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Objectives: To describe the characteristics of a large cohort of cancer patients receiving mechanical ventilation for >24 hrs and to identify clinical features predictive of in-hospital death.Design: Prospective cohort study.Setting: Ten-bed oncologic medical-surgical intensive care unit.Patients: A total of 463 consecutive patients were included over a 45-month period.Interventions: None.Measurements and Main Results: Data were collected on the day of admission to the intensive care unit. The intensive care unit and hospital mortality rates were 50% and 64%, respectively. There were 359 (78%) patients with solid tumors and 104 (22%) with hematologic malignancies; 35 (8%) patients had leukopenia. Sepsis (63%), coma (15%), invasion or compression by tumor (11%), pulmonary embolism (7%), and cardiopulmonary arrest (6%) were the main reasons for mechanical ventilation. The independent unfavorable risk factors for mortality were older age (odds ratio, 3.09; 95% confidence interval, 1.61-5.93, for patients 40-70 yrs old, and odds ratio, 9.26; 95% confidence interval, 4.16-20.58, for patients >70 yrs old); performance status 3-4 (odds ratio, 2.51; 95% confidence interval, 1.40-4.51); cancer recurrence/progression (odds ratio, 3.43; 95% confidence interval, 1.81-6.53); Pao2/Fio2 ratio <150 (odds ratio, 2.64; 95% confidence interval, 1.40-4.99); Sequential Organ Failure Assessment score (excluding respiratory domain, each 4 points; odds ratio, 2.34; 95% confidence interval, 1.70-3.24); and airway/pulmonary invasion or compression by tumor as a reason for mechanical ventilation (odds ratio, 5.73; 95% confidence interval, 1.92-17.08).Conclusions: Severity of acute organ failures, poor performance status, cancer status, and older age were the main determinants of mortality. The appropriate use of such easily available clinical characteristics may avoid forgoing intensive care for patients with a chance of survival. [ABSTRACT FROM AUTHOR]

Germ-cell tumors are a high-proliferative type of cancer that may evolve to significant bulky disease. Tumor lysis syndrome is rarely reported in this setting. The reports of three patients with germ-cell tumors who developed severe acute tumor lysis syndrome following the start of their anticancer therapy are presented. All patients developed renal dysfunction and multiorgan failure. Patients with extensive germ-cell tumors should be kept on close clinical and laboratory monitoring. Physicians should be aware of this uncommon but severe complication and consider early admission to the intensive care unit for the institution of measures to prevent acute renal failure. [ABSTRACT FROM AUTHOR]

