Purpose: To compare categorical and continuous combinations of the standardized mortality ratio (SMR) and the standardized resource use (SRU) to evaluate ICU performance.
Materials And Methods: We analysed data from adult patients admitted to 128 ICUs in Brazil and Uruguay (BR/UY) and 83 ICUs in The Netherlands between 2016 and 2018. SMR and SRU were calculated using SAPS-3 (BR/UY) or APACHE-IV (The Netherlands). Performance was defined as a combination of metrics. The categorical combination was the efficiency matrix, whereas the continuous combination was the average SMR and SRU (average standardized ratio, ASER). Association among metrics in each dataset was evaluated using Spearman's rho and R.
Results: We included 277,459 BR/UY and 164,399 Dutch admissions. Median [interquartile range] ASER = 0.99[0.83-1.21] in BR/UY and 0.99[0.92-1.09] in Dutch datasets. The SMR and SRU were more correlated in BR/UY ICUs than in Dutch ICUs (Spearman's Rho: 0.54vs.0.24). The highest and lowest ASER values were concentrated in the least and most efficient groups. An expert focus group listed potential advantages and limitations of both combinations.
Conclusions: The categorical combination of metrics is easy to interpret but limits statistical inference for benchmarking. The continuous combination offers appropriate statistical properties for evaluating performance when metrics are positively correlated.
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http://dx.doi.org/10.1016/j.jcrc.2022.154063 | DOI Listing |
Int J Med Inform
November 2024
D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Brazilian Research in Intensive Care Network (BRICNet), Brazil.
Purpose: Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency.
View Article and Find Full Text PDFJ Intensive Care Med
January 2024
Intensive Care Unit, Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz (Fiocruz), Rio de Janeiro, RJ, Brazil.
Background: There is little information comparing the performance of community acquired central nervous system infections (CNSI) treatment by intensive care units (ICUs) specialized in infectious diseases with treatment at other ICUs. Our objective was to reduce these gaps, creating bases for benchmarking and future case-mix classification.
Methods: This is a retrospective observational cohort of 785 admissions with 82 cases of CNSI admitted to the ICU of an important Brazilian referral center for infectious diseases (INI) between January 2012 and January 2019.
J Crit Care
August 2022
D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Brazilian Research in Intensive Care Network (BRICNet), Brazil.
Purpose: To compare categorical and continuous combinations of the standardized mortality ratio (SMR) and the standardized resource use (SRU) to evaluate ICU performance.
Materials And Methods: We analysed data from adult patients admitted to 128 ICUs in Brazil and Uruguay (BR/UY) and 83 ICUs in The Netherlands between 2016 and 2018. SMR and SRU were calculated using SAPS-3 (BR/UY) or APACHE-IV (The Netherlands).
PLoS One
January 2022
Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil.
Background: Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis.
View Article and Find Full Text PDFIntensive Care Med
December 2021
D'Or Research and Educational Institute, Rio de Janeiro, Brazil.
Purpose: To assess whether intensive care unit (ICU) outcomes for patients not affected by coronavirus disease 2019 (COVID-19) worsened during the COVID-19 pandemic.
Methods: Retrospective cohort study including prospectively collected information of patients admitted to 165 ICUs in a hospital network in Brazil between 2011 and 2020. Association between admission in 2020 and worse hospital outcomes was performed using different techniques, including assessment of changes in illness severity of admitted patients, a variable life-adjusted display of mortality during 2020, a multivariate mixed regression model with admission year as both fixed effect and random slope adjusted for SAPS 3 score, an analysis of trends in performance using standardized mortality ratio (SMR) and standardized resource use (SRU), and perturbation analysis.
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