The Effect of Catheter-Related Infection Control Education on Surgical Nurses' Knowledge Levels and Attitudes: A Randomized Controlled Trial

BACKGROUND

Catheters are commonly used in health care. As nurses play an active role in the prevention of catheter-related infections, their knowledge and attitudes on this subject are important. The goal of this study was to determine the effect of an educational intervention about catheter-related infection control precautions on nurses' knowledge levels and attitudes.

METHOD

This study was a single-center randomized controlled trial. The intervention group (n = 35) received evidence-based face-to-face education. The control group (n = 35) received routine in-service training. The nurses' knowledge and attitudes were assessed before, immediately after, and 3 months after the education.

RESULTS

After the training, the intervention group had statistically higher total scores than the control group on both scales immediately after the training (p < .001 and p = .008, respectively) and 3 months after the training (p = .001 and p < .001, respectively).

CONCLUSION

The evidence-based structured educational intervention about catheter-related infection prevention practices positively affected the knowledge and attitudes of surgical nurses. [J Contin Educ Nurs. 202x;5x(x):xx-xx.].

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.

Examining instruments used to measure knowledge of catheter-associated urinary tract infection prevention in health care workers: A systematic review

BACKGROUND

Catheter-associated urinary tract infection (CAUTI) is the most frequently occurring health care-associated infection among hospitalized patients. Adequate knowledge of CAUTI in health care workers supports effective prevention and control of the infection. This systematic review assesses instruments used to assess knowledge of CAUTI prevention in health care workers to inform future research. The catheter lifecycle model was used to evaluate the conceptual framework upon which the measurement instruments were based. Finally, the psychometric quality of these instruments was evaluated.

METHODS

Five electronic databases were searched for published studies and instruments. The COnsensus-based Standards for the selection of health status Measurement INstruments checklist was used to assess the psychometric quality reporting of the instruments.

RESULTS

Fifteen studies met the review inclusion criteria and 13 instruments were available for review. Most of the instruments did not address all knowledge components essential for CAUTI prevention as defined by the catheter lifecycle model. The psychometric quality of the instruments was not sufficiently evaluated.

CONCLUSIONS

Few instruments are available for CAUTI prevention knowledge measurement. The instruments were not closely aligned with the catheter lifecycle model as a framework. If CAUTI knowledge cannot be measured accurately using an effective instrument, this has the potential to impact negatively on clinical care and the focus of interventions. There is a need for a standardized instrument for the evaluation of CAUTI prevention knowledge so that targeted interventions can address knowledge deficits.