Objective: Patients with acute pulmonary hypertension during an acute pulmonary embolism (PE) can develop stroke due to paradoxical embolism in the presence of a patent foramen ovale (PFO). We evaluated the current evidence regarding the risk of ischemic stroke and mortality in patients with PE and a PFO., Methods: We performed a systematic review and meta-analysis of studies found on PubMed, the Cochrane Library, Embase, SCOPUS, and the BVS portal. We included full-length reports, prospective observational cohorts, and clinical trials of adult patients (aged ≥18 years) diagnosed with PE and investigating the presence of PFO and new ischemic brain injuries. This study is registered with PROSPERO (CRD42023467133)., Results: The initial search identified 1398 articles. After applying exclusion criteria, only 8 articles remained, including a total of 1197 individuals with PE, among whom PFO was identified in 318 patients. Ischemic stroke occurred in 62/318 (19.5 %) individuals in the PFO group and in 40/879 (4.5 %) individuals in the non-PFO group. The prevalence of ischemic stroke in the PFO group was higher than in the non-PFO group in all eight studies. The meta-analysis showed that PFO was significantly associated with ischemic stroke in patients with PE compared to those without PFO (odds ratio 5.36, 95 % CI 3.20-8.99, p < 0.00001; I² = 0 %). Three studies also reported higher mortality in the PFO group., Conclusion: Patent foramen ovale is a common condition in patients with acute PE and is associated with a higher incidence of ischemic stroke and increased mortality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Importance: Despite its implementation in several countries, there has not been a randomized clinical trial to assess whether telemedicine in intensive care units (ICUs) could improve clinical outcomes of critically ill patients., Objective: To determine whether an intervention comprising daily multidisciplinary rounds and monthly audit and feedback meetings performed by a remote board-certified intensivist reduces ICU length of stay (LOS) compared with usual care., Design, Setting, and Participants: A parallel cluster randomized clinical trial with a baseline period in 30 general ICUs in Brazil in which daily multidisciplinary rounds performed by board-certified intensivists were not routinely available. All consecutive adult patients (aged ≥18 years) admitted to the participating ICUs, excluding those admitted due to justice-related issues, were enrolled between June 1, 2019, and April 7, 2021, with last follow-up on July 6, 2021., Intervention: Remote daily multidisciplinary rounds led by a board-certified intensivist through telemedicine, monthly audit and feedback meetings for discussion of ICU performance indicators, and provision of evidence-based clinical protocols., Main Outcomes and Measures: The primary outcome was ICU LOS at the patient level. Secondary outcomes included ICU efficiency, in-hospital mortality, incidence of central line-associated bloodstream infections, ventilator-associated events, catheter-associated urinary tract infections, ventilator-free days at 28 days, patient-days receiving oral or enteral feeding, patient-days under light sedation, and rate of patients with oxygen saturation values under that of normoxemia, assessed using generalized linear mixed models., Results: Among 17 024 patients (1794 in the baseline period and 15 230 in the intervention period), the mean (SD) age was 61 (18) years, 44.7% were female, the median (IQR) Sequential Organ Failure Assessment score was 6 (2-9), and 45.5% were invasively mechanically ventilated at admission. The median (IQR) time under intervention was 20 (16-21) months. Mean (SD) ICU LOS, adjusted for baseline assessment, did not differ significantly between the tele-critical care and usual care groups (8.1 [10.0] and 7.1 [9.0] days; percentage change, 8.2% [95% CI, -5.4% to 23.8%]; P = .24). Results were similar in sensitivity analyses and prespecified subgroups. There were no statistically significant differences in any other secondary or exploratory outcomes., Conclusions and Relevance: Daily multidisciplinary rounds conducted by a board-certified intensivist through telemedicine did not reduce ICU LOS in critically ill adult patients., Trial Registration: ClinicalTrials.gov Identifier: NCT03920501.

Objective: To examine the associations between the scientific output of Brazilian intensive care units and their organizational characteristics., Methods: This study is a re-analysis of a previous retrospective cohort that evaluated organizational intensive care unit characteristics and their associations with outcomes. We analyzed data from 93 intensive care units across Brazil. Intensive care units were assessed for scientific productivity and the effects of their research activities, using indicators of care for comparison. We defined the most scientifically productive intensive care units as those with numerous publications and a SCImago Journal Rank score or an H-index above the median values of the participating intensive care units., Results: Intensive care units with more publications, higher SCImago Journal Rank scores and higher H-index scores had a greater number of certified intensivists (median of 7; IQR 5 - 10 versus 4; IQR 2 - 8; with p < 0.01 for the comparison between intensive care units with more versus fewer publications). Intensive care units with higher SCImago Journal Rank scores and H-index scores also had a greater number of fully implemented protocols (median of 8; IQR 6 - 8 versus 5; IQR 3.75 - 7.25; p < 0.01 for the comparison between intensive care units with higher versus lower SCImago Journal Rank scores)., Conclusions: Scientific engagement was associated with better staffing patterns and greater protocol implementation, suggesting that research activity may be an indicator of better intensive care unit organization and care delivery.

Competing Interests: Dr. Salluh is founder of Epimed Monitor, an electronic healthcare system used to track ICU quality metrics. Dr. Salluh is supported, in part, by individual research grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação Carlos Chagas Filho de Amparo à Pesquisa do Rio de Janeiro (FAPERJ). The remaining authors have disclosed that they do not have any potential conflicts of interest.

