Early sedation and clinical outcomes of mechanically ventilated patients: a prospective multicenter cohort study

Introduction: Sedation overuse is frequent and possibly associated with poor outcomes in the intensive care unit (ICU) patients. However, the association of early oversedation with clinical outcomes has not been thoroughly evaluated. the aim of this study was to assess the association of early sedation strategies with outcomes of critically ill adult patients under mechanical ventilation (MV).Methods: A secondary analysis of a multicenter prospective cohort conducted in 45 Brazilian ICUs, including adult patients requiring ventilatory support and sedation in the first 48 hours of ICU admissions, was performed. Sedation depth was evaluated after 48 hours of MV. Multivariate analysis was used to identify variables associated with hospital mortality.Results: A total of 322 patients were evaluated. Overall, ICU and hospital mortality rates were 30.4% and 38.8%, respectively. Deep sedation was observed in 113 patients (35.1%). Longer duration of ventilatory support was observed (7 (4 to 10) versus 5 (3 to 9) days, P = 0.041) and more tracheostomies were performed in the deep sedation group (38.9% versus 22%, P=0.001) despite similar PaO2/FiO(2) ratios and acute respiratory distress syndrome (ARDS) severity. in a multivariate analysis, age (Odds Ratio (OR) 1.02; 95% confidence interval (CI) 1.00 to 1.03), Charlson Comorbidity Index >2 (OR 2.06; 95% Cl, 1.44 to 2.94), Simplified Acute Physiology Score 3 (SAPS 3) score (OR 1.02; Cl 95%, 1.00 to 1.04), severe ARDS (OR 1.44; Cl 95%, 1.09 to 1.91) and deep sedation (OR 2.36; Cl 9596, 1.31 to 4.25) were independently associated with increased hospital mortality.Conclusions: Early deep sedation is associated with adverse outcomes and constitutes an independent predictor of hospital mortality in mechanically ventilated patients.Research and Education Institute from Hospital Sirio-Libanes, São PauloD'Or Institute for Research and Education, Rio de Janeiro, BrazilBrazilian Research in Intensive Care NetworkHosp Copa DOr, BR-22031010 Rio de Janeiro, BrazilHosp Sirio Libanes, Res & Educ Inst, BR-01308060 São Paulo, BrazilUniv São Paulo, Fac Med, Hosp Clin, ICU,Emergency Med Dept, BR-05403000 São Paulo, BrazilHosp Sao Camilo Pompeia, ICU, BR-05022000 São Paulo, BrazilCEPETI, BR-82530200 Curitiba, Parana, BrazilHosp Canc I, Inst Nacl Canc, ICU, BR-20230130 Rio de Janeiro, BrazilPasteur Hosp, ICU, BR-20735040 Rio de Janeiro, BrazilIrmandade Santa Casa Misericordia Porto Alegre, RIPIMI, BR-90020090 Porto Alegre, RS, BrazilVitoria Apart Hosp, ICU, BR-29161900 Serra, ES, BrazilHosp Mater Dei, ICU, BR-30140093 Belo Horizonte, MG, BrazilHosp Santa Luzia, ICU, BR-70390902 Brasilia, DF, BrazilHosp Sao Luiz, ICU, BR-04544000 São Paulo, BrazilUniversidade Federal de São Paulo, Anesthesiol Pain & Intens Care Dept, ICU, BR-04024900 São Paulo, BrazilHosp Sao Jose Criciuma, ICU, BR-88801250 Criciuma, BrazilUDI Hosp, ICU, BR-65076820 Sao Luis, BrazilUniv São Paulo, Univ Hosp, ICU, BR-05508000 São Paulo, BrazilUniv São Paulo, Fac Med, Hosp Clin, ICU,Surg Emergency Dept, BR-05403000 São Paulo, BrazilIDOR DOr Inst Res & Educ, BR-22281100 Rio de Janeiro, BrazilInst Nacl Canc, Postgrad Program, BR-20230130 Rio de Janeiro, BrazilUniversidade Federal de São Paulo, Anesthesiol Pain & Intens Care Dept, ICU, BR-04024900 São Paulo, BrazilWeb of Scienc

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost