The impact of the incorporation of a feasible postoperative mortality model at the Post-Anaesthestic Care Unit (PACU) on postoperative clinical deterioration : a pragmatic trial with 5,353 patients

Background: Practical use of risk predictive tools and the assessment of their impact on outcome reduction is still a challenge. This pragmatic study of quality improvement (QI) describes the preoperative adoption of a customised postoperative death probability model (SAMPE model) and the evaluation of the impact of a Postoperative Anaesthetic Care Unit (PACU) pathway on the clinical deterioration of high-risk surgical patients. Methods: A prospective cohort of 2,533 surgical patients compared with 2,820 historical controls after the adoption of a quality improvement (QI) intervention. We carried out quick postoperative high-risk pathways at PACU when the probability of postoperative death exceeded 5%. As outcome measures, we used the number of rapid response team (RRT) calls within 7 and 30 postoperative days, in-hospital mortality, and non-planned Intensive Care Unit (ICU) admission. Results: Not only did the QI succeed in the implementation of a customised risk stratification model, but it also diminished the postoperative deterioration evaluated by RRT calls on very high-risk patients within 30 postoperative days (from 23% before to 14% after the intervention, p = 0.05). We achieved no survival benefits or reduction of non-planned ICU. The small group of high-risk patients (13% of the total) accounted for the highest proportion of RRT calls and postoperative death. Conclusion: Employing a risk predictive tool to guide immediate postoperative care may influence postoperative deterioration. It encouraged the design of pragmatic trials focused on feasible, low-technology, and long-term interventions that can be adapted to diverse health systems, especially those that demand more accurate decision making and ask for full engagement in the control of postoperative morbi-mortality

Measuring emotional preoperative stress by an app approach and its applicability to predict postoperative pain

Background The Brief Measure of Emotional Preoperative Stress (B-MEPS) was developed to evaluate the preoperative individual vulnerability to emotional stress. To obtain a refined version of B-MEPS suitable for an app approach, this study aimed: (i) to identify items with more discriminant properties; (ii) to classify the level of preoperative emotional stress based on cut-off points; (iii) to assess concurrent validity through correlation with the Central Sensitization Inventory (CSI) score; (iv) to confirm whether the refined version of B-MEPS is an adequate predictive measure for identification of patients prone to intense postoperative pain. Methods We include 1016 patients who had undergone surgical procedures in a teaching hospital. The generalized partial credit model of item response theory and latent class model were employed, respectively, to reduce the number of items and to create cut-off points. We applied the CSI and assessed pain by Visual Analog Scale (0–10) and by the amount of postoperative morphine consumption. Results The refined B-MEPS shows satisfactory reliability (Cronbach’s alpha 0.79). Preoperative emotional stress, according to the cut-off points, is classified into categories: low, intermediate or high stress. The refined B-MEPS exhibited a linear association with the CSI scores (r2 = 0.53, p < 0.01). Patients with higher levels of emotional stress displayed a positive association with moderate to severe pain and greater morphine consumption. Conclusion The refined version of B-MEPS, along with an interface of easy applicability, assess emotional vulnerability at the bedside before surgery. This app may support studies focused on intervening with perioperative stress levels

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

Fatores de risco para complicações respiratórias perioperatórias em crianças menores de dezesseis anos submetidas a procedimentos não cardíacos