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., Methods: A retrospective analysis of 277,459 patients admitted to 128 Brazilian and Uruguayan ICUs over three years. ICU efficiency was assessed using the average standardised efficiency ratio (ASER), measured as the average of the standardised mortality ratio (SMR) and the standardised resource use (SRU) according to the SAPS-3 score. Using a causal inference framework, we estimated and compared the conditional average treatment effect (CATE) of seven common structural and organisational factors on ICU efficiency using LRM with interaction terms and CRF., Results: The hospital mortality was 14 %; median ICU and hospital lengths of stay were 2 and 7 days, respectively. Overall median SMR was 0.97 [IQR: 0.76,1.21], median SRU was 1.06 [IQR: 0.79,1.30] and median ASER was 0.99 [IQR: 0.82,1.21]. Both CRF and LRM showed that the average number of nurses per ten beds was independently associated with ICU efficiency (CATE [95 %CI]: -0.13 [-0.24, -0.01] and -0.09 [-0.17,-0.01], respectively). Finally, CRF identified some specific ICUs with a significant CATE in exposures that did not present a significant average effect., Conclusion: In general, both methods were comparable to identify organisational factors significantly associated with CATE on ICU efficiency. CRF however identified specific ICUs with significant effects, even when the average effect was nonsignificant. This can assist healthcare managers in further in-dept evaluation of process interventions to improve ICU efficiency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In recent decades, several databases of critically ill patients have become available in both low-, middle-, and high-income countries from all continents. These databases are also rich sources of data for the surveillance of emerging diseases, intensive care unit performance evaluation and benchmarking, quality improvement projects and clinical research. The Epimed Monitor database is turning 15 years old in 2024 and has become one of the largest of these databases. In recent years, there has been rapid geographical expansion, an increase in the number of participating intensive care units and hospitals, and the addition of several new variables and scores, allowing a more complete characterization of patients to facilitate multicenter clinical studies. As of December 2023, the database was being used regularly for 23,852 beds in 1,723 intensive care units and 763 hospitals from ten countries, totaling more than 5.6 million admissions. In addition, critical care societies have adopted the system and its database to establish national registries and international collaborations. In the present review, we provide an updated description of the database; report experiences of its use in critical care for quality improvement initiatives, national registries and clinical research; and explore other potential future perspectives and developments.

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., Competing Interests: Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: M. S. and J. I. F. S. are the founders and equity shareholders of Epimed Solutions, which commercializes the Epimed Monitor System, a cloud-based ICU management and benchmarking software. None declared (L. S. L. B., S. H., P. K., O. T. R., F. G. Z., F. A. B.)., (Copyright © 2023 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Community acquired pneumonia (CAP) is a prevalent infectious disease often requiring hospitalization, although its diagnosis remains challenging as there is no gold standard test. In severe CAP, clinical and radiologic criteria have poor sensitivity and specificity, and microbiologic documentation is usually delayed and obtained in less than half of sCAP patients. Biomarkers could be an alternative for diagnosis, treatment monitoring and establish resolution. Beyond the existing evidence about biomarkers as an adjunct diagnostic tool, most evidence comes from studies including CAP patients in primary care or emergency departments, and not only sCAP patients. Ideally, biomarkers used in combination with signs, symptoms, and radiological findings can improve clinical judgment to confirm or rule out CAP diagnosis, and may be valuable adjunctive tools for risk stratification, differentiate viral pneumonia and monitoring the course of CAP. While no single biomarker has emerged as an ideal one, CRP and PCT have gathered the most evidence. Overall, biomarkers offer valuable information and can enhance clinical decision-making in the management of CAP, but further research and validation are needed to establish their optimal use and clinical utility., Competing Interests: None declared., (Thieme. All rights reserved.)

Severe community-acquired pneumonia (sCAP) remains one of the leading causes of admission to the intensive care unit, thus consuming a large share of resources and is associated with high mortality rates worldwide. The evidence generated by clinical studies in the last decade was translated into recommendations according to the first published guidelines focusing on severe community-acquired pneumonia. Despite the advances proposed by the present guidelines, several challenges preclude the prompt implementation of these diagnostic and therapeutic measures. The present article discusses the challenges for the broad implementation of the sCAP guidelines and proposes solutions when applicable., (© 2024. Springer-Verlag GmbH Germany, part of Springer Nature.)

