Novo dispositivo de bolso para médicos pode detectar feridas infectadas mais rapidamente
É notoriamente difícil para os médicos identificar uma ferida que está infeccionando. Os sinais e sintomas clínicos são imprecisos e os métodos de identificação de bactérias podem ser demorados e inacessíveis, pelo que o diagnóstico pode ser...
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Domínio público
É notoriamente difícil para os médicos identificar uma ferida que está infeccionando. Os sinais e sintomas clínicos são imprecisos e os métodos de identificação de bactérias podem ser demorados e inacessíveis, pelo que o diagnóstico pode ser subjetivo e dependente da experiência do médico. Mas a infecção pode retardar a cura ou espalhar-se pelo corpo se não for tratada rapidamente, colocando a saúde do paciente em grave perigo. Uma equipa internacional de cientistas e médicos pensa ter a solução: um dispositivo executado a partir de uma aplicação para smartphone ou tablet, que permite imagens avançadas de uma ferida para identificar uma infecção.
“O tratamento de feridas é uma das ameaças mais caras e negligenciadas aos pacientes e ao nosso sistema de saúde em geral”, disse Robert Fraser, da Western University e da Swift Medical Inc., autor correspondente do estudo publicado na Frontiers in Medicine . “Os médicos precisam de melhores ferramentas e dados para melhor atender seus pacientes que sofrem desnecessariamente”.
Lançando luz sobre lesões
Os cientistas desenvolveram um dispositivo chamado Swift Ray 1, que pode ser acoplado a um smartphone e conectado ao software Swift Skin and Wound. Isso pode tirar fotografias de nível médico, imagens de termografia infravermelha (que medem o calor corporal) e imagens de fluorescência bacteriana (que revelam bactérias usando luz violeta).
Nenhuma dessas imagens seria suficiente para identificar a infecção por si só. A inspeção clínica tem baixa precisão, assim como a termografia que mede as mudanças de calor causadas por inflamação e infecção. A fluorescência bacteriana só pode observar a superfície de uma ferida, que está naturalmente contaminada com bactérias, portanto, são necessários métodos adicionais para diferenciar entre contaminação e uma ferida infectada.
“A pesquisa demonstrou que a imagem bacteriana ajuda a orientar o trabalho dos médicos para remover tecidos inviáveis, mas não consegue identificar a infecção por si só”, explicou o Dr. Jose Ramirez-Garcia Luna do Centro de Saúde da Universidade McGill, primeiro autor do estudo. "A termografia fornece informações sobre as alterações inflamatórias e circulatórias que ocorrem sob a pele."
Os cientistas procuraram combinar essas modalidades para chegar a um método que não precisasse de vários dispositivos caros, superasse as fraquezas de cada método de imagem e pudesse fornecer uma medida objetiva da cicatrização de feridas.
Para testar o dispositivo, eles recrutaram 66 pacientes feridos. Suas feridas não apresentavam sinais de propagação da infecção, não continham corpos estranhos e não haviam sido tratadas anteriormente com antibióticos ou fatores de crescimento. As feridas dos pacientes foram descobertas, limpas e secas antes da imagem e depois tratadas como de costume.
Uma imagem de saúde
As imagens foram revisadas por um pesquisador que não estava presente no processo de tratamento da ferida. Quatro padrões foram identificados.
As feridas onde a ferida não estava mais quente do que a pele saudável e não havia fluorescência bacteriana presente foram consideradas "não inflamadas", enquanto as feridas ligeiramente mais quentes do que a pele saudável e não tinham ou tinham pouca fluorescência bacteriana foram consideradas "inflamadas". Os dois últimos padrões – feridas substancialmente mais quentes, com ou sem fluorescência bacteriana – foram ambos designados como “infectados”, porque todos os médicos que examinaram estas feridas as consideraram infectadas.
Das 66 feridas, 20 foram consideradas não inflamadas, 26 estavam inflamadas e 20 estavam infectadas.
Os pesquisadores realizaram análises de componentes principais e usaram um algoritmo chamado agrupamento de k-vizinhos mais próximos para ver se um modelo de aprendizado de máquina poderia identificar com precisão essas diferentes categorias de feridas. Eles descobriram que o modelo poderia identificar todos os três muito bem, com uma precisão geral de 74%. Ao diferenciar entre feridas infectadas e não infectadas, o modelo identificou corretamente 100% das feridas infectadas e 91% das feridas não infectadas .
Uma nova ferramenta na caixa
Os pesquisadores ressaltaram que as imagens devem ser sempre consideradas no seu contexto médico. Por exemplo, uma ferida fria o suficiente para ser categorizada como não inflamada pode ter um suprimento sanguíneo limitado, comprometendo a cicatrização.
Mas como o Swift Ray 1 combinado com o software Swift Skin and Wound permite aos médicos combinar múltiplas modalidades de identificação de infecções, aumenta as ferramentas disponíveis sem exigir o uso de vários dispositivos caros. No futuro, poderá tornar possível garantir um diagnóstico rápido e preciso para cada paciente ferido e permitir avaliações de telemedicina mais eficazes.
“Este foi um estudo piloto e estudos de acompanhamento estão planejados”, advertiu Fraser. “No futuro, será necessário validar populações de pacientes com mais tipos de feridas”.
Mais informações: Minha ferida está infectada? Um estudo sobre o uso de imagens hiperespectrais para avaliar o status infeccioso de uma ferida, Frontiers in Medicine (2023). DOI: 10.3389/fmed.2023.1165281