Análise crítica da reconstrução da raiz da aorta com a preservação da valva aórtica: 11 anos de seguimento

INTRODUCTION: The composite mechanical valve conduit replacement is the standardized operation for aneurysms of the aortic root. The objective of this study is to evaluate the long-term surgical results of aortic valve-preserving procedures to the root reconstruction. METHODS: From 1996 to 2008, 54 consecutive patients underwent two different techniques of valve-sparing aortic root operation (40 Yacoub operations and 14 David operations). Mean age was 48 ± 14 years (range 17 to 74). 36 patients (66.7%) were male and 16 (29.6%) experienced Marfan's syndrome. The mean Euroscore was 4 ± 1.25. The mean follow up time was 4.1 years (from 49 days to 10.9 years). Clinical and echocardiographic parameters were analysed. T-Student paired test, the McNemar Non Parametric test and the Kaplan-Meyer Outcome Curves have been used. RESULTS: The hospital mortality was 5.6% and the average hospitalization time was 9±4 days. One non related late death (2%) was reported. The actuarial survival and freedom from reoperation were respectively 94.4% and 96% within 11 years of follow-up. There were benefits in reduction of functional class (P=0.002; 78% CF I), in reduction of aortic regurgitation (P<0.001; 78% with or without discrete reflux), in reduction of systolic and diastolic diameters, end-sytolic and end-diastolic volumes of left ventricle (respectively P=0.004; P<0.0001; P=0.036 and P<0.001). Two (3.9%) patients required aortic valve replacement due to severe aortic regurgitation during this same period. No thromboembolic, endocarditis or bleeding events were reported during the follow-up. CONCLUSION: The valve-sparing operation for aortic root aneurysms is an effective alternative to the use of a mechanical valve conduit replacement.INTRODUÇÃO: A utilização do tubo valvulado é a operação clássica para a reconstrução da raiz da aorta. O objetivo deste trabalho é avaliar a reconstrução da aorta ascendente com a preservação da valva aórtica. MÉTODOS: Entre 1996 e 2008, 54 pacientes consecutivos (66,7% do sexo masculino), com idade média de 48 ± 14 anos, foram submetidos à reconstrução da aorta ascendente e preservação da valva aórtica (40 remodelamentos e 14 reimplantes). O Euroscore médio foi de 4 ± 1,25 e 29,6% eram portadores de síndrome de Marfan. O tempo médio de seguimento foi de 4,1 anos (49 dias até 10,9 anos). Foram avaliados por parâmetros clínicos e ecocardiográficos. Para a análise dos dados foram utilizados os testes t de Student pareado, o não-paramétrico de McNemar e a curva de sobrevida de Kaplan-Meyer. RESULTADOS: A mortalidade hospitalar foi de 5,6%. O tempo médio de internação foi de 9 ± 4 dias. Houve um óbito tardio não relacionado (2%). A sobrevida e sobrevida livre de reoperação nos 11 anos de seguimento foram respectivamente de 94,4% e 96%. Houve melhora da classe funcional (P=0,002) (78% CF I), redução da insuficiência aórtica (P<0,001) (78% sem ou com refluxo discreto), redução dos diâmetros sistólico e diastólico, dos volumes sistólico final e diastólico final do ventrículo esquerdo, respectivamente P=0,004; P<0,001; P=0,036 e P<0,001. Dois pacientes foram submetidos à troca de valva aórtica (3,9%) com 4 e 10 anos da operação. Não foram observados fenômenos tromboembólicos, hemorrágicos ou endocardite durante o seguimento. CONCLUSÃO: A reconstrução da raiz da aorta com a preservação da valva aórtica é uma alternativa eficaz ao uso do tubo valvulado

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