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., Competing Interests: Declaration of Competing Interest The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Dr. Salluh is founder and shareholder of Epimed Solutions®, which commercializes the Epimed Monitor System®, a cloud-based software for ICU management and benchmarking. The other authors declare that they have no conflict of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

NA., Competing Interests: Declaration of Competing Interest Dr. Salluh is co-founder and shareholder of Epimed Solutions, a cloud-based analytics company. Dr. Pisani receives salary support from Wellcome. Dr. Quintairos has no conflicts of interest to declare., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Introduction: Acute kidney injury (AKI) requiring treatment with renal replacement therapy (RRT) is a common complication after admission to an intensive care unit (ICU) and is associated with significant morbidity and mortality. However, the prevalence of RRT use and the associated outcomes in critically patients across the globe are not well described. Therefore, we describe the epidemiology and outcomes of patients receiving RRT for AKI in ICUs across several large health system jurisdictions., Methods: Retrospective cohort analysis using nationally representative and comparable databases from seven health jurisdictions in Australia, Brazil, Canada, Denmark, New Zealand, Scotland, and the USA between 2006 and 2023, depending on data availability of each dataset. Patients with a history of end-stage kidney disease receiving chronic RRT and patients with a history of renal transplant were excluded., Results: A total of 4,104,480 patients in the ICU cohort and 3,520,516 patients in the mechanical ventilation cohort were included. Overall, 156,403 (3.8%) patients in the ICU cohort and 240,824 (6.8%) patients in the mechanical ventilation cohort were treated with RRT for AKI. In the ICU cohort, the proportion of patients treated with RRT was lowest in Australia and Brazil (3.3%) and highest in Scotland (9.2%). The in-hospital mortality for critically ill patients treated with RRT was almost fourfold higher (57.1%) than those not receiving RRT (16.8%). The mortality of patients treated with RRT varied across the health jurisdictions from 37 to 65%., Conclusion: The outcomes of patients who receive RRT in ICUs throughout the world vary widely. Our research suggests that differences in access to and provision of this therapy are contributing factors., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Objectives: Clinical quality registries (CQRs) have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. This narrative review describes the challenges, proposed solutions, and evidence generated by National ICU registries as facilitators for research and quality improvement., Data Sources: English language articles were identified in PubMed using phrases related to ICU registries, CQRs, outcomes, and case-mix., Study Selection: Original research, review articles, letters, and commentaries, were considered., Data Extraction: Data from relevant literature were identified, reviewed, and integrated into a concise narrative review., Data Synthesis: CQRs have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. The initial experience in European countries and in Oceania ensured that through locally generated data, ICUs could assess their performances by using risk-adjusted measures and compare their results through fair and validated benchmarking metrics with other ICUs contributing to the CQR. The accomplishment of these initiatives, coupled with the increasing adoption of information technology, resulted in a broad geographic expansion of CQRs as well as their use in quality improvement studies, clinical trials as well as international comparisons, and benchmarking for ICUs., Conclusions: ICU registries have provided increased knowledge of case-mix and outcomes of ICU patients based on real-world data and contributed to improve care delivery through quality improvement initiatives and trials. Recent increases in adoption of new technologies (i.e., cloud-based structures, artificial intelligence, machine learning) will ensure a broader and better use of data for epidemiology, healthcare policies, quality improvement, and clinical trials., Competing Interests: Dr. Beane received support for article research from Wellcome Trust/Charity Open Access Fund (COAF). Drs. Pilcher and Litton are members of the Australian and New Zealand Intensive Care Society Centre for Outcome and Resources Evaluation management committee. Drs Salluh is co-founder and shareholder of Epimed Solutions, a healthcare cloud-based analytics company. He is also supported, in part, by individual