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

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-OLID (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.

Univale
Cursos
Campus Armando Vieira

Rua Juiz de Paz José Lemos, 695 – Vila Bretas, CEP: 35030-260, Governador Valadares/MG
(33) 3279-5200
Campus Antônio Rodrigues Coelho

Rua Israel Pinheiro, 2000 – Universitário, CEP: 35020-220, Governador Valadares/MG (33) 3279-5500
®Copyright 2000 – 2021 | Fundação Percival Farquhar (33) 3279-5515 / (33) 3279-5505 CNPJ: 20.611.810/0001-91
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