The Association Between Prepandemic ICU Performance and Mortality Variation in COVID-19: A Multicenter Cohort Study of 35,619 Critically Ill Patients

BACKGROUND

During the COVID-19 pandemic, ICUs remained under stress and observed elevated mortality rates and high variations of outcomes. A knowledge gap exists regarding whether an ICU performing best during nonpandemic times would still perform better when under high pressure compared with the least performing ICUs.

RESEARCH QUESTION

Does prepandemic ICU performance explain the risk-adjusted mortality variability for critically ill patients with COVID-19?

STUDY DESIGN AND METHODS

This study examined a cohort of adults with real-time polymerase chain reaction-confirmed COVID-19 admitted to 156 ICUs in 35 hospitals from February 16, 2020, through December 31, 2021, in Brazil. We evaluated crude and adjusted in-hospital mortality variability of patients with COVID-19 in the ICU during the pandemic. Association of baseline (prepandemic) ICU performance and in-hospital mortality was examined using a variable life-adjusted display (VLAD) during the pandemic and a multivariable mixed regression model adjusted by clinical characteristics, interaction of performance with the year of admission, and mechanical ventilation at admission.

RESULTS

Thirty-five thousand six hundred nineteen patients with confirmed COVID-19 were evaluated. The median age was 52 years, median Simplified Acute Physiology Score 3 was 42, and 18% underwent invasive mechanical ventilation. In-hospital mortality was 13% and 54% for those receiving invasive mechanical ventilation. Adjusted in-hospital mortality ranged from 3.6% to 63.2%. VLAD in the most efficient ICUs was higher than the overall median in 18% of weeks, whereas VLAD was 62% and 84% in the underachieving and least efficient groups, respectively. The least efficient baseline ICU performance group was associated independently with increased mortality (OR, 2.30; 95% CI, 1.45-3.62) after adjusting for patient characteristics, disease severity, and pandemic surge.

INTERPRETATION

ICUs caring for patients with COVID-19 presented substantial variation in risk-adjusted mortality. ICUs with better baseline (prepandemic) performance showed reduced mortality and less variability. Our findings suggest that achieving ICU efficiency by targeting improvement in organizational aspects of ICUs may impact outcomes, and therefore should be a part of the preparedness for future pandemics.

Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19

PURPOSE

To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients.

MATERIALS AND METHODS

Retrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed.

RESULTS

Eight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08-0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75-0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]).

CONCLUSIONS

An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.

Effects of Tranexamic Acid in Patients with Subarachnoid Hemorrhage in Brazil: A Prospective Observational Study with Propensity Score Analysis

BACKGROUND

Rebleeding from a ruptured aneurysm increases the risk of unfavorable outcomes after subarachnoid hemorrhage (SAH) and is prevented by early aneurysm occlusion. The role of antifibrinolytics before aneurysm obliteration remains controversial. We investigated the effects of tranexamic acid on long-term functional outcomes of patients with aneurysmal SAH (aSAH).

METHODS

This was a single-center, prospective, observational study conducted in a high-volume tertiary hospital in a middle-income country from December 2016 to February 2020. We included all consecutive patients with aSAH who either received or did not receive tranexamic acid (TXA) treatment. Multivariate logistic regression analysis using propensity score was used to evaluate the association of TXA use with long-term functional outcomes, measured by the modified Rankin Scale (mRS) at 6 months.

RESULTS

A total of 230 patients with aSAH were analyzed. The median (interquartile range) age was 55 (46-63) years, 72% were women, 75% presented with good clinical grade (World Federation of Neurological Surgeons grade 1-3), and 83% had a Fisher scale of 3 or 4. Around 80% of patients were admitted up to 72 h from ictus. The aneurysm occlusion method was surgical clipping in 80% of the patients. A total of 129 patients (56%) received TXA. In multivariable logistic regression using inverse probability treatment weighting, the long-term rate of unfavorable outcomes (modified Rankin scale 4-6) was the same in the TXA and non-TXA groups (61 [48%] in TXA group vs. 33 [33%] in non-TXA group; odds ratio [OR] 1.39, 95% confidence interval [CI] 0.67-2.92; p = 0.377). The TXA group had higher in-hospital mortality (33 vs. 11% in non-TXA group; OR 4.13, 95% CI 1.55-12.53, p = 0.007). There were no differences between the groups concerning intensive care unit length of stay (16 ± 11.22 days in TXA group vs. 14 ± 9.24 days in non-TXA group; p = 0.2) or hospital (23 ± 13.35 days in TXA group vs. 22 ± 13.36 days in non-TXA group; p = 0.9). There was no difference in the rates of rebleeding (7.8% in TXA group vs. 8.9% in non-TXA group; p = 0.31) or delayed cerebral ischemia (27% in TXA group vs. 19% in non-TXA group; p = 0.14). For the propensity-matched analysis, 128 individuals were selected (64 in TXA group and 64 in non-TXA group), and the rates of unfavorable outcomes at 6 months were also similar between groups (45% in TXA group and 36% in non-TXA group; OR 1.22, 95% CI 0.51-2.89; p = 0.655).

