Dataset Information

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-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.

SUBMITTER: Gama RS

PROVIDER: S-EPMC6586366 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Gama Rafael Silva RS Souza Márcio Luís Moreira de MLM Sarno Euzenir Nunes EN Moraes Milton Ozório de MO Gonçalves Aline A Stefani Mariane M A MMA Garcia Raúl Marcel González RMG Fraga Lucia Alves de Oliveira LAO

PLoS neglected tropical diseases 20190610 6

<h4>Background</h4>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 accur ...[more]

PMID: 31181059

Similar Datasets

Project description:ImportanceDespite progress toward reducing global incidence, leprosy control remains a challenge in low- and middle-income countries.ObjectiveTo estimate new case detection rates of leprosy among household contacts of patients with previously diagnosed leprosy and to investigate its associated risk factors.Design, setting, and participantsThis population-based cohort study included families registered in the 100 Million Brazilian Cohort linked with nationwide registries of leprosy; data were collected from January 1, 2007, through December 31, 2014. Household contacts of patients with a previous diagnosis of leprosy from each household unit were followed up from the time of detection of the primary case to the time of detection of a subsequent case or until December 31, 2014. Data analysis was performed from May to December 2018.ExposuresClinical characteristics of the primary case and sociodemographic factors of the household contact.Main outcomes and measuresIncidence of leprosy, estimated as the new case detection rate of leprosy per 100 000 household contacts at risk (person-years at risk). The association between occurrence of a subsequent leprosy case and the exposure risk factors was assessed using multilevel mixed-effects logistic regressions allowing for state- and household-specific random effects.ResultsAmong 42 725 household contacts (22 449 [52.5%] female; mean [SD] age, 22.4 [18.5] years) of 17 876 patients detected with leprosy, the new case detection rate of leprosy was 636.3 (95% CI, 594.4-681.1) per 100 000 person-years at risk overall and 521.9 (95% CI, 466.3-584.1) per 100 000 person-years at risk among children younger than 15 years. Household contacts of patients with multibacillary leprosy had higher odds of developing leprosy (adjusted odds ratio [OR], 1.48; 95% CI, 1.17-1.88), and the odds increased among contacts aged 50 years or older (adjusted OR, 3.11; 95% CI, 2.03-4.76). Leprosy detection was negatively associated with illiterate or preschool educational level (adjusted OR, 0.59; 95% CI, 0.38-0.92). For children, the odds were increased among boys (adjusted OR, 1.70; 95% CI, 1.20-2.42).Conclusions and relevanceThe findings in this Brazilian population-based cohort study suggest that the household contacts of patients with leprosy may have increased risk of leprosy, especially in households with existing multibacillary cases and older contacts. Public health interventions, such as contact screening, that specifically target this population appear to be needed.

Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets