Polifarmácia no idoso como causa de iatrogenia: revisão de literatura e relato de caso / Polypharmacy in the elderly as a cause of iatrogeny: literature review and case report

Introdução: a quantidade de idosos no mundo em 2050 chegará a marca de 2 bilhões de pessoas, representando cerca de 22% da população. Devido a esta nova realidade, países que já possuem uma população envelhecida, tem enfrentado problemas no acolhimento e atendimento de saúde, uma vez que o aumento da expectativa de vida, aumenta a chance de o indivíduo ser acometido por um número maior de doenças crônicas. Desta forma, o idoso fica mais sujeito a polifarmácia e a iatrogenia por ela causada, sendo a desprescrição uma medida necessária e que comprovadamente leva a melhora da qualidade de vida do paciente. Objetivo: realizar um estudo reflexivo com relato de caso e revisão de literatura, sobre os problemas gerados quando o idoso é submetido a polifarmácia, a exemplo da iatrogenia e os benefícios da desprescrição medicamentosa. Método: uma revisão bibliográfica, fez-se necessária para responder os questionamentos levantados pelo relato de caso. A revisão foi realizada em plataformas digitais da área médica, como: SciELO, MEDLINE, PubMed e LILACS, considerando revistas com qualis superior a B4 e/ou fator de impacto maior que 0,5, bem como livros e teses de mestrado e doutorado. Ainda, para a elaboração do relato de caso, foram utilizados dados obtidos em prontuários médicos, exames e entrevistas com a paciente e familiares. Relato de caso: idosa de 89 anos, grau III de dependência, foi levada ao hospital com quadro de desidratação e astenia profunda. Estava em uso de oxacarbamazepina, ácido acetilsalicílico, donepezila, metimazol, oxibutinina, nitrofurantoína, losartana e vitamina D. Durante a internação foi levantada a hipótese de iatrogenia medicamentosa. Após a alta, em atendimento ambulatorial os medicamentos foram suspensos e fez-se uma avaliação para otimizar a prescrição. Um mês após a nova conduta, a paciente encontrava-se mais lúcida, animada e menos dependente para as atividades de vida diária. Conclusão:  o caso relatado, trata-se de uma condição prevalente nos atendimentos de saúde, e por isso, infere-se ao médico a responsabilidade de sempre avaliar todo o tratamento ao qual o paciente está submetido, desprescrevendo quando necessário, na intenção de proporcionar melhor qualidade de vida ao paciente.

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