RESUMO
OBJECTIVE: This study aims to develop and validate predictive models that assess the risk of leprosy development among contacts, contributing to an enhanced understanding of disease occurrence in this population. METHODS: A cohort of 600 contacts of people with leprosy treated at the National Reference Center for Leprosy and Health Dermatology at the Federal University of Uberlândia (CREDESH/HC-UFU) was followed up between 2002 and 2022. The database was divided into two parts: two-third to construct the disease risk score and one-third to validate this score. Multivariate logistic regression models were used to construct the disease score. RESULTS: Of the four models constructed, model 3, which included the variables anti-phenolic glycolipid I immunoglobulin M positive, absence of Bacillus Calmette-Guérin vaccine scar and age ≥60 years, was considered the best for identifying a higher risk of illness, with a specificity of 89.2%, a positive predictive value of 60% and an accuracy of 78%. CONCLUSIONS: Risk prediction models can contribute to the management of leprosy contacts and the systematisation of contact surveillance protocols.