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, , , , , Abstract
Crit Care Sci. 2024;36:e20240203en
DOI 10.62675/2965-2774.20240203-en
, , , , , , , , , , , , , To assess whether the respiratory oxygenation index (ROX index) measured after the start of high-flow nasal cannula oxygen therapy can help identify the need for intubation in patients with acute respiratory failure due to coronavirus disease 2019.
This retrospective, observational, multicenter study was conducted at the intensive care units of six Brazilian hospitals from March to December 2020. The primary outcome was the need for intubation up to 7 days after starting the high-flow nasal cannula.
A total of 444 patients were included in the study, and 261 (58.7%) were subjected to intubation. An analysis of the area under the receiver operating characteristic curve (AUROC) showed that the ability to discriminate between successful and failed high-flow nasal cannula oxygen therapy within 7 days was greater for the ROX index measured at 24 hours (AUROC 0.80; 95%CI 0.76 - 0.84). The median interval between high-flow nasal cannula initiation and intubation was 24 hours (24 - 72), and the most accurate predictor of intubation obtained before 24 hours was the ROX index measured at 12 hours (AUROC 0.75; 95%CI 0.70 - 0.79). Kaplan-Meier curves revealed a greater probability of intubation within 7 days in patients with a ROX index ≤ 5.54 at 12 hours (hazard ratio 3.07; 95%CI 2.24 - 4.20) and ≤ 5.96 at 24 hours (hazard ratio 5.15; 95%CI 3.65 - 7.27).
The ROX index can aid in the early identification of patients with acute respiratory failure due to COVID-19 who will progress to the failure of high-flow nasal cannula supportive therapy and the need for intubation.
Abstract
Crit Care Sci. 2024;36:e20240203en
DOI 10.62675/2965-2774.20240203-en
, , , , , , , , , , , , , To assess whether the respiratory oxygenation index (ROX index) measured after the start of high-flow nasal cannula oxygen therapy can help identify the need for intubation in patients with acute respiratory failure due to coronavirus disease 2019.
This retrospective, observational, multicenter study was conducted at the intensive care units of six Brazilian hospitals from March to December 2020. The primary outcome was the need for intubation up to 7 days after starting the high-flow nasal cannula.
A total of 444 patients were included in the study, and 261 (58.7%) were subjected to intubation. An analysis of the area under the receiver operating characteristic curve (AUROC) showed that the ability to discriminate between successful and failed high-flow nasal cannula oxygen therapy within 7 days was greater for the ROX index measured at 24 hours (AUROC 0.80; 95%CI 0.76 - 0.84). The median interval between high-flow nasal cannula initiation and intubation was 24 hours (24 - 72), and the most accurate predictor of intubation obtained before 24 hours was the ROX index measured at 12 hours (AUROC 0.75; 95%CI 0.70 - 0.79). Kaplan-Meier curves revealed a greater probability of intubation within 7 days in patients with a ROX index ≤ 5.54 at 12 hours (hazard ratio 3.07; 95%CI 2.24 - 4.20) and ≤ 5.96 at 24 hours (hazard ratio 5.15; 95%CI 3.65 - 7.27).
The ROX index can aid in the early identification of patients with acute respiratory failure due to COVID-19 who will progress to the failure of high-flow nasal cannula supportive therapy and the need for intubation.
