Persistent carbapenem resistance in mechanically ventilated ICU patients: A before-and-after analysis of the COVID-19 surge.

OBJECTIVE: How did the antimicrobial resistance profile of critically ill patients evolve before, during, and after COVID-19 surge periods? METHODS: We retrospectively analysed all critically ill mechanically ventilated adult patients admitted to eight Brazilian hospitals from January 1st, 2018, to April 30th, 2023. We stratified the patients into three periods based on their admission date: pre-surge (Jan 01/2018-Mar 01/2020), surge (Mar 01/2020 - Oct 01/2021), and post-surge (after Oct 01/2021). We compared the proportion of positive cultures, prevalence of pathogens, and resistance rates across periods using the rate ratios (RR) and their 95% confidence intervals (95% CI). RESULTS: We analysed 9,780 ICU patients: 3,718 were in the pre-surge, 3,815 in the surge, and 2,247 in the post-surge period. Patients in the surge period were younger (median: 70 vs. 74 pre-surge vs. 75 post-surge) and presented a higher duration of invasive mechanical ventilation (median 7 vs. 5 days). The utilisation of blood and respiratory cultures increased throughout periods (56.9 pre-surge vs. 69.4 surge vs. 70.4 patients/1,000 patient days post-surge). The isolation of carbapenem-resistant gram-negative bacteria increased during the surge (RR [95% CI]: 1.8 [1.5-2.2], compared to pre-surge), decreased in post-surge (RR [95% CI]: 0.72 [0.6-0.9], and remained higher than pre-surge (RR [95% CI]: 1.3 [1.0-1.6]). Resistance rates for Pseudomonas aeruginosa reduced from 32% in pre- to 23% post-surge, whereas Klebsiella pneumoniae doubled during the surge, 26% to 52%, and remained higher than pre-surge. CONCLUSION: Carbapenem resistance increased during the surge period. Although it decreased post-surge, it remained higher than the rates observed before the pandemic.

Long Covid: a global health issue - a prospective, cohort study set in four continents.

INTRODUCTION: A proportion of people develop Long Covid after acute COVID-19, but with most studies concentrated in high-income countries (HICs), the global burden is largely unknown. Our study aims to characterise long-term COVID-19 sequelae in populations globally and compare the prevalence of reported symptoms in HICs and low-income and middle-income countries (LMICs). METHODS: A prospective, observational study in 17 countries in Africa, Asia, Europe and South America, including adults with confirmed COVID-19 assessed at 2 to <6 and 6 to <12 months post-hospital discharge. A standardised case report form developed by International Severe Acute Respiratory and emerging Infection Consortium's Global COVID-19 Follow-up working group evaluated the frequency of fever, persistent symptoms, breathlessness (MRC dyspnoea scale), fatigue and impact on daily activities. RESULTS: Of 11 860 participants (median age: 52 (IQR: 41-62) years; 52.1% females), 56.5% were from HICs and 43.5% were from LMICs. The proportion identified with Long Covid was significantly higher in HICs vs LMICs at both assessment time points (69.0% vs 45.3%, p<0.001; 69.7% vs 42.4%, p<0.001). Participants in HICs were more likely to report not feeling fully recovered (54.3% vs 18.0%, p<0.001; 56.8% vs 40.1%, p<0.001), fatigue (42.9% vs 27.9%, p<0.001; 41.6% vs 27.9%, p<0.001), new/persistent fever (19.6% vs 2.1%, p<0.001; 20.3% vs 2.0%, p<0.001) and have a higher prevalence of anxiety/depression and impact on usual activities compared with participants in LMICs at 2 to <6 and 6 to <12 months post-COVID-19 hospital discharge, respectively. CONCLUSION: Our data show that Long Covid affects populations globally, manifesting similar symptomatology and impact on functioning in both HIC and LMICs. The prevalence was higher in HICs versus LMICs. Although we identified a lower prevalence, the impact of Long Covid may be greater in LMICs if there is a lack of support systems available in HICs. Further research into the aetiology of Long Covid and the burden in LMICs is critical to implement effective, accessible treatment and support strategies to improve COVID-19 outcomes for all.

