in the neonatal intensive care unit (NICU) of Children's Hospital of Fudan University from January 2013 to August 2017 were retrospectively reviewed. Collected data included demographics, perinatal risk factors, clinical symptoms, laboratory values, microbiology results and their antimicrobial susceptibility. Data for early-onset neonatal sepsis (EONS) and late-onset neonatal sepsis (LONS) were compared. The 341 of 976 culture-positive cases were selected, including 161 EONS cases (47.21% of 341) and 180 LONS cases (52.79% of 341). 635 incomplete cases were excluded. There was significant difference in risk factors between the EONS group and LONS group including birth weight, gestational age, 1-minute Apgar score, respiratory support, and the use of peripherally insertion central catheter (PICC). Clinical symptoms such as fever, feeding intolerance, abdominal distension, and neonatal jaundice, and laboratory results such as hemoglobin and lymphocyte counts also showed between-group differences. Staphylococcus epidermidis (22.87%), Escherichia coli (9.68%), Alcaligenes xylosoxidans (9.38%) and Klebsiella pneumoniae (9.09%) remain the principal organisms responsible for neonatal sepsis. Most isolates of Gram-positive bacteria were sensitive to vancomycin, linezolid, minocycline and tigecycline, of which more than 90% were resistant to penicillin. Most isolates of Gram-negative bacteria were sensitive to amikacin and imipenem and resistant to ampicillin. Fungus was sensitive to antifungal agents. Better medical decisions, especially early detection and appropriate initial antimicrobial therapy can be made after understanding the different clinical features and pathogens of EONS and LONS.

The clinical pharmacy practice provided by pharmacists aims to improve the rational use of medication and enhance the quality of life of patients. Although clinical pharmacy in the USA has been developing for almost 60 years, it began in China only in 2005. Despite this, rapid development in clinical pharmacy has been achieved under the support of China’s Ministry of Health. This article aims to compare the differences between, and similarities of, clinical pharmacy practice in China and the USA.

Background: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with sepsis-related deaths contributing substantially to the overall burden on healthcare systems worldwide. The primary objective was to construct and evaluate a machine learning (ML) model for forecasting 28-day all-cause mortality among ICU sepsis patients.Methods: Data for the study was sourced from the eICU Collaborative Research Database (eICU-CRD) (version 2.0). The main outcome was 28-day all-cause mortality. Predictor selection for the final model was conducted using the least absolute shrinkage and selection operator (LASSO) regression analysis and the Boruta feature selection algorithm. Five machine learning algorithms including logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (lightGBM) were employed to construct models using 10-fold cross-validation. Model performance was evaluated using AUC, accuracy, sensitivity, specificity, recall, and F1 score. Additionally, we performed an interpretability analysis on the model that showed the most stable performance.Results: The final study cohort comprised 4564 patients, among whom 568 (12.4%) died within 28 days of ICU admission. The XGBoost algorithm demonstrated the most reliable performance, achieving an AUC of 0.821, balancing sensitivity (0.703) and specificity (0.798). The top three risk predictors of mortality included APACHE score, serum lactate levels, and AST.Conclusion: ML models reliably predicted 28-day mortality in critically ill sepsis patients. Of the models evaluated, the XGBoost algorithm exhibited the most stable performance in identifying patients at elevated mortality risk. Model interpretability analysis identified crucial predictors, potentially informing clinical decisions for sepsis patients in the ICU.