CONCLUSIONS

Our findings in a cohort with delayed aneurysm treatment reinforce previous data that TXA use before aneurysm occlusion does not improve functional outcomes in aSAH.

Trends in Intensive Care Admissions and Outcomes of Stroke Patients Over 10 Years in Brazil: Impact of the COVID-19 Pandemic

BACKGROUND

The coronavirus 2019 (COVID-19) pandemic affected stroke care worldwide. Data from low- and middle-income countries are limited.

RESEARCH QUESTION

What was the impact of the pandemic in ICU admissions and outcomes of patients with stroke, in comparison with trends over the last 10 years?

STUDY DESIGN AND METHODS

Retrospective cohort study including prospectively collected data from 165 ICUs in Brazil between 2011 and 2020. We analyzed clinical characteristics and mortality over a period of 10 years and evaluated the impact of the pandemic on stroke outcomes, using the following approach: analyses of admissions for ischemic and hemorrhagic strokes and trends in in-hospital mortality over 10 years; analysis of variable life-adjusted display (VLAD) during 2020; and a mixed-effects multivariable logistic regression model.

RESULTS

A total of 17,115 stroke admissions were analyzed, from which 13,634 were ischemic and 3,481 were hemorrhagic. In-hospital mortality was lower after ischemic stroke as compared with hemorrhagic (9% vs 24%, respectively). Changes in VLAD across epidemiological weeks of 2020 showed that the rise in COVID-19 cases was accompanied by increased mortality, mainly after ischemic stroke. In logistic regression mixed models, mortality was higher in 2020 compared with 2019, 2018, and 2017 in patients with ischemic stroke, namely, in those without altered mental status. In hemorrhagic stroke, the increased mortality in 2020 was observed in patients 50 years of age or younger, as compared with 2019.

INTERPRETATION

Hospital outcomes of stroke admissions worsened during the COVID-19 pandemic, interrupting a trend of improvements in survival rates over 10 years. This effect was more pronounced during the surge of COVID-19 ICU admissions affecting predominantly patients with ischemic stroke without coma, and young patients with hemorrhagic stroke.

The resilient intensive care unit

Background

The COVID-19 pandemic tested the capacity of intensive care units (ICU) to respond to a crisis and demonstrated their fragility. Unsurprisingly, higher than usual mortality rates, lengths of stay (LOS), and ICU-acquired complications occurred during the pandemic. However, worse outcomes were not universal nor constant across ICUs and significant variation in outcomes was reported, demonstrating that some ICUs could adequately manage the surge of COVID-19.

Methods

In the present editorial, we discuss the concept of a resilient Intensive Care Unit, including which metrics can be used to address the capacity to respond, sustain results and incorporate new practices that lead to improvement.

Results

We believe that a resiliency analysis adds a component of preparedness to the usual ICU performance evaluation and outcomes metrics to be used during the crisis and in regular times.

Conclusions

The COVID-19 pandemic demonstrated the need for a resilient health system. Although this concept has been discussed for health systems, it was not tested in intensive care. Future studies should evaluate this concept to improve ICU organization for standard and pandemic times.

Using data envelopment analysis to perform benchmarking in intensive care units

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.

METHODS

We performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit.

RESULTS

We performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79-1.21] and SRU was 1.15 [IQR: 0.95-1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18-1.88] vs. 1.7 [IQR: 1.36-2.00]) and nursing workload (168 hours [IQR: 168-291] vs 396 hours [IQR: 336-672]) but higher nurses per bed ratio (2.02 [1.16-2.48] vs. 1.71 [1.43-2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the "most efficient" quadrant.

CONCLUSION

Data envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency.

Evolving changes in mortality of 13,301 critically ill adult patients with COVID-19 over 8 months

PURPOSE

Clinical characteristics and management of COVID-19 patients have evolved during the pandemic, potentially changing their outcomes. We analyzed the associations of changes in mortality rates with clinical profiles and respiratory support strategies in COVID-19 critically ill patients.