Prevalence, risk factors, and impact of long COVID in a socially vulnerable community in Brazil: a prospective cohort study.

Background: Long COVID is an emerging global public health issue. Socially vulnerable communities in low- and-middle-income countries were severely impacted by the pandemic and are underrepresented in research. This prospective study aimed to determine the prevalence of long COVID, its impact on health, and associated risk factors in one such community in Rio de Janeiro, Brazil. Methods: A total of 710 individuals aged 18 and older, with confirmed SARS-CoV-2 infection at least three months prior, were enrolled between November 25, 2021, and May 5, 2022. Participants were assessed via telephone or in person using a standardized questionnaire to evaluate their perception of recovery, symptoms, quality of life, and functional status. Findings: Twenty percent of participants did not feel fully recovered, 22% experienced new or persistent symptoms, 26% had worsened functional status, 18% had increased dyspnoea, and 32% reported a worse quality of life. Persistent symptoms included headache, cough, fatigue, muscle pain, and shortness of breath. Dyspnoea during the acute phase was the strongest independent predictor of worsening outcomes. Females and individuals with comorbidities were more likely to report worse recovery, functioning, dyspnoea, and quality of life. Interpretation: Our findings reveal a high burden of severe and persistent physical and mental health sequelae in a socially vulnerable community following COVID-19. Funding: UK Foreign, Commonwealth and Development Office and Wellcome Trust Grant (222048/Z/20/Z), Fundação Oswaldo Cruz (FIOCRUZ), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), and the Centers for Disease Control and Prevention (CDC).

Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency.

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.

Prediction of multidrug-resistant bacteria (MDR) hospital-acquired infection (HAI) and colonisation: A systematic review.

BACKGROUND: Hospital-Acquired Infections (HAI) represent a public health priority in most countries worldwide. Our main objective was to systematically review the quality of the predictive modeling literature regarding multidrug-resistant gram-negative bacteria in Intensive Care Units (ICUs). METHODS: We conducted and reported a Systematic Literature Review according to the recommendations of the PRISMA statement. We analysed the quality of the articles in terms of adherence to the TRIPOD checklist. RESULTS: The initial search identified 1935 papers and 15 final articles were included in the review. Most studies analysed used traditional prediction models (logistic regression), and only three developed machine-learning techniques. We noted poor adherence to the main methodological issues recommended in the TRIPOD checklist to develop prediction models, such as handling missing data (20% adherence), model-building procedures (20% adherence), assessing model performance (47% adherence), and reporting performance measures (33% adherence). CONCLUSIONS: Our review found few studies that use efficient alternatives to predict the acquisition of multidrug-resistant gram-negative bacteria in ICUs. Furthermore, we noted a lack of strategies for dealing with missing data, feature selection, and imbalanced datasets, a common problem in HAI studies.

Impact of the first year of COVID-19 vaccination strategy in Brazil: an ecological study.

OBJECTIVES: No consensus exists about the best COVID-19 vaccination strategy to be adopted by low-income and middle-income countries. Brazil adopted an age-based calendar strategy to reduce mortality and the burden on the healthcare system. This study evaluates the impact of the vaccination campaign in Brazil on the progression of the reported COVID-19 deaths. METHODS: This ecological study analyses the dynamic of vaccination coverage and COVID-19 deaths in hospitalised adults (≥20 years) during the first year of the COVID-19 vaccination roll-out (January to December 2021) using nationwide data (DATASUS). We stratified the adult population into 20-49, 50-59, 60-69 and 70+ years. The dynamic effect of the vaccination campaign on mortality rates was estimated by applying a negative binomial regression. The prevented and possible preventable deaths (observed deaths higher than expected) and potential years of life lost (PYLL) for each age group were obtained in a counterfactual analysis. RESULTS: During the first year of COVID-19 vaccination, 266 153 517 doses were administered, achieving 91% first-dose coverage. A total of 380 594 deaths were reported, 154 091 (40%) in 70+ years and 136 804 (36%) from 50-59 or 20-49 years. The mortality rates of 70+ decreased by 52% (rate ratio [95% CI]: 0.48 [0.43-0.53]) in 6 months, whereas rates for 20-49 were still increasing due to low coverage (52%). The vaccination roll-out strategy prevented 59 618 deaths, 53 088 (89%) from those aged 70+ years. However, the strategy did not prevent 54 797 deaths, 85% from those under 60 years, being 26 344 (45%) only in 20-49, corresponding to 1 589 271 PYLL, being 1 080 104 PYLL (68%) from those aged 20-49 years. CONCLUSION: The adopted aged-based calendar vaccination strategy initially reduced mortality in the oldest but did not prevent the deaths of the youngest as effectively as compared with the older age group. Countries with a high burden, limited vaccine supply and young populations should consider other factors beyond the age to prioritise who should be vaccinated first.