Abstract
Rev Bras Ter Intensiva. 2011;23(2):176-182
DOI 10.1590/S0103-507X2011000200010
OBJECTIVE: The aim of this study was to characterize and quantify metabolic acidosis that was caused by initial volume expansion during the reanimation of patients with severe sepsis and septic shock. METHODS: A blood sample was drawn for physicochemical characterization of the patient's acid-base equilibrium both before and after volume expansion using 30 mL/kg 0.9% saline solution. The diagnosis and quantification of metabolic acidosis were based on the standard base excess (SBE). RESULTS: Eight patients with a mean age of 58 ± 13 years and mean APACHE II scores of 20 ± 4 were expanded using 2,000 ± 370 mL of 0.9% saline solution. Blood pH dropped from 7.404 ± 0.080 to 7.367 ± 0.086 (p=0.018), and PC O2 increased from 30 ± 5 to 32 ± 2 mmHg (p=0.215); SBE dropped from -4.4 ± 5.6 to -6.0 ± 5.7 mEq/L (p=0.039). The drop in SBE was associated with the acidifying power of two factors, namely, a significant increase in the strong ion gap (SIG) from 6.1 ± 3.4 to 7.7 ± 4.0 mEq/L (p = 0.134) and a non-significant drop in the apparent inorganic strong ion differences (SIDai) from 40 ± 5 to 38 ± 4 mEq/L (p = 0.318). Conversely, the serum albumin levels decreased from 3.1 ± 1.0 to 2.6 ± 0.8 mEq/L (p = 0.003) with an alkalinizing effect on SBE. Increased serum chloride levels from 103 ± 10 to 106 ± 7 mEq/L (p < 0.001) led to a drop in SIDai. CONCLUSION: Initial resuscitation using 30 mL/kg of 0.9% saline solution for patients with severe sepsis and septic shock is associated with worsened metabolic acidosis, as measured by SBE. This worsened SBE can be ascribed to a serum increase in the levels of unmeasurable anions and chloride.
Abstract
Rev Bras Ter Intensiva. 2011;23(2):176-182
DOI 10.1590/S0103-507X2011000200010
OBJECTIVE: The aim of this study was to characterize and quantify metabolic acidosis that was caused by initial volume expansion during the reanimation of patients with severe sepsis and septic shock. METHODS: A blood sample was drawn for physicochemical characterization of the patient's acid-base equilibrium both before and after volume expansion using 30 mL/kg 0.9% saline solution. The diagnosis and quantification of metabolic acidosis were based on the standard base excess (SBE). RESULTS: Eight patients with a mean age of 58 ± 13 years and mean APACHE II scores of 20 ± 4 were expanded using 2,000 ± 370 mL of 0.9% saline solution. Blood pH dropped from 7.404 ± 0.080 to 7.367 ± 0.086 (p=0.018), and PC O2 increased from 30 ± 5 to 32 ± 2 mmHg (p=0.215); SBE dropped from -4.4 ± 5.6 to -6.0 ± 5.7 mEq/L (p=0.039). The drop in SBE was associated with the acidifying power of two factors, namely, a significant increase in the strong ion gap (SIG) from 6.1 ± 3.4 to 7.7 ± 4.0 mEq/L (p = 0.134) and a non-significant drop in the apparent inorganic strong ion differences (SIDai) from 40 ± 5 to 38 ± 4 mEq/L (p = 0.318). Conversely, the serum albumin levels decreased from 3.1 ± 1.0 to 2.6 ± 0.8 mEq/L (p = 0.003) with an alkalinizing effect on SBE. Increased serum chloride levels from 103 ± 10 to 106 ± 7 mEq/L (p < 0.001) led to a drop in SIDai. CONCLUSION: Initial resuscitation using 30 mL/kg of 0.9% saline solution for patients with severe sepsis and septic shock is associated with worsened metabolic acidosis, as measured by SBE. This worsened SBE can be ascribed to a serum increase in the levels of unmeasurable anions and chloride.
Abstract
Rev Bras Ter Intensiva. 2019;31(2):193-201
DOI 10.5935/0103-507X.20190033
To characterize resource availability from a nationally representative random sample of intensive care units in Brazil.
A structured online survey of participating units in the Sepsis PREvalence Assessment Database (SPREAD) study, a nationwide 1-day point prevalence survey to assess the burden of sepsis in Brazil, was sent to the medical director of each unit.