METHODS

A multicenter cohort of RT-PCR-confirmed COVID-19 patients admitted at 126 Brazilian intensive care units between February 27^th and October 28^th, 2020. Assessing temporal changes in deaths, we identified distinct time periods. We evaluated the association of characteristics and respiratory support strategies with 60-day in-hospital mortality using random-effects multivariable Cox regression with inverse probability weighting.

RESULTS

Among the 13,301 confirmed-COVID-19 patients, 60-day in-hospital mortality was 13%. Across four time periods identified, younger patients were progressively more common, non-invasive respiratory support was increasingly used, and the 60-day in-hospital mortality decreased in the last two periods. 4188 patients received advanced respiratory support (non-invasive or invasive), from which 42% underwent only invasive mechanical ventilation, 37% only non-invasive respiratory support and 21% failed non-invasive support and were intubated. After adjusting for organ dysfunction scores and premorbid conditions, we found that younger age, absence of frailty and the use of non-invasive respiratory support (NIRS) as first support strategy were independently associated with improved survival (hazard ratio for NIRS first [95% confidence interval], 0.59 [0.54-0.65], p < 0.001).

CONCLUSION

Age and mortality rates have declined over the first 8 months of the pandemic. The use of NIRS as the first respiratory support measure was associated with survival, but causal inference is limited by the observational nature of our data.

App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning

BACKGROUND

Tests are scarce resources, especially in low and middle-income countries, and the optimization of testing programs during a pandemic is critical for the effectiveness of the disease control. Hence, we aim to use the combination of symptoms to build a predictive model as a screening tool to identify people and areas with a higher risk of SARS-CoV-2 infection to be prioritized for testing.

MATERIALS AND METHODS

We performed a retrospective analysis of individuals registered in "Dados do Bem," a Brazilian app-based symptom tracker. We applied machine learning techniques and provided a SARS-CoV-2 infection risk map of Rio de Janeiro city.

RESULTS

From April 28 to July 16, 2020, 337,435 individuals registered their symptoms through the app. Of these, 49,721 participants were tested for SARS-CoV-2 infection, being 5,888 (11.8%) positive. Among self-reported symptoms, loss of smell (OR[95%CI]: 4.6 [4.4-4.9]), fever (2.6 [2.5-2.8]), and shortness of breath (2.1 [1.6-2.7]) were independently associated with SARS-CoV-2 infection. Our final model obtained a competitive performance, with only 7% of false-negative users predicted as negatives (NPV = 0.93). The model was incorporated by the "Dados do Bem" app aiming to prioritize users for testing. We developed an external validation in the city of Rio de Janeiro. We found that the proportion of positive results increased significantly from 14.9% (before using our model) to 18.1% (after the model).

CONCLUSIONS

Our results showed that the combination of symptoms might predict SARS-Cov-2 infection and, therefore, can be used as a tool by decision-makers to refine testing and disease control strategies.

Sociodemographic factors associated with COVID-19 in-hospital mortality in Brazil

OBJECTIVES

The coronavirus disease 2019 (COVID-19) pandemic has highlighted inequalities in access to healthcare systems, increasing racial disparities and worsening health outcomes in these populations. This study analysed the association between sociodemographic characteristics and COVID-19 in-hospital mortality in Brazil.

STUDY DESIGN

A retrospective analysis was conducted on quantitative reverse transcription polymerase chain reaction-confirmed hospitalised adult patients with COVID-19 with a defined outcome (i.e. hospital discharge or death) in Brazil. Data were retrieved from the national surveillance system database (SIVEP-Gripe) between February 16 and August 8, 2020.

METHODS

Clinical characteristics, sociodemographic variables, use of hospital resources and outcomes of hospitalised adult patients with COVID-19, stratified by self-reported race, were investigated. The primary outcome was in-hospital mortality. The association between self-reported race and in-hospital mortality, after adjusting for clinical characteristics and comorbidities, was evaluated using a logistic regression model.

RESULTS

During the study period, Brazil had 3,018,397 confirmed COVID-19 cases and 100,648 deaths. The study population included 228,196 COVID-19-positive adult in-hospital patients with a defined outcome; the median age was 61 years, 57% were men, 35% (79,914) self-reported as Black/Brown and 35.4% (80,853) self-reported as White. The total in-hospital mortality was 37% (85,171/228,196). Black/Brown patients showed higher in-hospital mortality than White patients (42% vs 37%, respectively), were admitted less frequently to the intensive care unit (ICU) (32% vs 36%, respectively) and used more invasive mechanical ventilation (21% vs 19%, respectively), especially outside the ICU (17% vs 11%, respectively). Black/Brown race was independently associated with high in-hospital mortality after adjusting for sex, age, level of education, region of residence and comorbidities (odds ratio = 1.15; 95% confidence interval = 1.09-1.22).