Changes in neuroinflammatory biomarkers correlate with disease severity and neuroimaging alterations in patients with COVID-19 neurological complications.

COVID-19 induces acute and persistent neurological symptoms in mild and severe cases. Proposed concomitant mechanisms include direct viral infection and strain, coagulopathy, hypoxia, and neuroinflammation. However, underlying molecular alterations associated with multiple neurological outcomes in both mild and severe cases are majorly unexplored. To illuminate possible mechanisms leading to COVID-19 neurological disease, we retrospectively investigated in detail a cohort of 35 COVID-19 mild and severe hospitalized patients presenting neurological alterations subject to clinically indicated cerebrospinal fluid (CSF) sampling. Clinical and neurological investigation, brain imaging, viral sequencing, and cerebrospinal CSF analyses were carried out. We found that COVID-19 patients presented heterogeneous neurological symptoms dissociated from lung burden. Nasal swab viral sequencing revealed a dominant strain at the time of the study, and we could not detect traces of SARS-CoV-2's spike protein in patients' CSF by multiple reaction monitoring analysis. Patients presented ubiquitous systemic hyper-inflammation and broad alterations in CSF proteomics related to inflammation, innate immunity, and hemostasis, irrespective of COVID-19 severity or neuroimaging alterations. Elevated CSF interleukin-6 (IL6) correlated with disease severity (sex-, age-, and comorbidity-adjusted mean Severe 24.5 pg/ml, 95% confidence interval (CI) 9.62-62.23 vs. Mild 3.91 pg/mL CI 1.5-10.3 patients, p = 0.019). CSF tumor necrosis factor-alpha (TNFα) and IL6 levels were higher in patients presenting pronounced neuroimaging alterations compared to those who did not (sex-, age-, and comorbidity-adjusted mean TNFα Pronounced 3.4, CI 2.4-4.4 vs. Non-Pronounced 2.0, CI 1.4-2.5, p = 0.022; IL6 Pronounced 33.11, CI 8.89-123.31 vs Non-Pronounced 6.22, CI 2.9-13.34, p = 0.046). Collectively, our findings put neuroinflammation as a possible driver of COVID-19 acute neurological disease in mild and severe cases.

Long term cognitive dysfunction among critical care survivors: associated factors and quality of life-a multicenter cohort study.

OBJECTIVES: To identify the prevalence and associated factors of cognitive dysfunction, 1 year after ICU discharge, among adult patients, and it´s relation with quality of life. METHODS: Multicenter, prospective cohort study including ICUs of 10 tertiary hospitals in Brazil, between May 2014 and December 2018. The patients included were 452 adult ICU survivors (median age 60; 47.6% women) with an ICU stay greater than 72 h. RESULTS: At 12 months after ICU discharge, a Montreal Cognitive Assessment (tMOCA) telephone score of less than 12 was defined as cognitive dysfunction. At 12 months, of the 452 ICU survivors who completed the cognitive evaluation 216 (47.8%) had cognitive dysfunction. In multivariable analyses, the factors associated with long-term (1-year) cognitive dysfunction were older age (Prevalence Ratio-PR = 1.44, P < 0.001), absence of higher education (PR = 2.81, P = 0.005), higher comorbidities on admission (PR = 1.089; P = 0.004) and delirium (PR = 1.13, P < 0.001). Health-related Quality of life (HRQoL), assessed by the mental and physical dimensions of the SF-12v2, was significantly better in patients without cognitive dysfunction (Mental SF-12v2 Mean difference = 2.54; CI 95%, - 4.80/- 0.28; p = 0.028 and Physical SF-12v2 Mean difference = - 2.85; CI 95%, - 5.20/- 0.50; P = 0.018). CONCLUSIONS: Delirium was found to be the main modifiable predictor of long-term cognitive dysfunction in ICU survivors. Higher education consistently reduced the probability of having long-term cognitive dysfunction. Cognitive dysfunction significantly influenced patients' quality of life, leading us to emphasize the importance of cognitive reserve for long-term prognosis after ICU discharge.

