A novel integrated molecular and serological analysis method to predict new cases of leprosy amongst household contacts

PLoS Negl Trop Dis. 2019 Jun 10;13(6):e0007400. doi: 10.1371/journal.pntd.0007400. eCollection 2019 Jun.

Abstract

Background: Early detection of Mycobacterium leprae is a key strategy for disrupting the transmission chain of leprosy and preventing the potential onset of physical disabilities. Clinical diagnosis is essential, but some of the presented symptoms may go unnoticed, even by specialists. In areas of greater endemicity, serological and molecular tests have been performed and analyzed separately for the follow-up of household contacts, who are at high risk of developing the disease. The accuracy of these tests is still debated, and it is necessary to make them more reliable, especially for the identification of cases of leprosy between contacts. We proposed an integrated analysis of molecular and serological methods using artificial intelligence by the random forest (RF) algorithm to better diagnose and predict new cases of leprosy.

Methods: The study was developed in Governador Valadares, Brazil, a hyperendemic region for leprosy. A longitudinal study was performed, including new cases diagnosed in 2011 and their respective household contacts, who were followed in 2011, 2012, and 2016. All contacts were diligently evaluated by clinicians from Reference Center for Endemic Diseases (CREDEN-PES) before being classified as asymptomatic. Samples of slit skin smears (SSS) from the earlobe of the patients and household contacts were collected for quantitative polymerase chain reaction (qPCR) of 16S rRNA, and peripheral blood samples were collected for ELISA assays to detect LID-1 and ND-O-LID.

Results: The statistical analysis of the tests revealed sensitivity for anti-LID-1 (63.2%), anti-ND-O-LID (57.9%), qPCR SSS (36.8%), and smear microscopy (30.2%). However, the use of RF allowed for an expressive increase in sensitivity in the diagnosis of multibacillary leprosy (90.5%) and especially paucibacillary leprosy (70.6%). It is important to report that the specificity was 92.5%.

Conclusion: The proposed model using RF allows for the diagnosis of leprosy with high sensitivity and specificity and the early identification of new cases among household contacts.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Antibodies, Bacterial / blood
  • Artificial Intelligence
  • Brazil
  • Child
  • Child, Preschool
  • DNA, Bacterial / chemistry
  • DNA, Bacterial / genetics
  • DNA, Ribosomal / chemistry
  • DNA, Ribosomal / genetics
  • Enzyme-Linked Immunosorbent Assay / methods*
  • Family Characteristics*
  • Family Health*
  • Female
  • Humans
  • Leprosy / diagnosis*
  • Longitudinal Studies
  • Male
  • Middle Aged
  • Molecular Diagnostic Techniques / methods
  • Mycobacterium leprae / genetics*
  • Mycobacterium leprae / immunology*
  • RNA, Ribosomal, 16S / genetics
  • Real-Time Polymerase Chain Reaction / methods*
  • Sensitivity and Specificity
  • Serologic Tests / methods
  • Young Adult

Substances

  • Antibodies, Bacterial
  • DNA, Bacterial
  • DNA, Ribosomal
  • RNA, Ribosomal, 16S

Grants and funding

This work was funded by Fundação de Amparo a Pesquisa de Minas Gerais - FAPEMIG, Conselho Nacional de Pesquisa - CNPq/DECIT 2008 and DECIT 2012, Termo de Convênio - TC 304/2013/ Fundo Nacional de Saúde -FNS/ Ministério da Saúde -MS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.