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, , , , , Abstract
Critical Care Science. 2025;37:e20250237
04-02-2025
DOI 10.62675/2965-2774.20250237
, , , , , To evaluate risk factors, molecular profiles, and hospital mortality of carbapenem-resistant Enterobacterales (CRE) infections in intensive care unit patients.
In this retrospective, multicenter cohort study, intensive care unit admissions from 52 intensive care units between January 2019 and December 2020 were analyzed in a nested case-control design. Patients with carbapenem-resistant Enterobacterales infections were propensity score-matched 1:1 to those with carbapenem-susceptible Enterobacterales infections. Hierarchical conditional logistic regression identified risk factors for carbapenem-resistant Enterobacterales, and multivariable logistic regression assessed the association of carbapenem-resistant Enterobacterales with 60-day in-hospital mortality. Molecular genotyping was also conducted.
Matching resulted in 250 carbapenem-resistant Enterobacterales patients and 250 carbapenem-susceptible Enterobacterales patients. Sepsis was more common in the carbapenem-resistant Enterobacterales group (58% versus 35%; p < 0.001). Risk factors for carbapenem-resistant Enterobacterales included major premorbid assistance requirements (OR 1.72, 95%CI 0.99 - 3.01; p = 0.06) and intensive care unit readmission (OR 1.87, 95%CI 1.00 - 3.49; p = 0.05), although with weak associations. Acute COVID-19 (OR 3.55, 95%CI 1.96 - 6.45; p < 0.001) also increased the odds of resistance. Carbapenem-resistant Enterobacterales infection was associated with twice the likelihood of 60-day mortality after adjusting for covariates (OR 1.95, 95%CI 1.26 - 3.02; p < 0.001). The predominant bacteria and carbapenemase resistance genes included Klebsiella pneumoniae (79%), Klebsiella pneumoniae carbapenemase (73%), New Delhi metallo-beta-lactamase (13%), and xacillinase-48 (9%).
Carbapenem-resistant Enterobacterales-related infections in intensive care unit patients were associated with major premorbid dependence, intensive care unit readmission, and acute COVID-19. In addition, carbapenem-resistant Enterobacterales infections were independently associated with poorer hospital outcomes. This study also characterized the resistance profile of Enterobacterales in Brazilian intensive care units, which are dominated by K. pneumoniae with high rates of carbapenemase and increased rates of New Delhi metallo-beta-lactamase, in comparison with previous reports.
Abstract
Critical Care Science. 2025;37:e20250237
04-02-2025
DOI 10.62675/2965-2774.20250237
, , , , , To evaluate risk factors, molecular profiles, and hospital mortality of carbapenem-resistant Enterobacterales (CRE) infections in intensive care unit patients.
In this retrospective, multicenter cohort study, intensive care unit admissions from 52 intensive care units between January 2019 and December 2020 were analyzed in a nested case-control design. Patients with carbapenem-resistant Enterobacterales infections were propensity score-matched 1:1 to those with carbapenem-susceptible Enterobacterales infections. Hierarchical conditional logistic regression identified risk factors for carbapenem-resistant Enterobacterales, and multivariable logistic regression assessed the association of carbapenem-resistant Enterobacterales with 60-day in-hospital mortality. Molecular genotyping was also conducted.
Matching resulted in 250 carbapenem-resistant Enterobacterales patients and 250 carbapenem-susceptible Enterobacterales patients. Sepsis was more common in the carbapenem-resistant Enterobacterales group (58% versus 35%; p < 0.001). Risk factors for carbapenem-resistant Enterobacterales included major premorbid assistance requirements (OR 1.72, 95%CI 0.99 - 3.01; p = 0.06) and intensive care unit readmission (OR 1.87, 95%CI 1.00 - 3.49; p = 0.05), although with weak associations. Acute COVID-19 (OR 3.55, 95%CI 1.96 - 6.45; p < 0.001) also increased the odds of resistance. Carbapenem-resistant Enterobacterales infection was associated with twice the likelihood of 60-day mortality after adjusting for covariates (OR 1.95, 95%CI 1.26 - 3.02; p < 0.001). The predominant bacteria and carbapenemase resistance genes included Klebsiella pneumoniae (79%), Klebsiella pneumoniae carbapenemase (73%), New Delhi metallo-beta-lactamase (13%), and xacillinase-48 (9%).
Carbapenem-resistant Enterobacterales-related infections in intensive care unit patients were associated with major premorbid dependence, intensive care unit readmission, and acute COVID-19. In addition, carbapenem-resistant Enterobacterales infections were independently associated with poorer hospital outcomes. This study also characterized the resistance profile of Enterobacterales in Brazilian intensive care units, which are dominated by K. pneumoniae with high rates of carbapenemase and increased rates of New Delhi metallo-beta-lactamase, in comparison with previous reports.