Data challenges for international health emergencies: lessons learned from ten international COVID-19 driver projects.

The COVID-19 pandemic highlighted the importance of international data sharing and access to improve health outcomes for all. The International COVID-19 Data Alliance (ICODA) programme enabled 12 exemplar or driver projects to use existing health-related data to address major research questions relating to the pandemic, and developed data science approaches that helped each research team to overcome challenges, accelerate the data research cycle, and produce rapid insights and outputs. These approaches also sought to address inequity in data access and use, test approaches to ethical health data use, and make summary datasets and outputs accessible to a wider group of researchers. This Health Policy paper focuses on the challenges and lessons learned from ten of the ICODA driver projects, involving researchers from 19 countries and a range of health-related datasets. The ICODA programme reviewed the time taken for each project to complete stages of the health data research cycle and identified common challenges in areas such as data sharing agreements and data curation. Solutions included provision of standard data sharing templates, additional data curation expertise at an early stage, and a trusted research environment that facilitated data sharing across national boundaries and reduced risk. These approaches enabled the driver projects to rapidly produce research outputs, including publications, shared code, dashboards, and innovative resources, which can all be accessed and used by other research teams to address global health challenges.

Prevention of lipid droplet accumulation by DGAT1 inhibition ameliorates sepsis-induced liver injury and inflammation.

Background & Aims: Lipid droplet (LD) accumulation in cells and tissues is understood to be an evolutionarily conserved tissue tolerance mechanism to prevent lipotoxicity caused by excess lipids; however, the presence of excess LDs has been associated with numerous diseases. Sepsis triggers the reprogramming of lipid metabolism and LD accumulation in cells and tissues, including the liver. The functions and consequences of sepsis-triggered liver LD accumulation are not well known. Methods: Experimental sepsis was induced by CLP (caecal ligation and puncture) in mice. Markers of hepatic steatosis, liver injury, hepatic oxidative stress, and inflammation were analysed using a combination of functional, imaging, lipidomic, protein expression and immune-enzymatic assays. To prevent LD formation, mice were treated orally with A922500, a pharmacological inhibitor of DGAT1. Results: We identified that liver LD overload correlates with liver injury and sepsis severity. Moreover, the progression of steatosis from 24 h to 48 h post-CLP occurs in parallel with increased cytokine expression, inflammatory cell recruitment and oxidative stress. Lipidomic analysis of purified LDs demonstrated that sepsis leads LDs to harbour increased amounts of unsaturated fatty acids, mostly 18:1 and 18:2. An increased content of lipoperoxides within LDs was also observed. Conversely, the impairment of LD formation by inhibition of the DGAT1 enzyme reduces levels of hepatic inflammation and lipid peroxidation markers and ameliorates sepsis-induced liver injury. Conclusions: Our results indicate that sepsis triggers lipid metabolism alterations that culminate in increased liver LD accumulation. Increased LDs are associated with disease severity and liver injury. Moreover, inhibition of LD accumulation decreased the production of inflammatory mediators and lipid peroxidation while improving tissue function, suggesting that LDs contribute to the pathogenesis of liver injury triggered by sepsis. Impact and Implications: Sepsis is a complex life-threatening syndrome caused by dysregulated inflammatory and metabolic host responses to infection. The observation that lipid droplets may contribute to sepsis-associated organ injury by amplifying lipid peroxidation and inflammation provides a rationale for therapeutically targeting lipid droplets and lipid metabolism in sepsis.

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.

Impact of the immune profiles of hypertensive patients with and without obesity on COVID-19 severity.