A representative sample of 277 of the 317 invited units responded to the resources survey. Most of the hospitals had fewer than 500 beds (94.6%) with a median of 14 beds in the intensive care unit. Providing care for public-insured patients was the main source of income in two-thirds of the surveyed units. Own microbiology laboratory was not available for 26.8% of the surveyed intensive care units, and 10.5% did not always have access to blood cultures. Broad spectrum antibiotics were not always available in 10.5% of surveyed units, and 21.3% could not always measure lactate within three hours. Those institutions with a high resource availability (158 units, 57%) were usually larger and preferentially served patients from the private health system compared to institutions without high resource availability. Otherwise, those without high resource availability did not always have broad-spectrum antibiotics (24.4%), vasopressors (4.2%) or crystalloids (7.6%).
Our study indicates that a relevant number of units cannot perform basic monitoring and therapeutic interventions in septic patients. Our results highlight major opportunities for improvement to adhere to simple but effective interventions in Brazil.
Abstract
Rev Bras Ter Intensiva. 2019;31(2):193-201
DOI 10.5935/0103-507X.20190033
To characterize resource availability from a nationally representative random sample of intensive care units in Brazil.
A structured online survey of participating units in the Sepsis PREvalence Assessment Database (SPREAD) study, a nationwide 1-day point prevalence survey to assess the burden of sepsis in Brazil, was sent to the medical director of each unit.
A representative sample of 277 of the 317 invited units responded to the resources survey. Most of the hospitals had fewer than 500 beds (94.6%) with a median of 14 beds in the intensive care unit. Providing care for public-insured patients was the main source of income in two-thirds of the surveyed units. Own microbiology laboratory was not available for 26.8% of the surveyed intensive care units, and 10.5% did not always have access to blood cultures. Broad spectrum antibiotics were not always available in 10.5% of surveyed units, and 21.3% could not always measure lactate within three hours. Those institutions with a high resource availability (158 units, 57%) were usually larger and preferentially served patients from the private health system compared to institutions without high resource availability. Otherwise, those without high resource availability did not always have broad-spectrum antibiotics (24.4%), vasopressors (4.2%) or crystalloids (7.6%).
Our study indicates that a relevant number of units cannot perform basic monitoring and therapeutic interventions in septic patients. Our results highlight major opportunities for improvement to adhere to simple but effective interventions in Brazil.
, , , , Abstract
Rev Bras Ter Intensiva. 2022;34(1):202-204
DOI 10.5935/0103-507X.20220015-en
, , , , Abstract
Rev Bras Ter Intensiva. 2022;34(1):202-204
DOI 10.5935/0103-507X.20220015-en
, , , , 
, , , , Abstract
Rev Bras Ter Intensiva. 2022;34(2):220-226
DOI 10.5935/0103-507X.20220019-en
, , , , To compare the predictive performance of residents, senior intensive care unit physicians and surrogates early during intensive care unit stays and to evaluate whether different presentations of prognostic data (probability of survival versus probability of death) influenced their performance.
We questioned surrogates and physicians in charge of critically ill patients during the first 48 hours of intensive care unit admission on the patient’s probability of hospital outcome. The question framing (i.e., probability of survival versus probability of death during hospitalization) was randomized. To evaluate the predictive performance, we compared the areas under the ROC curves (AUCs) for hospital outcome between surrogates and physicians’ categories. We also stratified the results according to randomized question framing.
We interviewed surrogates and physicians on the hospital outcomes of 118 patients. The predictive performance of surrogate decisionmakers was significantly lower than that of physicians (AUC of 0.63 for surrogates, 0.82 for residents, 0.80 for intensive care unit fellows and 0.81 for intensive care unit senior physicians). There was no increase in predictive performance related to physicians’ experience (i.e., senior physicians did not predict outcomes better than junior physicians). Surrogate decisionmakers worsened their prediction performance when they were asked about probability of death instead of probability of survival, but there was no difference for physicians.
Different predictive performance was observed when comparing surrogate decision-makers and physicians, with no effect of experience on health care professionals’ prediction. Question framing affected the predictive performance of surrogates but not of physicians.
Abstract
Rev Bras Ter Intensiva. 2022;34(2):220-226
DOI 10.5935/0103-507X.20220019-en
, , , , To compare the predictive performance of residents, senior intensive care unit physicians and surrogates early during intensive care unit stays and to evaluate whether different presentations of prognostic data (probability of survival versus probability of death) influenced their performance.
