ABSTRACT
Objective: To establish a predictive modeling for the risk of bloodstream infection associated with peripherally inserted central catheter (PICC). Methods: Patients receiving PICC treatment in Shenzhen People's Hospital from June 2020 to December 2020 were retrospectively enrolled and divided into the infection group and the non-infection group according to the presence and absence of PICC-related infections. Then, relevant clinical information of patients was collected and the predictors of PICC-related infection were screened by the least absolute shrinkage and selection operator regression (LASSO) model. Besides, multivariate logistic regression was used to analyze the influencing factors of PICC-related infection, A nomogram was constructed based on the results of the multivariate analysis. Ultimately, a receiver operating characteristic (ROC) curve was plotted to analyze the application value of influencing factors to predict PICC-related infections. Results: A total of 505 patients were included, including 75 patients with PICC-related infections (14.85%). The main pathogen was gram-positive cocci. The predictors screened by LASSO included age >60 years, catheter movement, catheter maintenance cycle, insertion technique, immune function, complications, and body temperature ≥37.2 °C before PICC placement. Multivariate logistic regression analysis showed that independent risk factors of infections related to PICC included age >60 years [odds ratio (OR) = 1.722; 95% confidence interval (CI) = 1.312-3.579; P = 0.006], catheter movement (OR = 1.313; 95% CI = 1.119-3.240; P = 0.014), catheter maintenance cycle >7 days (OR = 2.199; 95% CI = 1.677-4.653; P = 0.000), direct insertion (OR = 1.036; 95% CI = 1.019-2.743; P = 0.000), poor immune function (OR = 2.322; 95% CI = 2.012-4.579; P = 0.000), complications (OR = 1.611; 95% CI = 1.133-3.454; P = 0.019), and body temperature ≥37.2 °C before PICC placement (OR = 1.713; 95% CI = 1.172-3.654; P = 0.012). Besides, the area under the ROC curve was 0.889. Conclusion: PICC-related infections are associated with factors such as age >60 years, catheter movement, catheter maintenance cycle, insertion technique, immune function, complications, and body temperature ≥37.2 °C before PICC placement. Additionally, the LASSO model is moderately predictive for predicting the occurrence of PICC-related infections.
ABSTRACT
Fe single atoms and N co-doped carbon nanomaterials (Fe-N-C) are the most promising oxygen reduction reaction (ORR) catalysts to replace platinum group metals. However, high-activity Fe single-atom catalysts suffer from poor stability owing to the low graphitization degree. Here, an effective phase-transition strategy is reported to enhance the stability of Fe-N-C catalysts by inducing increased degree of graphitization and incorporation of Fe nanoparticles encapsulated by graphitic carbon layer without sacrificing activity. Remarkably, the resulted Fe@Fe-N-C catalysts achieved excellent ORR activity (E1/2 = 0.829 V) and stability (19 mV loss after 30K cycles) in acid media. Density functional theory (DFT) calculations agree with experimental phenomena that additional Fe nanoparticles not only favor to the activation of O2 by tailoring d-band center position but also inhibit the demetallization of Fe active center from FeN4 sites. This work provides a new insight into the rational design of highly efficient and durable Fe-N-C catalysts for ORR.
ABSTRACT
BACKGROUND: Midline catheters (MCs) have been widely applied in clinical settings as they can provide painless venous access, thus improving the quality of life and reducing medical costs. Nursing-sensitive indicators (NSIs) are real and effective measures of nursing quality. Using evidence-based methods, we established the NSIs of MC care, with an attempt to provide a basis for evaluating and monitoring nursing quality for MC use. METHODS: An electronic search was performed in 5 databases including China National Knowledge Infrastructure (CNKI), Chinese Scientific Journal Database (VIP), Wanfang Data, PubMed, and Web of Science to identify studies that evaluated nursing quality during MC use. Two evaluators independently selected literature, extracted data, and evaluated the risk of bias. According to the Donabedian's structure-process-outcome model, we divided the NSIs into 3 levels. RESULTS: The established NSIs for MC use included 3 indicators (i.e., structure indicators, process indicators, and outcome indicators), among which there were 3 level-2 indicators and 7 level-3 indicators at the structure level, 2 level-2 indicators and 9 level-3 indicators at the process level, and 5 level-2 indicators and 17 level-3 indicators at the outcome level. CONCLUSIONS: The established NSIs for MC use offer a set of objective criteria for evaluating nursing performance during MC use and will help to improve nursing quality control.