BACKGROUND: Comorbidities such as obesity, hypertension, and diabetes are associated with COVID-19 development and severity, probably due to immune dysregulation; however, the mechanisms underlying these associations are not clear. The immune signatures of hypertensive patients with obesity with COVID-19 may provide new insight into the mechanisms of immune dysregulation and progression to severe disease in these patients. METHODS: Hypertensive patients were selected prospectively from a multicenter registry of adults hospitalized with COVID-19 and stratified according to obesity (BMI ≥ 30 kg/m²). Clinical data including baseline characteristics, complications, treatment, and 46 immune markers were compared between groups. Logistic regression was performed to identify variables associated with the risk of COVID-19 progression in each group. RESULTS: The sample comprised 213 patients (89 with and 124 without obesity). The clinical profiles of patients with and without obesity differed, suggesting potential interactions with COVID-19 severity. Relative to patients without obesity, patients with obesity were younger and fewer had cardiac disease and myocardial injury. Patients with obesity had higher EGF, GCSF, GMCSF, interleukin (IL)-1ra, IL-5, IL-7, IL-8, IL-15, IL-1ß, MCP 1, and VEGF levels, total lymphocyte counts, and CD8+ CD38+ mean fluorescence intensity (MFI), and lower NK-NKG2A MFI and percentage of CD8+ CD38+ T cells. Significant correlations between cytokine and immune cell expression were observed in both groups. Five variables best predicted progression to severe COVID-19 in patients with obesity: diabetes, the EGF, IL-10, and IL-13 levels, and the percentage of CD8+ HLA-DR+ CD38+ cells. Three variables were predictive for patients without obesity: myocardial injury and the percentages of B lymphocytes and HLA-DR+ CD38+ cells. CONCLUSION: Our findings suggest that clinical and immune variables and obesity interact synergistically to increase the COVID-19 progression risk. The immune signatures of hypertensive patients with and without obesity severe COVID-19 highlight differences in immune dysregulation mechanisms, with potential therapeutic applications.

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.

Clinical features, etiologies, and outcomes of central nervous system infections in intensive care: A multicentric retrospective study in a large Brazilian metropolitan area.

PURPOSE: The goal of this study was to investigate severe central nervous system infections (CNSI) in adults admitted to the intensive care unit (ICU). We analyzed the clinical presentation, causes, and outcomes of these infections, while also identifying factors linked to higher in-hospital mortality rates. MATERIALS AND METHODS: We conducted a retrospective multicenter study in Rio de Janeiro, Brazil, from 2012 to 2019. Using a prediction tool, we selected ICU patients suspected of having CNSI and reviewed their medical records. Multivariate analyses identified variables associated with in-hospital mortality. RESULTS: In a cohort of 451 CNSI patients, 69 (15.3%) died after a median 11-day hospitalization (5-25 IQR). The distribution of cases was as follows: 29 (6.4%) had brain abscess, 161 (35.7%) had encephalitis, and 261 (57.8%) had meningitis. Characteristics: median age 41 years (27-53 IQR), 260 (58%) male, and 77 (17%) HIV positive. The independent mortality predictors for encephalitis were AIDS (OR = 4.3, p = 0.01), ECOG functional capacity limitation (OR = 4.0, p < 0.01), ICU admission from ward (OR = 4.0, p < 0.01), mechanical ventilation ≥10 days (OR = 6.1, p = 0.04), SAPS 3 ≥ 55 points (OR = 3.2, p = 0.02). Meningitis: Age > 60 years (OR = 234.2, p = 0.04), delay >3 days for treatment (OR = 2.9, p = 0.04), mechanical ventilation ≥10 days (OR = 254.3, p = 0.04), SOFA >3 points (OR = 2.7, p = 0.03). Brain abscess: No associated factors found in multivariate regression. CONCLUSIONS: Patients' overall health, prompt treatment, infection severity, and prolonged respiratory support in the ICU all significantly affect in-hospital mortality rates. Additionally, the implementation of CNSI surveillance with the used prediction tool could enhance public health policies.

Association of Lack of Fear of Dying With New Organ Failure: Results of a Multicenter Prospective Cohort Study.