research grants from the National Council for Scientific and Technological Development and Research Support Foundation of the State of Rio de Janeiro. Dr. Dongelmans is unpaid chair of National Intensive Care Evaluation Foundation. Dr. Ichihara’s primary affiliation is the Department of Healthcare Quality Assessment, which is a social collaboration department at the University of Tokyo supported by National Clinical Database, Johnson & Johnson K.K., and Nipro Corporation. Dr. Vijayaraghavan is the National Coordinator for the Indian Registry of IntenSive Care and is supported for 0.5 full-time equivalent hours by funding from the Wellcome Trust, U.K. Dr. Bagshaw received funding from Baxter and BioPorto. Dr. Hashimoto’s institution received funding from the Japanese Ministry of Health, Labour and Welfare for the Japanese Intensive care PAtient Database (JIPAD), the Japanese Society of Intensive Care Medicine, and JMS. Dr. Haniffa’s institution received funding from Wellcome Trust/COAF and UK Research and Innovation (UKRI); he disclosed that he is an honorary director of National Intensive Care Surveillance MORU. Dr. Pisani received funding from Wellcome Trust and The African Critical Care registry network funded by this UKRI MRC (grant MR/V030884/1). The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Background: It is not known whether anatomical scores perform better than general critical care scores for trauma patients admitted to the intensive care unit (ICU). We compare the predictive performance for hospital mortality of general critical care scores (SAPS 3 and SOFA) with anatomical injury-based scores (Injury Severity Score [ISS] and New ISS [NISS]). Methods: Retrospective cohort study of patients admitted to a specialized trauma ICU from a tertiary hospital in São Paulo, Brazil between May, 2012 and January, 2016. We retrieved data from the ICU database for critical care scores and calculated ISS and NISS from chart data and whole body computed tomography results. We compared the predictive performance for hospital mortality of each model through discrimination, calibration, and decision-curve analysis. Results: The sample comprised 1053 victims of trauma admitted to the ICU, with 84.2% male patients and mean age of 40 (±18) years. Main injury mechanism was blunt trauma (90.7%). Traumatic brain injury was present in 67.8% of patients; 43.3% with severe TBI. At the time of ICU admission, 846 patients (80.3%) were on mechanical ventilation and 644 (64.3%) on vasoactive drugs. Hospital mortality was 23.8% (251). Median SAPS 3 was 41; median maximum SOFA within 24 h of admission, 7; ISS, 29; and NISS, 41. AUROCs (95% CI) were: SAPS 3 = 0.786 (0.756-0.817), SOFA = 0.807 (0.778-0.837), ISS = 0.616 (0.577-0.656), and NISS = 0.689 (0.649-0.729). In pairwise comparisons, SAPS 3 and SOFA did not differ, while both outperformed the anatomical scores ( p  < .001). Maximum SOFA within 24 h of admission presented the best calibration and net benefit in decision-curve analysis. Conclusions: Trauma-specific anatomical scores have fair performance in critically ill trauma patients and are outperformed by SAPS 3 and SOFA. Illness severity is best characterized by organ dysfunction and physiological variables than anatomical injuries., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Subsyndromal delirium (SSD) in the Intensive Care Unit (ICU) is associated with an increased morbidity with unknown post-discharge functional and cognitive outcomes. We performed a prospective multicenter study to analyze the mental status of patients during their first 72 h after ICU admission and its trajectory, with follow-ups at 3 and 6 months after hospital discharge. Amongst the 106 included patients, SSD occurred in 24.5% (n = 26) and was associated with the duration of mechanical ventilation ( p = 0.003) and the length of the ICU stay ( p = 0.002). After the initial 72 h, most of the SSD patients (30.8%) improved and no longer had SSD; 19.2% continued to experience SSD and one patient (3.8%) progressed to delirium. The post-hospital discharge survival rate for the SSD patients was 100% at 3 months and 87.5% at 6 months. At admission, 96.2% of the SSD patients were fully independent in daily living activities, 66.7% at 3-month follow-up, and 100% at 6-month follow-up. Most SSD patients demonstrated a cognitive decline from admission to 3-month follow-up and improved at 6 months (IQCODE-SF: admission 3.13, p < 0.001; 3 months 3.41, p = 0.019; 6 months 3.19, p = 0.194). We concluded that early SSD is associated with worse outcomes, mainly a transitory cognitive decline after hospital discharge at 3 months, with an improvement at 6 months. This highlights the need to prevent and identify this condition during ICU stays.