, Abstract
Revista Brasileira de Terapia Intensiva. 2020;32(2):224-228
06-24-2020
DOI 10.5935/0103-507X.20200030
, , , , To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states.
We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil’s expected case-fatality ratio was also adjusted by the population’s age pyramid. Therefore, the notification rate can be defined as the number of confirmed cases (notified by the Ministry of Health) divided by the number of expected cases (estimated from the number of deaths).
The reporting rate for COVID-19 in Brazil was estimated at 9.2% (95%CI 8.8% - 9.5%), with all the states presenting rates below 30%. São Paulo and Rio de Janeiro, the most populated states in Brazil, showed small reporting rates (8.9% and 7.2%, respectively). The highest reporting rate occurred in Roraima (31.7%) and the lowest in Paraiba (3.4%).
The results indicated that the reporting of confirmed cases in Brazil is much lower as compared to other countries we analyzed. Therefore, decision-makers, including the government, fail to know the actual dimension of the pandemic, which may interfere with the determination of control measures.
Abstract
Revista Brasileira de Terapia Intensiva. 2020;32(2):224-228
06-24-2020
DOI 10.5935/0103-507X.20200030
, , , , To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states.
We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil’s expected case-fatality ratio was also adjusted by the population’s age pyramid. Therefore, the notification rate can be defined as the number of confirmed cases (notified by the Ministry of Health) divided by the number of expected cases (estimated from the number of deaths).
The reporting rate for COVID-19 in Brazil was estimated at 9.2% (95%CI 8.8% - 9.5%), with all the states presenting rates below 30%. São Paulo and Rio de Janeiro, the most populated states in Brazil, showed small reporting rates (8.9% and 7.2%, respectively). The highest reporting rate occurred in Roraima (31.7%) and the lowest in Paraiba (3.4%).
The results indicated that the reporting of confirmed cases in Brazil is much lower as compared to other countries we analyzed. Therefore, decision-makers, including the government, fail to know the actual dimension of the pandemic, which may interfere with the determination of control measures.
, , Abstract
Revista Brasileira de Terapia Intensiva. 2020;32(2):213-223
05-22-2020
DOI 10.5935/0103-507X.20200028
, , , To analyse the measures adopted by countries that have shown control over the transmission of coronavirus disease 2019 (COVID-19) and how each curve of accumulated cases behaved after the implementation of those measures.
The methodology adopted for this study comprises three phases: systemizing control measures adopted by different countries, identifying structural breaks in the growth of the number of cases for those countries, and analyzing Brazilian data in particular.
We noted that China (excluding Hubei Province), Hubei Province, and South Korea have been effective in their deceleration of the growth rates of COVID-19 cases. The effectiveness of the measures taken by these countries could be seen after 1 to 2 weeks of their application. In Italy and Spain, control measures at the national level were taken at a late stage of the epidemic, which could have contributed to the high propagation of COVID-19. In Brazil, Rio de Janeiro and São Paulo adopted measures that could be effective in slowing the propagation of the virus. However, we only expect to see their effects on the growth of the curve in the coming days.
Our results may help decisionmakers in countries in relatively early stages of the epidemic, especially Brazil, understand the importance of control measures in decelerating the growth curve of confirmed cases.
Abstract
Revista Brasileira de Terapia Intensiva. 2020;32(2):213-223
05-22-2020
DOI 10.5935/0103-507X.20200028
, , , To analyse the measures adopted by countries that have shown control over the transmission of coronavirus disease 2019 (COVID-19) and how each curve of accumulated cases behaved after the implementation of those measures.
The methodology adopted for this study comprises three phases: systemizing control measures adopted by different countries, identifying structural breaks in the growth of the number of cases for those countries, and analyzing Brazilian data in particular.
We noted that China (excluding Hubei Province), Hubei Province, and South Korea have been effective in their deceleration of the growth rates of COVID-19 cases. The effectiveness of the measures taken by these countries could be seen after 1 to 2 weeks of their application. In Italy and Spain, control measures at the national level were taken at a late stage of the epidemic, which could have contributed to the high propagation of COVID-19. In Brazil, Rio de Janeiro and São Paulo adopted measures that could be effective in slowing the propagation of the virus. However, we only expect to see their effects on the growth of the curve in the coming days.
Our results may help decisionmakers in countries in relatively early stages of the epidemic, especially Brazil, understand the importance of control measures in decelerating the growth curve of confirmed cases.
