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You searched for:"Jose Mauro Vieira Jr."
We found (3) results for your search.Abstract
Rev Bras Ter Intensiva. 2017;29(3):317-324
DOI 10.5935/0103-507X.20170047
This study intended to determine whether the systemic inflammatory response syndrome criteria can predict hospital mortality in a Brazilian cohort of critically ill patients.
We performed a retrospective cohort study at a private tertiary hospital in São Paulo (SP), Brazil. We extracted information from the adult intensive care unit database (Sistema EpimedTM). We compared the SAPS 3 and the systemic inflammatory response syndrome model as dichotomous (≥ 2 criteria: systemic inflammatory response syndrome -positive versus 0 - 1 criterion: systemic inflammatory response syndrome -negative) and ordinal variables from 0 to 4 (according to the number of systemic inflammatory response syndrome criteria met) in the prediction of hospital mortality at intensive care unit admission. Model discrimination was compared using the area under the receiver operating characteristics (AUROC) curve.
From January to December 2012, we studied 932 patients (60.4% were systemic inflammatory response syndrome -positive). systemic inflammatory response syndrome -positive patients were more critically ill than systemic inflammatory response syndrome -negative patients and had higher hospital mortality (16.9% versus 8.1%, p < 0.001). In the adjusted analysis, being systemic inflammatory response syndrome -positive independently increased the risk of death by 82% (odds ratio 1.82; 95% confidence interval [CI] 1.12 - 2.96, p = 0.016). However, the AUROC curve for the SAPS 3 model was higher (0.81, 95%CI 0.78 - 0.85) compared to the systemic inflammatory response syndrome model with the systemic inflammatory response syndrome criteria as a dichotomous variable (0.60, 95%CI 0.55 - 0.65) and as an ordinal variable (0.62, 95%CI 0.57 - 0.68; p < 0.001) for hospital mortality.
Although systemic inflammatory response syndrome is associated with hospital mortality, the systemic inflammatory response syndrome criteria show low accuracy in the prediction of mortality compared with the SAPS 3.
Abstract
Rev Bras Ter Intensiva. 2017;29(3):317-324
DOI 10.5935/0103-507X.20170047
This study intended to determine whether the systemic inflammatory response syndrome criteria can predict hospital mortality in a Brazilian cohort of critically ill patients.
We performed a retrospective cohort study at a private tertiary hospital in São Paulo (SP), Brazil. We extracted information from the adult intensive care unit database (Sistema EpimedTM). We compared the SAPS 3 and the systemic inflammatory response syndrome model as dichotomous (≥ 2 criteria: systemic inflammatory response syndrome -positive versus 0 - 1 criterion: systemic inflammatory response syndrome -negative) and ordinal variables from 0 to 4 (according to the number of systemic inflammatory response syndrome criteria met) in the prediction of hospital mortality at intensive care unit admission. Model discrimination was compared using the area under the receiver operating characteristics (AUROC) curve.
From January to December 2012, we studied 932 patients (60.4% were systemic inflammatory response syndrome -positive). systemic inflammatory response syndrome -positive patients were more critically ill than systemic inflammatory response syndrome -negative patients and had higher hospital mortality (16.9% versus 8.1%, p < 0.001). In the adjusted analysis, being systemic inflammatory response syndrome -positive independently increased the risk of death by 82% (odds ratio 1.82; 95% confidence interval [CI] 1.12 - 2.96, p = 0.016). However, the AUROC curve for the SAPS 3 model was higher (0.81, 95%CI 0.78 - 0.85) compared to the systemic inflammatory response syndrome model with the systemic inflammatory response syndrome criteria as a dichotomous variable (0.60, 95%CI 0.55 - 0.65) and as an ordinal variable (0.62, 95%CI 0.57 - 0.68; p < 0.001) for hospital mortality.
Although systemic inflammatory response syndrome is associated with hospital mortality, the systemic inflammatory response syndrome criteria show low accuracy in the prediction of mortality compared with the SAPS 3.
Abstract
Rev Bras Ter Intensiva. 2016;28(1):33-39
DOI 10.5935/0103-507X.20160011
The purpose of our study was to determine the admission factors associated with intensive care unit readmission among oncohematological patients.
Retrospective cohort study using an intensive care unit database from a tertiary oncological center. The participants included 1,872 critically ill oncohematological patients who were admitted to the intensive care unit from January 2012 to December 2014 and who were subsequently discharged alive. We used univariate and multivariate analysis to identify the admission risk factors associated with later intensive care unit readmission.
One hundred seventy-two patients (9.2% of 1,872 oncohematological patients discharged alive from the intensive care unit) were readmitted after intensive care unit discharge. The readmitted patients were sicker compared with the non-readmitted group and had higher hospital mortality (32.6% versus 3.7%, respectively; p < 0.001). In the multivariate analysis, the independent risk factors for intensive care unit readmission were male sex (OR: 1.5, 95% CI: 1.07 - 2.12; p = 0.019), emergency surgery as the admission reason (OR: 2.91, 95%CI: 1.53 - 5.54; p = 0.001), longer hospital length of stay before intensive care unit transfer (OR: 1.02, 95%CI: 1.007 - 1.035; p = 0.003), and mechanical ventilation (OR: 2.31, 95%CI: 1.57 - 3.40; p < 0.001).
In this cohort of oncohematological patients, we identified some risk factors associated with intensive care unit readmission, most of which are not amenable to interventions. The identification of risk factors at intensive care unit discharge might be a promising approach.
Abstract
Rev Bras Ter Intensiva. 2016;28(1):33-39
DOI 10.5935/0103-507X.20160011
The purpose of our study was to determine the admission factors associated with intensive care unit readmission among oncohematological patients.
Retrospective cohort study using an intensive care unit database from a tertiary oncological center. The participants included 1,872 critically ill oncohematological patients who were admitted to the intensive care unit from January 2012 to December 2014 and who were subsequently discharged alive. We used univariate and multivariate analysis to identify the admission risk factors associated with later intensive care unit readmission.
One hundred seventy-two patients (9.2% of 1,872 oncohematological patients discharged alive from the intensive care unit) were readmitted after intensive care unit discharge. The readmitted patients were sicker compared with the non-readmitted group and had higher hospital mortality (32.6% versus 3.7%, respectively; p < 0.001). In the multivariate analysis, the independent risk factors for intensive care unit readmission were male sex (OR: 1.5, 95% CI: 1.07 - 2.12; p = 0.019), emergency surgery as the admission reason (OR: 2.91, 95%CI: 1.53 - 5.54; p = 0.001), longer hospital length of stay before intensive care unit transfer (OR: 1.02, 95%CI: 1.007 - 1.035; p = 0.003), and mechanical ventilation (OR: 2.31, 95%CI: 1.57 - 3.40; p < 0.001).
In this cohort of oncohematological patients, we identified some risk factors associated with intensive care unit readmission, most of which are not amenable to interventions. The identification of risk factors at intensive care unit discharge might be a promising approach.
Abstract
Rev Bras Ter Intensiva. 2016;28(3):354-355
DOI 10.5935/0103-507X.20160062
Abstract
Rev Bras Ter Intensiva. 2016;28(3):354-355
DOI 10.5935/0103-507X.20160062