Association of SARS-CoV-2 Status and Antibiotic-Resistant Bacteria with Inadequate Empiric Therapy in Hospitalized Patients: a US Multicenter Cohort Evaluation (july 2019 - October 2021)
BMC Infectious Diseases(2023)SCI 3区
Abstract
Background Antibiotic usage and antibiotic resistance (ABR) patterns changed during the COVID-19 pandemic. Inadequate empiric antibiotic therapy (IET) is a significant public health problem and contributes to ABR. We evaluated factors associated with IET before and during the COVID-19 pandemic to determine the impact of the pandemic on antibiotic management. Methods This multicenter, retrospective cohort analysis included hospitalized US adults who had a positive bacterial culture (specified gram-positive or gram-negative bacteria) from July 2019 to October 2021 in the BD Insights Research Database. IET was defined as antibacterial therapy within 48 h that was not active against the bacteria. ABR results were based on susceptibility testing and reports from local facilities. Multivariate analysis was used to identify risk factors associated with IET in patients with any positive bacterial culture and ABR-positive cultures, including multidrug-resistant (MDR) bacteria. Results Of 278,344 eligible patients in 269 hospitals, 56,733 (20.4%) received IET; rates were higher in patients with ABR-positive (n = 93,252) or MDR-positive (n = 39,000) cultures (34.9% and 45.0%, respectively). Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-positive patients had significantly higher rates of IET (25.9%) compared with SARS-CoV-2-negative (20.3%) or not tested (19.7%) patients overall and in the ABR and MDR subgroups. Patients with ABR- or MDR-positive cultures had more days of therapy and longer lengths of stay. In multivariate analyses, ABR, MDR, SARS-CoV-2-positive status, respiratory source, and prior admissions were identified as key IET risk factors. Conclusions IET remained a persistent problem during the COVID-19 pandemic and occurred at higher rates in patients with ABR/MDR bacteria or a co-SARS-CoV-2 infection.
MoreTranslated text
Key words
Antibiotic resistance,SARS-CoV-2,COVID-19,Inadequate empiric therapy,Bacteria
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话