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- Elbediwi, M, et al.
(författare)
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Global Burden of Colistin-Resistant Bacteria: Mobilized Colistin Resistance Genes Study (1980-2018)
- 2019
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Ingår i: Microorganisms. - : MDPI AG. - 2076-2607. ; 7:10
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Tidskriftsartikel (refereegranskat)abstract
- Colistin is considered to be an antimicrobial of last-resort for the treatment of multidrug-resistant Gram-negative bacterial infections. The recent global dissemination of mobilized colistin resistance (mcr) genes is an urgent public health threat. An accurate estimate of the global prevalence of mcr genes, their reservoirs and the potential pathways for human transmission are required to implement control and prevention strategies, yet such data are lacking. Publications from four English (PubMed, Scopus, the Cochrane Database of Systematic Reviews and Web of Science) and two Chinese (CNKI and WANFANG) databases published between 18 November 2015 and 30 December 2018 were identified. In this systematic review and meta-analysis, the prevalence of mcr genes in bacteria isolated from humans, animals, the environment and food products were investigated. A total of 974 publications were identified. 202 observational studies were included in the systematic review and 71 in the meta-analysis. mcr genes were reported from 47 countries across six continents and the overall average prevalence was 4.7% (0.1–9.3%). China reported the highest number of mcr-positive strains. Pathogenic Escherichia coli (54%), isolated from animals (52%) and harboring an IncI2 plasmid (34%) were the bacteria with highest prevalence of mcr genes. The estimated prevalence of mcr-1 pathogenic E. coli was higher in food-animals than in humans and food products, which suggests a role for foodborne transmission. This study provides a comprehensive assessment of prevalence of the mcr gene by source, organism, genotype and type of plasmid.
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- Mei, J, et al.
(författare)
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Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
- 2020
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 10:12, s. e044028-
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.DesignRetrospective cohort study.SettingFive designated tertiary hospitals for COVID-19 in Hubei province, China.ParticipantsWe routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.MethodsThe model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers’ D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measuresThe primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.ResultsThe full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.ConclusionThe prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.
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