Objective: To investigate the impact of delirium severity in critically ill COVID-19 patients and its association with outcomes., Methods: This prospective cohort study was performed in two tertiary intensive care units in Rio de Janeiro, Brazil. COVID-19 patients were evaluated daily during the first 7 days of intensive care unit stay using the Richmond Agitation Sedation Scale, Confusion Assessment Method for Intensive Care Unit (CAM-ICU) and Confusion Method Assessment for Intensive Care Unit-7 (CAM-ICU-7). Delirium severity was correlated with outcomes and one-year mortality., Results: Among the 277 COVID-19 patients included, delirium occurred in 101 (36.5%) during the first 7 days of intensive care unit stay, and it was associated with a higher length of intensive care unit stay in days (IQR 13 [7 - 25] versus 6 [4 - 12]; p < 0.001), higher hospital mortality (25.74% versus 5.11%; p < 0.001) and additional higher one-year mortality (5.3% versus 0.6%, p < 0.001). Delirium was classified by CAM-ICU-7 in terms of severity, and higher scores were associated with higher in-hospital mortality (17.86% versus 34.38% versus 38.46%, 95%CI, p value < 0.001). Severe delirium was associated with a higher risk of progression to coma (OR 7.1; 95%CI 1.9 - 31.0; p = 0.005) and to mechanical ventilation (OR 11.09; 95%CI 2.8 - 58.5; p = 0.002) in the multivariate analysis, adjusted by severity and frailty., Conclusion: In patients admitted with COVID-19 in the intensive care unit, delirium was an independent risk factor for the worst prognosis, including mortality. The delirium severity assessed by the CAM-ICU-7 during the first week in the intensive care unit was associated with poor outcomes, including progression to coma and to mechanical ventilation.

The number of patients with cancer requiring intensive care unit admission is increasing around the world. The improvement in the pathophysiological understanding of this group of patients, as well as the increasingly better and more targeted treatment options for their underlying disease, has led to a significant increase in their survival over the past three decades. Within the organizational concepts, it is necessary to know what adds value in the care of critical oncohematological patients. Practices in medicine that do not benefit patients and possibly cause harm are called low-value practices, while high-value practices are defined as high-quality care at relatively low cost. In this article, we discuss ten domains with high-value evidence in the care of cancer patients: (1) intensive care unit admission policies; (2) intensive care unit organization; (3) etiological investigation of hypoxemia; (4) management of acute respiratory failure; (5) management of febrile neutropenia; (6) urgent chemotherapy treatment in critically ill patients; (7) patient and family experience; (8) palliative care; (9) care of intensive care unit staff; and (10) long-term impact of critical disease on the cancer population. The disclosure of such policies is expected to have the potential to change health care standards. We understand that it is a lengthy process, and initiatives such as this paper are one of the first steps in raising awareness and beginning a discussion about high-value care in various health scenarios.

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., (Copyright © 2022 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Purpose: The length of stay (LoS) is one of the most used metrics for resource use in Intensive Care Units (ICU). We propose a structured data-driven methodology to predict the ICU length of stay and the risk of prolonged stay, and its application in a large multicentre Brazilian ICU database., Methods: Demographic data, comorbidities, complications, laboratory data, and primary and secondary diagnosis were prospectively collected and retrospectively analysed by a data-driven methodology, which includes eight different machine learning models and a stacking model. The study setting included 109 mixed-type ICUs from 38 Brazilian hospitals and the external validation was performed by 93 medical-surgical ICUs of 55 hospitals in Brazil., Results: A cohort of 99,492 adult ICU admissions were included from the 1 st of January to the 31 st of December 2019. The stacking model combining Random Forests and Linear Regression presented the best results to predict ICU length of stay (RMSE = 3.82; MAE = 2.52; R² = 0.36). The prediction model for the risk of long stay were accurate to early identify prolonged stay patients (Brier Score = 0.04, AUC = 0.87, PPV = 0.83, NPV = 0.95)., Conclusion: The data-driven methodology to predict ICU length of stay and the risk of long-stay proved accurate in a large multicentre cohort of general ICU patients. The proposed models are helpful to predict the individual length of stay and to early identify patients with high risk of prolonged stay., (Copyright © 2022 Société française d'anesthésie et de réanimation (Sfar). Published by Elsevier Masson SAS. All rights reserved.)

