Antimicrobials are commonly used to treat acute respiratory tract infection in adults. Furthermore, their overuse has raised concern. We conducted a field survey study that included 170 medical institutions from January 2008 to June 2010. The purpose of this study was to clarify the relationship between the rate of antimicrobial use and patient outcomes with each indication. The study included 1753 patients diagnosed with acute respiratory tract infection. Antimicrobials were used for treatment of 1420 of these patients, whereas 333 cases were not treated with antimicrobials. After 3 days of treatment, patients administered antimicrobials experienced a higher improvement rate than those who did not receive antimicrobial treatment (92.2% vs. 83.3%, p < 0.0001). However, after 7 days of treatment, the rates of improvement for patients in both groups were similar (95.0% and 93.4%, respectively, p = 0.2391). In addition, according to the criteria for the usage of antimicrobials described in the Japanese Respiratory Society guidelines for the management of respiratory tract infection in adults, the patients were classified into the 3 categories (6 indication factors for antimicrobial use): Grade 1, ≤ 2 factors; Grade 2, 3-4 factors; Grade 3, 5-6 factors). The indication factors considered were the following: 1) temperature; 2) purulent sputum or nasal discharge; 3) tonsillar enlargement and tonsillolith/white puss; 4) middle otitis/sinusitis; 5) inflammatory reaction; and 6) high-risk patients. The results indicate that the improvement observed after 3 days of treatment in Grade 2 and Grade 3 patients was significantly higher with antimicrobial treatment than without antimicrobial treatment. In conclusion, the administration of antimicrobials is not recommended in younger patients with no underlying disease. However, the use of antimicrobials is required in patients with a higher relative risk that corresponds to the presence of ≥ 3 of the 6 indication factors for antimicrobial use.

Download full-text PDF

Source

Publication Analysis

Top Keywords

respiratory tract
16
tract infection
16
acute respiratory
12
infection adults
12
days treatment
12
antimicrobial treatment
12
indication factors
12
patients
9
study included
8
factors antimicrobial
8

Similar Publications

Neisseria gonorrhoeae, which causes the sexually transmitted infection gonorrhea and Neisseria meningitidis, a leading cause of bacterial meningitis and septicemia, are closely related human-restricted pathogens that inhabit distinct primary mucosal niches. While successful vaccines against invasive meningococcal disease have been available for decades, the rapid rise in antibiotic resistance has led to an urgent need to develop an effective gonococcal vaccine. Several surface antigens are shared among these two pathogens, making cross-species protection an exciting prospect.

View Article and Find Full Text PDF

Background And Objective: Coughing events are eruptive sources of virus-laden droplets/droplet nuclei. These increase the risk of infection in susceptible individuals during airborne transmission. The oral cavity functions as an exit route for exhaled droplets.

View Article and Find Full Text PDF

Unlabelled: Respiratory and encephalitic virus infections represent a significant risk to public health globally. Detailed investigations of immunological responses and disease outcomes during sequential virus infections are rare. Here, we define the impact of influenza virus infection on a subsequent virus encephalitis.

View Article and Find Full Text PDF

This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: To determine the diagnostic accuracy of transtracheal ultrasound for detecting endotracheal intubation in adult patients. Secondary objectives Secondary objectives include assessing the diagnostic accuracy of transtracheal ultrasound amongst the following subgroups: setting (e.

View Article and Find Full Text PDF

IntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!