Investigation of multiple nosocomial infections using a semi-Markov multi-state model.

BACKGROUND: The prevalence of multiple nosocomial infections (MNIs) is on the rise, however, there remains a limited comprehension regarding the associated risk factors, cumulative risk, probability of occurrence, and impact on length of stay (LOS). METHOD: This multicenter study includes all hospitalized patients from 2020 to July 2023 in two sub-hospitals of a tertiary hospital in Guangming District, Shenzhen. The semi-Markov multi-state model (MSM) was utilized to analyze risk factors and cumulative risk of MNI, predict its occurrence probability, and calculate the extra LOS of nosocomial infection (NI). RESULTS: The risk factors for MNI include age, community infection at admission, surgery, and combined use of antibiotics. However, the cumulative risk of MNI is lower than that of single nosocomial infection (SNI). MNI is most likely to occur within 14 days after admission. Additionally, SNI prolongs LOS by an average of 7.48 days (95% Confidence Interval, CI: 6.06-8.68 days), while MNI prolongs LOS by an average of 15.94 days (95% CI: 14.03-18.17 days). Furthermore, the more sites of infection there are, the longer the extra LOS will be. CONCLUSION: The longer LOS and increased treatment difficulty of MNI result in a heavier disease burden for patients, necessitating targeted prevention and control measures.

Generating a skeleton reaction network for reactions of large-scale ReaxFF MD pyrolysis simulations based on a machine learning predicted reaction class.

The reactive molecular dynamics using ReaxFF provides an effective means to generate global reactions for pyrolysis of realistic fuel mixtures. The reactions from large-scale pyrolysis simulations of a fuel mixture may be characterized by multiple reaction sites, explosion of intermediate species structures, and scattered contribution of diversified pathways to product species. This work proposes an approach of SRG-Reax aiming at generating skeleton reaction networks based on reaction patterns or classes of reaction centers from huge reactions obtained from ReaxFF MD simulations of realistic fuel pyrolysis. SRG-Reax (Skeleton Reaction network Generation for ReaxFF MD) is implemented through building a semi-supervised machine learning model of tri-training for predicting the reaction classes of pyrolysis reactions based on an extended reaction center. Three different reaction center descriptions of reaction features and reaction transformation fingerprints are employed as inputs for developing the tri-training classifier. Major reaction pathways can be identified based on reaction class ratios and product species ratios calculated by merging reaction pathways of the same reaction class. The SRG-Reax approach was applied in skeleton reaction network generation for RP-3 pyrolysis based on the ReaxFF MD simulations of a high-fidelity 45-component RP-3 fuel model. The skeleton reaction networks for n-paraffins, iso-paraffins, cycloparaffins, olefins, and aromatics in RP-3 pyrolysis were obtained. The reaction class ratios and product species ratios in the obtained skeleton reaction network provide comprehensive intuitive insight into global pyrolysis chemistry. SRG-Reax has the potential to obtain relatively complete skeleton reaction networks for the pyrolysis of hydrocarbon fuel, polymers, biomass, coal, and more.

Impact of a quality control circle on the incidence of catheter-associated urinary tract infection: An interrupted time series analysis.

BACKGROUND: To report a quality control circle (QCC) activity on the theme of reducing the incidence of catheter-associated urinary tract infection (CAUTI), and used an interrupted time series analysis to evaluate the impact of the QCC. METHODS: In a general tertiary hospital in Shenzhen, China, we carried out a QCC activity with the theme of reducing CAUTI from April 2017 to December 2017. Before the QCC, we carried out the routine measures; during the QCC, we implemented usual measures and the countermeasures of QCC, and after the QCC, we performed the routine measures and adhered to the core measures of QCC. The interrupted time series analysis method was used to analyze the changes in the CAUTI incidence during the 3 stages. RESULTS: Before, during, and after the QCC activities, the catheter use ratios and mean indwelling time both had a downward trend; meanwhile, the compliance rate of CAUTI prevention measures showed an upward trend. After the interventions, the CAUTI incidence decreased by 1.317‰ immediately, then gradually decreased by 0.510‰ per month. After the completion of QCC, the CAUTI incidence increased by 0.266‰ immediately and increased by 0.070‰ over time, but the difference was not statistically significant. CONCLUSIONS: The CAUTI incidence is reduced through QCC, providing a useful reference for the prevention of CAUTI and the development of medical quality improvement activities.