Introdução: Crianças submetidas a procedimentos cirúrgicos estão sob risco de desenvolver complicações, e, dentre elas, as respiratórias são as mais prevalentes. O reconhecimento de fatores de risco independentes para complicações respiratórias pode auxiliar na tomada e no compartilhamento de decisões, assim como embasar possíveis intervenções para otimizar os desfechos nessa população. Objetivos: O presente trabalho tem como objetivo principal determinar os fatores de risco para complicações respiratórias no perioperatório de pacientes menores de 16 anos submetidos a procedimentos não cardíacos em dois hospitais terciários do sul do Brasil. Métodos: Foi realizada uma coorte prospectiva com 1616 crianças menores de 16 anos submetidas a procedimentos não cardíacos no Hospital de Clínicas de Porto Alegre e Hospital da Criança Conceição. Os participantes do estudo foram observados desde a avaliação pré-anestésica até 2 horas após o procedimento para avaliar a ocorrência de alguma complicação respiratória perioperatória (CRPO). Resultados: Foram incluídos no estudo 1616 pacientes, desses, 353 (21,8%) possuíam menos de 1 ano e 352 (21,8%) possuíam doença pulmonar ou de vias aéreas. 994 (61,5%) foram submetidas à intubação endotraqueal e 81 (5%) estavam resfriados no momento da cirurgia e 179 (11,1%) tiveram episódio de resfriado nas 6 semanas antecedentes à cirurgia. A incidência de CRPO foi de 15,3%, sendo a dessaturação de oxigênio a CRPO mais frequente. Verificou-se que doença pulmonar ou de VA (OR 2,02; IC 1,47-2,79), resfriado no momento da cirurgia (OR 2,90; IC 1,69-5,0), prematuridade (OR 2,52; IC 1,83-3,48), cirurgia envolvendo VA (OR 1,50; IC 1,05-2,15), intubação endotraqueal para realizar o procedimento (OR 1,81; IC 1,29-2,53) e idade menor que 1 ano (OR 2,23; IC 1,57-3,16) foram preditores independentes para CRPO. Conclusão: Observamos diferentes fatores de risco relacionados ao paciente, cirurgia e anestesia que implicam em CRPO na população pediátrica. O conhecimento desses fatores na nossa população auxilia na tomada de decisão e/ou na implementação de medidas preventivas para aumentar a segurança dos pacientes cirúrgicos pediátricos. Modelos de risco com base nesses fatores estão sendo construídos para compor o processo decisório de realizar ou não uma cirurgia.Introduction: Children undergoing surgical procedures are at risk of developing complications, and, among them, respiratory complications are the most prevalent. Determining the risk factors that independently increase the chances of pulmonary complications can help in making and sharing decisions, as well as providing a basis for possible interventions to optimize outcomes in this population. Objectives: The main objective of this study is to determine the risk factors for perioperative respiratory complications in patients younger than 16 years undergoing non-cardiac procedures in two tertiary hospitals in southern Brazil. Methods: A prospective cohort was carried out with 1616 children under 16 years of age who underwent non-cardiac procedures at Hospital de Clínicas de Porto Alegre and Hospital da Criança Conceição. Study participants were observed from the pre-anesthetic evaluation until 2 hours after the procedure to assess the occurrence of any perioperative respiratory complications (PORS). Results: A total of 1616 patients were included in the study, of which 353 (21.8%) were less than 1 year old and 352 (21.8%) had lung or airway disease. 994 (61.5%) underwent endotracheal intubation and 81 (5%) had a cold at the time of surgery and 179 (11.1%) had a cold episode in the 6 weeks prior to surgery. The incidence of PORC was 15.3%, with oxygen desaturation being the most frequent PORC. It was found that pulmonary or AV disease (OR 2.02; CI 1.47-2.79), cold at the time of surgery (OR 2.90; CI 1.69-5.0), prematurity (OR 2 .52; CI 1.83-3.48), surgery involving VA (OR 1.50; CI 1.05- 2.15), endotracheal intubation to perform the procedure (OR 1.81; CI 1.29-2 .53) and age less than 1 year (OR 2.23; CI 1.57-3.16) were independent predictors of PORC. Conclusion: We observed different risk factors related to patient, surgery and anesthesia that implicate PORC in the pediatric population. Knowing these factors in our population helps in decision-making and/or in the implementation of preventive measures to increase the safety of pediatric surgical patients. Risk models based on these factors are being built to compose the decision-making process of whether or not to perform surgery