Classification and regression trees for predicting the risk of a negative test result for tuberculosis infection in Brazilian healthcare workers: a cross-sectional study

• Autor(a) principal: Souza,Fernanda Mattos
• Data de Publicação: 2021
• Outros Autores: Prado,Thiago Nascimento do, Werneck,Guilherme Loureiro, Luiz,Ronir Raggio, Maciel,Ethel Leonor Noia, Faerstein,Eduardo, Trajman,Anete
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista brasileira de epidemiologia (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2021000100426
• topic: Latent tuberculosis; Occupational risks; Machine learning; Decision trees

ABSTRACT: Objectives: Healthcare workers (HCWs) have a high risk of acquiring tuberculosis infection (TBI). However, annual testing is resource-consuming. We aimed to develop a predictive model to identify HCWs best targeted for TBI screening. Methodology: We conducted a secondary analysis of previously published results of 708 HCWs working in primary care services in five Brazilian State capitals who underwent two TBI tests: tuberculin skin test and Quantiferon®-TB Gold in-tube. We used a classification and regression tree (CART) model to predict HCWs with negative results for both tests. The performance of the model was evaluated using the receiver operating characteristics (ROC) curve and the area under the curve (AUC), cross-validated using the same dataset. Results: Among the 708 HCWs, 247 (34.9%) had negative results for both tests. CART identified that physician or a community health agent were twice more likely to be uninfected (probability = 0.60) than registered or aid nurse (probability = 0.28) when working less than 5.5 years in the primary care setting. In cross validation, the predictive accuracy was 68% [95% confidence interval (95%CI): 65 - 71], AUC was 62% (95%CI 58 - 66), specificity was 78% (95%CI 74 - 81), and sensitivity was 44% (95%CI 38 - 50). Conclusion: Despite the low predictive power of this model, CART allowed to identify subgroups with higher probability of having both tests negative. The inclusion of new information related to TBI risk may contribute to the construction of a model with greater predictive power using the same CART technique.