Artículo

Practical diagnostic algorithms for Chagas disease: a focus on low resource settings

Gabaldón Figueira, Juan Carlos ; Skjefte, Malia ; Longhi, Silvia Andrea Icon; Escabia, Elisa ; García Casares, Lady Juliette Icon; Ros Lucas, Albert ; Martínez Peinado, Nieves ; Muñoz Calderon, Arturo Alejandro Icon; Gascón, Joaquim ; Schijman, Alejandro Gabriel Icon; Alonso Padilla, Julio Icon

Otros responsables

Fecha

2023-11-18

Editorial

Taylor & Francis Ltd

Revista

Expert Review of Anti-Infective Therapy

Título del libro

Nombre del evento

Fecha del evento

Institución organizadora

ISSN

1478-7210

eISSN

1744-8336

ISBN

Idioma

Inglés

Tipo del evento

Clasificación temática

Resumen

Introduction: Chagas disease, caused by parasite Trypanosoma cruzi, is the most important neglected tropical disease in the Americas. Two drugs are available for treatment, but access to them is challenging, in part due to complex diagnostic algorithms. These are stage-dependent, involve multiple tests, and are ill-adapted to the reality of vast areas where the disease is endemic. Molecular and serologic tools are used to detect acute and chronic infections, with the performance of the latter showing geographic differences. Breakthroughs in the development of new diagnostic tools include the validation of a loop-mediated isothermal amplification assay for acute infections (T. cruzi-LAMP), and the regional validation of several rapid diagnostic tests (RDTs) for chronic infection, which simplify testing in resource-limited settings. The literature search was carried out in the MEDLINE database until 1 August 2023.Areas covered: This review outlines existing algorithms, and proposes new ones focused on point-of-care testing.Expert opinion: Integrating point-of-care testing into existing diagnostic algorithms in certain endemic areas will increase access to timely diagnosis and treatment. However, additional research is needed to validate the use of these techniques across a wider geography, and to better understand the cost-effectiveness of their large-scale implementation.

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