We questioned surrogates and physicians in charge of critically ill patients during the first 48 hours of intensive care unit admission on the patient’s probability of hospital outcome. The question framing (i.e., probability of survival versus probability of death during hospitalization) was randomized. To evaluate the predictive performance, we compared the areas under the ROC curves (AUCs) for hospital outcome between surrogates and physicians’ categories. We also stratified the results according to randomized question framing.
We interviewed surrogates and physicians on the hospital outcomes of 118 patients. The predictive performance of surrogate decisionmakers was significantly lower than that of physicians (AUC of 0.63 for surrogates, 0.82 for residents, 0.80 for intensive care unit fellows and 0.81 for intensive care unit senior physicians). There was no increase in predictive performance related to physicians’ experience (i.e., senior physicians did not predict outcomes better than junior physicians). Surrogate decisionmakers worsened their prediction performance when they were asked about probability of death instead of probability of survival, but there was no difference for physicians.
Different predictive performance was observed when comparing surrogate decision-makers and physicians, with no effect of experience on health care professionals’ prediction. Question framing affected the predictive performance of surrogates but not of physicians.
Abstract
Rev Bras Ter Intensiva. 2020;32(2):268-276
DOI 10.5935/0103-507X.20200043
To conduct a national survey of intensive care professionals to identify the practices for promoting sleep in adult intensive care units in Brazil and describe the professionals’ perceptions of the importance of sleep for patients.
An electronic questionnaire was distributed by the clinical research cooperation network of the Associação de Medicina Intensiva Brasileira and by the Brazilian Research in Intensive Care Network to physicians and nurses registered with the association. The questionnaire evaluated the profile of the respondents, the profile of their intensive care units, whether protocols for promoting sleep were present, the pharmacological and nonpharmacological measures typically employed in the unit, and the professionals’ perceptions regarding sleep in critically ill patients.
A total of 118 questionnaires were evaluated. The Southeast region of the country was the most represented (50 questionnaires, 42.4%). The majority of units had a clinical-surgical profile (93 questionnaires; 78.8%), and 26 had a continuous visitation policy (22.0%). Only 18 intensive care units (15.3%) reported having protocols for promoting sleep. The most cited measure for sleep promotion was reducing light during the night (95 questionnaires; 80.5%), which was more often performed in private intensive care units. Almost all of the responders (99%) believed that poor-quality sleep has a negative impact on patient recovery.
The responses to this Brazilian survey revealed that few intensive care units had a program for promoting sleep, although almost all participants recognized the importance of sleep in patient recovery.
Abstract
Rev Bras Ter Intensiva. 2020;32(2):268-276
DOI 10.5935/0103-507X.20200043
To conduct a national survey of intensive care professionals to identify the practices for promoting sleep in adult intensive care units in Brazil and describe the professionals’ perceptions of the importance of sleep for patients.
An electronic questionnaire was distributed by the clinical research cooperation network of the Associação de Medicina Intensiva Brasileira and by the Brazilian Research in Intensive Care Network to physicians and nurses registered with the association. The questionnaire evaluated the profile of the respondents, the profile of their intensive care units, whether protocols for promoting sleep were present, the pharmacological and nonpharmacological measures typically employed in the unit, and the professionals’ perceptions regarding sleep in critically ill patients.
A total of 118 questionnaires were evaluated. The Southeast region of the country was the most represented (50 questionnaires, 42.4%). The majority of units had a clinical-surgical profile (93 questionnaires; 78.8%), and 26 had a continuous visitation policy (22.0%). Only 18 intensive care units (15.3%) reported having protocols for promoting sleep. The most cited measure for sleep promotion was reducing light during the night (95 questionnaires; 80.5%), which was more often performed in private intensive care units. Almost all of the responders (99%) believed that poor-quality sleep has a negative impact on patient recovery.
The responses to this Brazilian survey revealed that few intensive care units had a program for promoting sleep, although almost all participants recognized the importance of sleep in patient recovery.
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.