Despite recent advances in the field, the association between subsyndromal delirium (SSD) in the ICU and poor outcomes is not entirely clear. We performed a retrospective multicentric observational study analyzing mental status during the first 72 h of ICU stay. Of the 681 patients included, SSD occurred in 22.7%. Considering the worst cognitive assessment during the first 72 h, 233 (34%) patients had normal mental status, 124 (18%) patients had SSD and 324 (48%) patients had delirium or coma. SSD was not independently associated with an increased risk of death when compared with normal mental status (OR 95%IC 1.0 vs. 1.35 [0.73−1.49], p = 0.340), but was associated with a longer ICU LOS (7.0 (4−12) vs. 4 (3−8) days, p < 0.001). SSD patients who deteriorated to delirium or coma (21%) had a longer ICU LOS in comparison with those who improved or maintained mental status (8 (5−11) vs. 6 (4−8) days, p = 0.025), but did not have an increase in mortality. The main factors associated with the progression from SSD to delirium or coma were the use of mechanical ventilation, the use of intravenous benzodiazepines and a baseline APACHE II score > 23 points. Our findings support the association of SSD with increased ICU LOS, but not with ICU mortality. Monitoring the trajectory of SSD early at ICU admission can help to identify patients with increased risk of conversion from SSD to delirium or coma.

Competing Interests: Dr. Salluh is a shareholder of Epimed Solutions. Dr. Haniffa was a founding collaborator of the Global Open Source Severity of Illness Score initiative. Dr. Haniffa disclosed that he is the founding collaborator of Linking of Global Intensive Care, director of Crit Care Asia Network Registry, trustee of Network for Improving Critical Care Systems and Training, and co-lead of Critical Care Asia and Africa; received support for article research from the Wellcome Trust/Charity Open Access Fund. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Purpose: Studies of critically ill hematopoietic stem cell transplantation (HSCT) recipients have mainly been single-center and focused on allogenic HSCT recipients. We aimed to describe a cohort of autologous HSCT with an unplanned intensive care unit (ICU) admission., Methods: This study is a retrospective cohort study of autologous HSCT performed as a treatment for a hematological malignancy, during their first unplanned ICU admission in 50 hospitals in Brazil. We assessed the hospital mortality and the association between mechanical ventilation, vasopressors, and renal replacement therapy and hospital mortality in autologous HSCT recipients, adjusted for potential confounders., Results: We included 301 patients. Multiple myeloma was the most common malignancy driving to HSCT. ICU and hospital mortality were 22.9% and 37.5%, respectively. After adjustment for potential confounders, mechanical ventilation (OR = 9.10; CI 95%, 4.82-17.15) was associated with hospital mortality, but vasopressors (OR = 1.43; CI 95%, 0.77-2.64) and renal replacement therapy (OR = 1.30; CI 95%, 0.63-2.66) were not., Conclusions: In this large cohort of critically ill autologous HSCT recipients, mechanical ventilation was the only organ support-therapy associated with increased mortality in autologous HSCT recipients., Competing Interests: Declaration of Competing Interest MS is founder of Epimed Monitor®, an electronic healthcare system used to collect data and track ICU quality metrics. FGZ has received grants for investigator-initiated studies from Ionis Pharmaceuticals (USA), Bactiguard (Sweden) and Brazilian Ministry of Health, none related to the scope of this study., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: To characterize the knowledge and perceived attitudes toward pharmacologic interventions for light sedation in mechanically ventilated patients and to understand the current gaps comparing current practice with the recommendations of the Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the Intensive Care Unit., Methods: This was a cross-sectional cohort study based on the application of an electronic questionnaire focused on sedation practices., Results: A total of 303 critical care physicians provided responses to the survey. Most respondents reported routine use of a structured sedation scale (281; 92.6%). Almost half of the respondents reported performing daily interruptions of sedation (147; 48.4%), and the same percentage of participants (48.0%) agreed that patients are often over sedated. During the COVID-19 pandemic, participants reported that patients had a higher chance of receiving midazolam compared to before the pandemic (178; 58.8% versus 106; 34.0%; p = 0.05), and heavy sedation was more common during the COVID-19 pandemic (241; 79.4% versus 148; 49.0%; p = 0.01)., Conclusion: This survey provides valuable data on the perceived attitudes of Brazilian intensive care physicians regarding sedation. Although daily interruption of sedation was a well-known concept and sedation scales were often used by the respondents, insufficient effort was put into frequent monitoring, use of protocols and systematic implementation of sedation strategies. Despite the perception of the benefits linked with light sedation, there is a need to identify improvement targets to propose educational strategies to improve current practices.

