Análise integrada de métodos moleculares e sorológicos para diagnóstico de hanseníase e monitoramento de contatos domiciliares

Abstract

The diagnosis of leprosy is eminently clinical and may be supplemented with bacilloscopy, histopathology and immunological tests. Classical bacteriological methods for the identification of pathogenic bacteria can not be applied for the diagnosis of leprosy, mainly due to the impossibility of in vitro culture of M. leprae. Histopathological examination and smear microscopy have been used as auxiliary methods for the clinical classification of cases. Molecular and serological techniques have been evaluated as diagnostic tools in leprosy. Unlike smear microscopy, which requires about 104 organisms per gram of tissue for actual detection, PCR is a technique of high specificity and sensitivity, capable of detecting 25 fg (10-15g) of M. leprae DNA. In addition, a possibility of its use in almost all types of clinical specimens gives this method a high potential for differential diagnosis. Serological tests are aimed at detecting specific antibodies against M. leprae that indicate infection. These tests may be useful in monitoring therapy efficacy, in determining disease prevalence, and in assessing the distribution of infection between household contacts. It is considered that the individual who resides or has lived with the leprosy patient has a higher risk of becoming ill in relation to the general population due to the fact that they are exposed to M. leprae. Relatives of patients Multibacillary and Paucibacillary should be examined, regardless of the time of conviviality. It is suggested to evaluate annually, for five years, all non-sick contacts, whether family or social. In this study we used the support of Artificial Intelligence (Random Forest) for integrated analysis of serological and molecular methods, in the diagnosis of new cases of leprosy and monitoring of household contacts for a period of five years. The study was developed in Governador Valadares - MG, considered an endemic area of leprosy. The proposed design is of the longitudinal type, with the collection of cadastral data of all cases diagnosed in 2011 and their respective household contacts registered in the period of 2011, 2012 and 2016. A total of 196 subjects, including 43 cases, 113 household contacts and 40 individuals considered endemic controls who reported not having lived with patients with leprosy or a history of leprosy in the family were included in the study. Blood samples and intradermal scrapings were collected from leprosy cases and their respective household contacts for analysis by qPCR (16S rRNA) and ELISA (anti-ND-O-LID and LID-1). The integrated analysis of the data was performed through Random Forest with the objective of improving the performance of the tests for leprosy diagnosis. In isolation, the qPCR showed sensitivity of 48.8% and specificity of 100% in the diagnosis of leprosy cases. In the anti-NDO- LID ELISA the sensitivity reached was 57.9% and specificity was 97.5%, whereas in the anti-LID-1 assay the sensitivity and specificity were 63.2% and 92.5%, respectively. However, the integrated analysis of the data by Random forest, using 10,000 decision trees, with a modal error of 12.8%, obtained a sensitivity rate of 81.6% and specificity of 92.5% in the prediction of new cases of leprosy. The Random Forest model was used to monitor household contacts within a period of 5 years. This analysis tool identified between the contacts, 02 patients, even before the clinical diagnosis. At the end of the follow-up period, 03 household contacts were reported as new cases of leprosy. Thus, the model proposed by the Random Forest analysis allowed the diagnosis of leprosy cases with high sensitivity and specificity and early identification of new cases among household contacts during monitoring.