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BMC Infect Dis ; 20(1): 536, 2020 Jul 23.
Artigo em Inglês | MEDLINE | ID: mdl-32703188

RESUMO

BACKGROUND: As the COVID-19 pandemic continues to spread, early, ideally real-time, identification of SARS-CoV-2 infected individuals is pivotal in interrupting infection chains. Volatile organic compounds produced during respiratory infections can cause specific scent imprints, which can be detected by trained dogs with a high rate of precision. METHODS: Eight detection dogs were trained for 1 week to detect saliva or tracheobronchial secretions of SARS-CoV-2 infected patients in a randomised, double-blinded and controlled study. RESULTS: The dogs were able to discriminate between samples of infected (positive) and non-infected (negative) individuals with average diagnostic sensitivity of 82.63% (95% confidence interval [CI]: 82.02-83.24%) and specificity of 96.35% (95% CI: 96.31-96.39%). During the presentation of 1012 randomised samples, the dogs achieved an overall average detection rate of 94% (±3.4%) with 157 correct indications of positive, 792 correct rejections of negative, 33 incorrect indications of negative or incorrect rejections of 30 positive sample presentations. CONCLUSIONS: These preliminary findings indicate that trained detection dogs can identify respiratory secretion samples from hospitalised and clinically diseased SARS-CoV-2 infected individuals by discriminating between samples from SARS-CoV-2 infected patients and negative controls. This data may form the basis for the reliable screening method of SARS-CoV-2 infected people.


Assuntos
Betacoronavirus/isolamento & purificação , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/virologia , Programas de Rastreamento/métodos , Odorantes/análise , Pneumonia Viral/diagnóstico , Pneumonia Viral/virologia , Animais , Brônquios/química , Brônquios/virologia , COVID-19 , Estudos de Casos e Controles , Cães , Método Duplo-Cego , Humanos , Pandemias/prevenção & controle , Projetos Piloto , SARS-CoV-2 , Saliva/química , Saliva/virologia , Sensibilidade e Especificidade
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