ArticleInfection and drug resistance2023
Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic.
Article in Infection and drug resistance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Geographical mapping and temporal trends of Acinetobacter baumannii carbapenem resistance: A comprehensive meta-analysis.PloS one · 2024Pooled it
- Virulence and resistance adaptation of Streptococcus agalactiae Ib/ST261 infecting bullfrogs in Hainan, China between 2019 and 2023.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2026Article
- Investigating the Antimicrobial Potential of 560 Compounds from the Pandemic Response Box and COVID Box against Resistant Gram-Negative Bacteria.Antibiotics (Basel, Switzerland) · 2024Article
- Distribution and Drug Resistance of Bacterial Infection in Hospitalized Patients at the Respiratory Department before and after the COVID-19 Pandemic in Guangzhou, China.Microorganisms · 2023Article
- Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study.Infection and drug resistance · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To investigate the distribution and drug resistance of pathogens among hospitalized patients in the respiratory unit during the COVID-19 pandemic, analyze the risk factors of drug resistance, construct a risk prediction model. Methods: This study isolated 791 strains from 489 patients admitted to the Affiliated Hospital of Chengdu University, who were retrospectively enrolled between December 2019 and June 2021. The patients were divided into training and validation sets based on a random number table method (8:2). The baseline information, clinical characteristics, and culture results were collected using an electronic database and WHONET 5.6 software and compared between the two groups. A risk prediction model for drug-resistant bacteria was constructed using multi-factor logistic regression. Results: Conclusion:
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