CONCLUSIONS

Among hospitalised Brazilian adults with COVID-19, Black/Brown patients showed higher in-hospital mortality, less frequently used hospital resources and had potentially more severe conditions than White patients. Racial disparities in health outcomes and access to health care highlight the need to actively implement strategies to reduce inequities caused by the wider health determinants, ultimately leading to a sustainable change in the health system.

Effect of Convalescent Plasma in Critically Ill Patients With COVID-19: An Observational Study

Background: Convalescent plasma is a potential therapeutic option for critically ill patients with coronavirus disease 19 (COVID-19), yet its efficacy remains to be determined. The aim was to investigate the effects of convalescent plasma (CP) in critically ill patients with COVID-19.

Methods: This was a single-center prospective observational study conducted in Rio de Janeiro, Brazil, from March 17th to May 30th, with final follow-up on June 30th. We included 113 laboratory-confirmed COVID-19 patients with respiratory failure. Primary outcomes were time to clinical improvement and survival within 28 days. Secondary outcomes included behavior of biomarkers and viral loads. Kaplan–Meier analyses and Cox proportional-hazards regression using propensity score with inverse-probability weighing were performed.

Results: 41 patients received CP and 72 received standard of care (SOC). Median age was 61 years (IQR 48–68), disease duration was 10 days (IQR 6–13), and 86% were mechanically ventilated. At least 29 out of 41CP-recipients had baseline IgG titers ≥ 1:1,080. Clinical improvement within 28 days occurred in 19 (46%) CP-treated patients, as compared to 23 (32%) in the SOC group [adjusted hazard ratio (aHR) 0.91 (0.49–1.69)]. There was no significant change in 28-day mortality (CP 49% vs. SOC 56%; aHR 0.90 [0.52–1.57]). Biomarker assessment revealed reduced inflammatory activity and increased lymphocyte count after CP.

Conclusions: In this study, CP was not associated with clinical improvement or increase in 28-day survival. However, our study may have been underpowered and included patients with high IgG titers and life-threatening disease.

Clinical Trial Registration: The study protocol was retrospectively registered at the Brazilian Registry of Clinical Trials (ReBEC) with the identification RBR-4vm3yy (http://www.ensaiosclinicos.gov.br).

Characterisation of the first 250,000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data

Background

Most low-income and middle-income countries (LMICs) have little or no data integrated into a national surveillance system to identify characteristics or outcomes of COVID-19 hospital admissions and the impact of the COVID-19 pandemic on their national health systems. We aimed to analyse characteristics of patients admitted to hospital with COVID-19 in Brazil, and to examine the impact of COVID-19 on health-care resources and in-hospital mortality.

Methods

We did a retrospective analysis of all patients aged 20 years or older with quantitative RT-PCR (RT-qPCR)-confirmed COVID-19 who were admitted to hospital and registered in SIVEP-Gripe, a nationwide surveillance database in Brazil, between Feb 16 and Aug 15, 2020 (epidemiological weeks 8–33). We also examined the progression of the COVID-19 pandemic across three 4-week periods within this timeframe (epidemiological weeks 8–12, 19–22, and 27–30). The primary outcome was in-hospital mortality. We compared the regional burden of hospital admissions stratified by age, intensive care unit (ICU) admission, and respiratory support. We analysed data from the whole country and its five regions: North, Northeast, Central-West, Southeast, and South.

Findings

Between Feb 16 and Aug 15, 2020, 254 288 patients with RT-qPCR-confirmed COVID-19 were admitted to hospital and registered in SIVEP-Gripe. The mean age of patients was 60 (SD 17) years, 119 657 (47%) of 254 288 were aged younger than 60 years, 143 521 (56%) of 254 243 were male, and 14 979 (16%) of 90 829 had no comorbidities. Case numbers increased across the three 4-week periods studied: by epidemiological weeks 19–22, cases were concentrated in the North, Northeast, and Southeast; by weeks 27–30, cases had spread to the Central-West and South regions. 232 036 (91%) of 254 288 patients had a defined hospital outcome when the data were exported; in-hospital mortality was 38% (87 515 of 232 036 patients) overall, 59% (47 002 of 79 687) among patients admitted to the ICU, and 80% (36 046 of 45 205) among those who were mechanically ventilated. The overall burden of ICU admissions per ICU beds was more pronounced in the North, Southeast, and Northeast, than in the Central-West and South. In the Northeast, 1545 (16%) of 9960 patients received invasive mechanical ventilation outside the ICU compared with 431 (8%) of 5388 in the South. In-hospital mortality among patients younger than 60 years was 31% (4204 of 13 468) in the Northeast versus 15% (1694 of 11 196) in the South.

