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Author(s): Jorge I. F. Salluh [sup.1] [sup.2], Leticia Kawano-Dourado [sup.3] [sup.4] [sup.5] Author Affiliations: (1) https://ror.org/01mar7r17, grid.472984.4, Department of Critical Care and Postgraduate Program in Translational Medicine, D'Or Institute for [...]

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.sup.th and October 28.sup.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., Author(s): Pedro Kurtz [sup.1] [sup.2] [sup.3], Leonardo S. L. Bastos [sup.4], Leila F. Dantas [sup.4], Fernando G. Zampieri [sup.1] [sup.5] [sup.6], Marcio Soares [sup.1], Silvio Hamacher [sup.4], Jorge I. F. [...]

Purpose To describe trends in outcomes of cancer patients with unplanned admissions to intensive-care units (ICU) according to cancer type, organ support use, and performance status (PS) over an 8-year period. Methods We retrospectively analyzed prospectively collected data from all cancer patients admitted to 92 medical-surgical ICUs from July/2011 to June/2019. We assessed trends in mortality through a Bayesian hierarchical model adjusted for relevant clinical confounders and whether there was a reduction in ICU length-of-stay (LOS) over time using a competing risk model. Results 32,096 patients (8.7% of all ICU admissions; solid tumors, 90%; hematological malignancies, 10%) were studied. Bed/days use by cancer patients increased up to more than 30% during the period. Overall adjusted mortality decreased by 9.2% [95% credible interval (CI), 13.1-5.6%]. The largest reductions in mortality occurred in patients without need for organ support (9.6%) and in those with need for mechanical ventilation (MV) only (11%). Smallest reductions occurred in patients requiring MV, vasopressors, and dialysis (3.9%) simultaneously. Survival gains over time decreased as PS worsened. Lung cancer patients had the lowest decrease in mortality. Each year was associated with a lower sub-hazard for ICU death [SHR 0.93 (0.91-0.94)] and a higher chance of being discharged alive from the ICU earlier [SHR 1.01 (1-1.01)]. Conclusion Outcomes in critically ill cancer patients improved in the past 8 years, with reductions in both mortality and ICU LOS, suggesting improvements in overall care. However, outcomes remained poor in patients with lung cancer, requiring multiple organ support and compromised PS., Author(s): Fernando G. Zampieri [sup.1] [sup.2] [sup.3], Thiago G. Romano [sup.4] [sup.5] [sup.9], Jorge I. F. Salluh [sup.6], Leandro U. Taniguchi [sup.7] [sup.8], Pedro V. Mendes [sup.4] [sup.8] [sup.9], Antonio [...]

Author(s): Jorge I. F. Salluh [sup.1] [sup.2], Gaston Burghi [sup.3], Rashan Haniffa [sup.4] [sup.5] [sup.6] Author Affiliations: (1) grid.472984.4, Department of Critical Care and Postgraduate Program in Translational Medicine, D'Or [...]

Author(s): Pedro Kurtz [sup.1] [sup.2] [sup.3], Leonardo S. L. Bastos [sup.4], Jorge I. F. Salluh [sup.1] [sup.5], Fernando A. Bozza [sup.1] [sup.6], Marcio Soares [sup.1] Author Affiliations: (1) grid.472984.4, D'Or [...]

Purpose To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning. Methods The following variables were included in the analysis: average bed to nurse, physiotherapist and physician ratios, presence of 24/7 board-certified intensivists and dedicated pharmacists in the ICU, and nurse and physiotherapist autonomy scores. Clusters were defined using the partition around medoids method. We assessed the association between clusters and hospital mortality using logistic regression and with ICU LOS and MV duration using competing risk regression. Results Analysis included data from 129,680 patients admitted to 93 ICUs (2014-2015). Three clusters were identified. The features distinguishing between the clusters were: the presence of board-certified intensivists in the ICU 24/7 (present in Cluster 3), dedicated pharmacists (present in Clusters 2 and 3) and the extent of nurse autonomy (which increased from Clusters 1 to 3). The patients in Cluster 3 exhibited the best outcomes, with lower adjusted hospital mortality [odds ratio 0.92 (95% confidence interval (CI), 0.87-0.98)], shorter ICU LOS [subhazard ratio (SHR) for patients surviving to ICU discharge 1.24 (95% CI 1.22-1.26)] and shorter durations of MV [SHR for undergoing extubation 1.61(95% CI 1.54-1.69)]. Cluster 1 had the worst outcomes. Conclusion Patients treated in ICUs combining 24/7 expert intensivist coverage, a dedicated pharmacist and nurses with greater autonomy had the best outcomes. All of these features represent achievable targets that should be considered by policy makers with an interest in promoting equal and optimal ICU care., Author(s): Fernando G. Zampieri [sup.1] [sup.2], Jorge I. F. Salluh [sup.1] [sup.3], Luciano C. P. Azevedo [sup.4], Jeremy M. Kahn [sup.5] [sup.6], Lucas P. Damiani [sup.2], Lunna P. Borges [sup.3], [...]

Author(s): Jorge I. F. Salluh [sup.1] [sup.2], Nicola Latronico [sup.3] [sup.4] Author Affiliations: (1) grid.472984.4, D'Or Institute for Research and Education, , Rua Diniz Cordeiro, 30, Botafogo, 22281-100, Rio de [...]

Author(s): Jorge I. F. Salluh [sup.1] [sup.2], Marcio Soares [sup.1] [sup.2], Mark T. Keegan [sup.3] Author Affiliations: (1) grid.472984.4, Department of Critical Care and Graduate Program in Translational Medicine, D'Or [...]

Author(s): Yaseen M. Arabi [sup.1] [sup.2], Marcus J. Schultz [sup.3] [sup.4], Jorge I. F. Salluh [sup.5] [sup.6] Author Affiliations: (1) 0000 0004 0608 0662, grid.412149.b, Department of Intensive Care, King [...]

Author(s): Pieter O. Depuydt [sup.1], John P. Kress [sup.2], Jorge I. F. Salluh [sup.3] [sup.4] [sup.5] Author Affiliations: (1) grid.5342.0, 0000000120697798, Department of Intensive Care, Ghent University Hospital and Heymans [...]