Cross-cultural adaptation and assessment of reproducibility of the Duke Activity Status Index for COPD patients in Brazil

OBJECTIVE: To cross-culturally adapt the Duke Activity Status Index (DASI) for use in Brazil and evaluate the reproducibility of the new (Brazilian Portuguese-language) version. METHODS: We selected stable patients with clinical and spirometric diagnosis of COPD. Initially, the DASI was translated into Brazilian Portuguese, and the cross-cultural adaptation was performed by an expert committee. Subsequently, 12 patients completed the questionnaire, so that their questions and difficulties could be identified and adjustments could be made. An independent translator back-translated the final version into English, which was then submitted to and approved by the original author. The final Brazilian Portuguese-language version of the DASI was applied to 50 patients at three distinct times. For the assessment of interobserver reproducibility, it was applied twice within a 30-min interval by two different interviewers. For the assessment of intraobserver reproducibility, it was applied again 15 days later by one of the interviewers. RESULTS: The mean age of the patients was 62.3 ± 10.0 years, the mean FEV1 was 45.2 ± 14.7% of the predicted value, and the mean body mass index was 26.8 ± 5.8 kg/m². The intraclass correlation coefficients for intraobserver and interobserver reproducibility were 0.95 and 0.90, respectively. The correlations between the DASI and the Saint George's Respiratory Questionnaire (SGRQ) domains were all negative and statistically significant. The DASI correlated best with the SGRQ activity domain (r = -0.70), the total SGRQ score (r = -0.66), and the six-minute walk distance (r = 0.55). CONCLUSIONS: The Brazilian Portuguese-language version of the DASI is reproducible, fast, and simple, correlating well with the SGRQ.OBJETIVO: Adaptar culturalmente e avaliar a reprodutibilidade do Duke Activity Status Index (DASI) para o português do Brasil. MÉTODOS: Foram selecionados pacientes estáveis com diagnóstico clínico e espirométrico de DPOC. Inicialmente, o DASI foi traduzido para o português, e a adaptação cultural foi realizada por uma comissão de especialistas. Em seguida, o questionário foi aplicado em 12 pacientes para saber suas dúvidas e dificuldades, sendo realizadas as devidas adaptações. Um tradutor independente fez a tradução retrógrada, que foi submetida e aprovada pelo autor original. A versão final do DASI foi aplicada em 50 pacientes em dois momentos, com intervalo de 30 minutos (reprodutibilidade interobservador) e, num terceiro momento, após 15 dias (reprodutibilidade intraobservador). RESULTADOS: A média de idade dos pacientes foi de 62,3 ± 10,0 anos, a média do VEF1 foi de 45,2 ± 14,7% do valor previsto, e a do índice de massa corpórea foi de 26,8 ± 5,8 kg/m². Os coeficientes de correlação intraclasse intraobservador e interobservador foram de 0,95 e 0,90, respectivamente. As correlações do DASI com todos os domínios do Saint George's Respiratory Questionnaire (SGRQ) foram negativas e estatisticamente significantes. As melhores correlações ocorreram com o domínio atividade (r = -0,70) e a pontuação total do SGRQ (r = -0,66), assim como com a distância percorrida no teste de caminhada de seis minutos (r = 0,55). CONCLUSÕES: A versão em língua portuguesa do Brasil do DASI é reprodutível, de rápida e fácil aplicação e apresentou uma boa correlação com o SGRQ.Secretaria Estadual de Saúde Secretaria Municipal de SaúdeUniversidade Federal de Sergipe Hospital Universitário Serviço de PneumologiaUniversidade Federal de São Paulo (UNIFESP) Centro de Reabilitação PulmonarAssociação de Assistência à Criança DeficienteUniversidade Estadual de Ciências da Saúde de Alagoas Núcleo de Propedêutica e TerapêuticaStanford University School of MedicineUNIFESP, Centro de Reabilitação PulmonarSciEL

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

Geoeconomic variations in epidemiology, ventilation management, and outcomes in invasively ventilated intensive care unit patients without acute respiratory distress syndrome: a pooled analysis of four observational studies

Background: Geoeconomic variations in epidemiology, the practice of ventilation, and outcome in invasively ventilated intensive care unit (ICU) patients without acute respiratory distress syndrome (ARDS) remain unexplored. In this analysis we aim to address these gaps using individual patient data of four large observational studies. Methods: In this pooled analysis we harmonised individual patient data from the ERICC, LUNG SAFE, PRoVENT, and PRoVENT-iMiC prospective observational studies, which were conducted from June, 2011, to December, 2018, in 534 ICUs in 54 countries. We used the 2016 World Bank classification to define two geoeconomic regions: middle-income countries (MICs) and high-income countries (HICs). ARDS was defined according to the Berlin criteria. Descriptive statistics were used to compare patients in MICs versus HICs. The primary outcome was the use of low tidal volume ventilation (LTVV) for the first 3 days of mechanical ventilation. Secondary outcomes were key ventilation parameters (tidal volume size, positive end-expiratory pressure, fraction of inspired oxygen, peak pressure, plateau pressure, driving pressure, and respiratory rate), patient characteristics, the risk for and actual development of acute respiratory distress syndrome after the first day of ventilation, duration of ventilation, ICU length of stay, and ICU mortality. Findings: Of the 7608 patients included in the original studies, this analysis included 3852 patients without ARDS, of whom 2345 were from MICs and 1507 were from HICs. Patients in MICs were younger, shorter and with a slightly lower body-mass index, more often had diabetes and active cancer, but less often chronic obstructive pulmonary disease and heart failure than patients from HICs. Sequential organ failure assessment scores were similar in MICs and HICs. Use of LTVV in MICs and HICs was comparable (42·4% vs 44·2%; absolute difference -1·69 [-9·58 to 6·11] p=0·67; data available in 3174 [82%] of 3852 patients). The median applied positive end expiratory pressure was lower in MICs than in HICs (5 [IQR 5-8] vs 6 [5-8] cm H2O; p=0·0011). ICU mortality was higher in MICs than in HICs (30·5% vs 19·9%; p=0·0004; adjusted effect 16·41% [95% CI 9·52-23·52]; p<0·0001) and was inversely associated with gross domestic product (adjusted odds ratio for a US$10 000 increase per capita 0·80 [95% CI 0·75-0·86]; p<0·0001). Interpretation: Despite similar disease severity and ventilation management, ICU mortality in patients without ARDS is higher in MICs than in HICs, with a strong association with country-level economic status