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 2 ., 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., Competing Interests: Declaration of Competing Interest The funders had no role in study design, data collection and analysis, decision to publish, or the preparation of the manuscript. Dr. Soares and Dr. Salluh are founders and equity shareholders of Epimed Solutions®, which commercializes the Epimed Monitor System®, a cloud-based software for ICU management and benchmarking. Dr. Zampieri has received grants for investigator-initiated studies from Ionis Pharmaceuticals (USA), Bactiguard (Sweden) and the Brazilian Ministry of Health, none related to the scope of this study. N.F. de Keizer and D.A. Dongelmans are members of the board of the Dutch National Intensive Care Evaluation (NICE) foundation. The NICE foundation pays the department of Medical Informatics, Amsterdam UMC, for processing data of all Dutch ICUs into audit and feedback information. S.A., N.F. de Keizer and F. Bakhshi-Raiez are employees of the department of medical informatics and work on the NICE project. The other authors declare that they have no conflict of interest., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Hospital length of stay and mortality are associated with resource use and clinical severity, respectively, in patients admitted to the intensive care unit (ICU) with acute stroke. We proposed a structured data-driven methodology to develop length of stay and 30-day mortality prediction models in a large multicenter Brazilian ICU cohort., Methods: We analyzed data from 130 ICUs from 43 Brazilian hospitals. All consecutive adult patients admitted with stroke (ischemic or nontraumatic hemorrhagic) to the ICU from January 2011 to December 2020 were included. Demographic data, comorbidities, acute disease characteristics, organ support, and laboratory data were retrospectively analyzed by a data-driven methodology, which included seven different types of machine learning models applied to training and test sets of data. The best performing models, based on discrimination and calibration measures, are reported as the main results. Outcomes were hospital length of stay and 30-day in-hospital mortality., Results: Of 17,115 ICU admissions for stroke, 16,592 adult patients (13,258 ischemic and 3334 hemorrhagic) were analyzed; 4298 (26%) patients had a prolonged hospital length of stay (> 14 days), and 30-day mortality was 8% (n = 1392). Prolonged hospital length of stay was best predicted by the random forests model (Brier score = 0.17, area under the curve = 0.73, positive predictive value = 0.61, negative predictive value = 0.78). Mortality prediction also yielded the best discrimination and calibration through random forests (Brier score = 0.05, area under the curve = 0.90, positive predictive value = 0.66, negative predictive value = 0.94). Among the 20 strongest contributor variables in both models were (1) premorbid conditions (e.g., functional impairment), (2) multiple organ dysfunction parameters (e.g., hypotension, mechanical ventilation), and (3) acute neurological aspects of stroke (e.g., Glasgow coma scale score on admission, stroke type)., Conclusions: Hospital length of stay and 30-day mortality of patients admitted to the ICU with stroke were accurately predicted through machine learning methods, even in the absence of stroke-specific data, such as the National Institutes of Health Stroke Scale score or neuroimaging findings. The proposed methods using general intensive care databases may be used for resource use allocation planning and performance assessment of ICUs treating stroke. More detailed acute neurological and management data, as well as long-term functional outcomes, may improve the accuracy and applicability of future machine-learning-based prediction algorithms., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.)