Interpretation

We observed a widespread distribution of COVID-19 across all regions in Brazil, resulting in a high overall disease burden. In-hospital mortality was high, even in patients younger than 60 years, and worsened by existing regional disparities within the health system. The COVID-19 pandemic highlights the need to improve access to high-quality care for critically ill patients admitted to hospital with COVID-19, particularly in LMICs.

Funding

National Council for Scientific and Technological Development (CNPq), Coordinating Agency for Advanced Training of Graduate Personnel (CAPES), Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ), and Instituto de Salud Carlos III.

Systemic Severity and Organ Dysfunction in Subarachnoid Hemorrhage: A Large Retrospective Multicenter Cohort Study

BACKGROUND AND PURPOSE

Acute physiologic derangements and multiple organ dysfunction are common after subarachnoid hemorrhage. We aimed to evaluate the simplified acute physiology score 3 (SAPS-3) and the sequential organ failure assessment (SOFA) scores for the prediction of in-hospital mortality in a large multicenter cohort of SAH patients.

METHODS

This was a retrospective analysis of prospectively collected data from 45 ICUs in Brazil, during 2014 and 2015. Patients admitted with non-traumatic subarachnoid hemorrhage (SAH) were included. Clinical and outcome data were retrieved from an electronic ICU quality registry. SAPS-3 and SOFA scores, without the neurological components (i.e., nSAPS-3 and nSOFA, respectively) were recorded, as well as the World Federation of Neurological Surgeons (WFNS) scale. We used multilevel logistic regression analysis to identify factors associated with in-hospital mortality. We evaluated performance using the area under the receiver operating characteristic curve (AUROC), as well as calibration belts and precision-recall plots.

RESULTS

The study included 997 patients, from which 426 (43%) had poor clinical grade (WFNS 4 or 5) and in-hospital mortality was 34%. Median nSAPS-3 and nSOFA score at admission were 46 (IQR: 38-55) and 2 (0-5), respectively. Non-survivors were older, had higher nSAPS-3 and nSOFA, and more often poor grade. After adjustment for age, poor grade and withdrawal of life sustaining therapies, multivariable analysis identified nSAPS-3 and nSOFA score as independent clinical predictors of in-hospital mortality. The AUROC curve that included nSAPS-3 and nSOFA scores significantly improved the already good discrimination and calibration of age and WFNS to predict in-hospital mortality (AUROC: 0.89 for the full final model vs. 0.85 for age and WFNS; P < 0.0001).

CONCLUSIONS

nSAPS-3 and nSOFA scores were independently associated with in-hospital mortality after SAH. The addition of these scores improved early prediction of hospital mortality in our cohort and should be integrated to other specific prognostic indices in the early assessment of SAH.

Structure and process associated with the efficiency of intensive care units in low-resource settings: An analysis of the CHECKLIST-ICU trial database

PURPOSE

Characteristics of structure and process impact ICU performance and the outcomes of critically ill patients. We sought to identify organizational characteristics associated with efficient ICUs in low-resource settings.

MATERIALS AND METHODS

This is a secondary analysis of a multicenter cluster-randomized clinical trial in Brazil (CHECKLIST-ICU). Efficient units were defined by standardized mortality ratio (SMR) and standardized resource use (SRU) lower than the overall medians and non-efficient otherwise. We used a regularized logistic regression model to evaluate associations between organizational factors and efficiency.

RESULTS

From 118 ICUs (13,635 patients), 47 units were considered efficient and 71 non-efficient. Efficient units presented lower incidence rates (median[IQR]) of central line-associated bloodstream infections (4.95[0.00-22.0] vs 6.29[0.00-25.6], p = .04), utilization rates of mechanical ventilation (0.41[0.07-0.73] vs 0.58[0.19-0.82], p < .001), central venous catheter (0.67[0.15-0.98] vs 0.78[0.33-0.98], p = .04), and indwelling urinary catheter (0.62[0.22-0.95] vs 0.76[0.32-0.98], p < .01) than non-efficient units. The reported active surveillance of ventilator-associated pneumonia (OR = 1.72; 95%CI, 1.16-2.57) and utilization of central venous catheters (OR = 1.94; 95%CI, 1.32-2.94) were associated with efficient ICUs.

CONCLUSIONS

In low-resource settings, active surveillance of nosocomial infections and the utilization of invasive devices were associated with efficiency, supporting the management and evaluation of performance indicators as a starting point for improvement in ICU.