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The additional benefit of the ML Flow test to classify leprosy patients

  • Samira Bührer-Sékula
  • , Ximena Illarramendi
  • , Rose B. Teles
  • , Maria Lucia F. Penna
  • , José Augusto C. Nery
  • , Anna Maria Sales
  • , Linda Oskam
  • , Elizabeth P. Sampaio
  • , Euzenir N. Sarno
  • Royal Tropical Institute
  • Fundação Oswaldo Cruz
  • Universidade do Estado do Rio de Janeiro

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The use of the skin lesion counting classification leads to both under and over diagnosis of leprosy in many instances. Thus, there is a need to complement this classification with another simple and robust test for use in the field. Data of 202 untreated leprosy patients diagnosed at FIOCRUZ, Rio de Janeiro, Brazil, was analyzed. There were 90 patients classified as PB and 112 classified as MB according to the reference standard. The BI was positive in 111 (55%) patients and the ML Flow test in 116 (57.4%) patients. The ML Flow test was positive in 95 (86%) of the patients with a positive BI. The lesion counting classification was confirmed by both BI and ML Flow tests in 65% of the 92 patients with 5 or fewer lesions, and in 76% of the 110 patients with 6 or more lesions. The combination of skin lesion counting and the ML Flow test results yielded a sensitivity of 85% and a specificity of 87% for MB classification, and correctly classified 86% of the patients when compared to the standard reference. A considerable proportion of the patients (43.5%) with discordant test results in relation to standard classification was in reaction. The use of any classification system has limitations, especially those that oversimplify a complex disease such as leprosy. In the absence of an experienced dermatologist and slit skin smear, the ML Flow test could be used to improve treatment decisions in field conditions. © 2009 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)172-176
JournalActa tropica
Volume111
Issue number2
DOIs
Publication statusPublished - Aug 2009
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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