BACKGROUND: Patients' anxiety on intensive care unit (ICU) admission is associated with subsequent deterioration. OBJECTIVE: To assess whether patients' fears/anxiety are predictive of new organ failure within 7 days of ICU admission. METHODS: In a prospective 3-center cohort study of non-comatose patients without delirium or invasive mechanical ventilation, 9 specific fears were evaluated through yes/no questions. Illness severity was assessed using the Simplified Acute Physiology Score II (SAPS II) and the Sequential Organ Failure Assessment (SOFA). Intensity of acute and chronic anxiety was assessed with the state and trait components of the State-Trait Anxiety Inventory (STAI). Patients were followed up for 7 days. RESULTS: From April 2014 to December 2017, 373 patients (median [IQR] age, 63 [48-74] years; 152 [40.8%] women; median (IQR) SAPS II, 27 [19-37]) were included. Feelings of vulnerability and fear of dying were reported by 203 (54.4%) and 172 (46.1%) patients, respectively. The STAI-State score was 40 or greater in 192 patients (51.5%). Ninety-four patients (25.2%) had new organ failure. Feelings of vulnerability (odds ratio, 1.96 [95% CI, 1.12-3.43]; P=.02) and absence of fear of dying (odds ratio, 2.38 [95% CI, 1.37-4.17]; P=.002) were associated with new organ failure after adjustment for STAI-State score (≥40), SAPS II, and SOFA score. CONCLUSION: Absence of fear of dying is associated with new organ failure within the first 7 days after ICU admission. Fear of dying may protect against subsequent deterioration by mobilizing patients' homeostatic resources. ClinicalTrials.gov Identifier: NCT02355626.

Evidence-Based Checklist to Delay Cardiac Arrest in Brain-Dead Potential Organ Donors: The DONORS Cluster Randomized Clinical Trial.

Importance: The effectiveness of goal-directed care to reduce loss of brain-dead potential donors to cardiac arrest is unclear. Objective: To evaluate the effectiveness of an evidence-based, goal-directed checklist in the clinical management of brain-dead potential donors in the intensive care unit (ICU). Design, Setting, and Participants: The Donation Network to Optimize Organ Recovery Study (DONORS) was an open-label, parallel-group cluster randomized clinical trial in Brazil. Enrollment and follow-up were conducted from June 20, 2017, to November 30, 2019. Hospital ICUs that reported 10 or more brain deaths in the previous 2 years were included. Consecutive brain-dead potential donors in the ICU aged 14 to 90 years with a condition consistent with brain death after the first clinical examination were enrolled. Participants were randomized to either the intervention group or the control group. The intention-to-treat data analysis was conducted from June 15 to August 30, 2020. Interventions: Hospital staff in the intervention group were instructed to administer to brain-dead potential donors in the intervention group an evidence-based checklist with 13 clinical goals and 14 corresponding actions to guide care, every 6 hours, from study enrollment to organ retrieval. The control group provided or received usual care. Main Outcomes and Measures: The primary outcome was loss of brain-dead potential donors to cardiac arrest at the individual level. A prespecified sensitivity analysis assessed the effect of adherence to the checklist in the intervention group. Results: Among the 1771 brain-dead potential donors screened in 63 hospitals, 1535 were included. These patients included 673 males (59.2%) and had a median (IQR) age of 51 (36.3-62.0) years. The main cause of brain injury was stroke (877 [57.1%]), followed by trauma (485 [31.6%]). Of the 63 hospitals, 31 (49.2%) were assigned to the intervention group (743 [48.4%] brain-dead potential donors) and 32 (50.8%) to the control group (792 [51.6%] brain-dead potential donors). Seventy potential donors (9.4%) at intervention hospitals and 117 (14.8%) at control hospitals met the primary outcome (risk ratio [RR], 0.70; 95% CI, 0.46-1.08; P = .11). The primary outcome rate was lower in those with adherence higher than 79.0% than in the control group (5.3% vs 14.8%; RR, 0.41; 95% CI, 0.22-0.78; P = .006). Conclusions and Relevance: This cluster randomized clinical trial was inconclusive in determining whether the overall use of an evidence-based, goal-directed checklist reduced brain-dead potential donor loss to cardiac arrest. The findings suggest that use of such a checklist has limited effectiveness without adherence to the actions recommended in this checklist. Trial Registration: ClinicalTrials.gov Identifier: NCT03179020.