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

dc.contributor.authorGabaldón Figueira, Juan Carlos
dc.contributor.authorSkjefte, Malia
dc.contributor.authorLonghi, Silvia
dc.contributor.authorEscabia, Elisa
dc.contributor.authorGarcía, Lady Juliette
dc.contributor.authorRos Lucas, Albert
dc.contributor.authorMartinez-Peinado, Nieves
dc.contributor.authorMuñoz Calderón, Arturo
dc.contributor.authorGascón i Brustenga, Joaquim
dc.contributor.authorSchijman, Alejandro G.
dc.contributor.authorAlonso Padilla, Julio
dc.date.accessioned2024-09-04T12:42:01Z
dc.date.available2024-09-04T12:42:01Z
dc.date.issued2023-07
dc.date.updated2024-09-04T12:42:01Z
dc.description.abstractIntroduction: 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.
dc.format.extent32 p.
dc.format.mimetypeapplication/pdf
dc.identifier.idgrec746305
dc.identifier.issn1478-7210
dc.identifier.pmidPMID: 37933443
dc.identifier.urihttps://hdl.handle.net/2445/214991
dc.language.isoeng
dc.publisherTaylor & Francis
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1080/14787210.2023.2279110
dc.relation.ispartofExpert Review of Anti-infective Therapy, 2023, vol. 21, num.12, p. 1287-1299
dc.relation.urihttps://doi.org/10.1080/14787210.2023.2279110
dc.rights(c) Taylor & Francis, 2023
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.sourceArticles publicats en revistes (Biologia, Sanitat i Medi Ambient)
dc.subject.classificationEpidemiologia
dc.subject.classificationAlgorismes
dc.subject.classificationMalaltia de Chagas
dc.subject.classificationDiagnòstic
dc.subject.otherEpidemiology
dc.subject.otherAlgorithms
dc.subject.otherChagas' disease
dc.subject.otherDiagnosis
dc.titlePractical diagnostic algorithms for Chagas disease: a focus on low resource settings
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion

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