Neural network models for supporting drug and multidrug resistant tuberculosis screening diagnosis

Tuberculosis (TB) is the leading cause of global mortality among communicable diseases. The diagnosis of Drug-Resistant Tuberculosis (DR-TB) demands even more attention, leading to longer treatments and higher deceased rates. All diagnostic methods available have deficiencies in their detection rates, time release results, or have a higher cost and need a complex infrastructure to setup. New molecular diagnostics, such as the Xpert MTB/RIF assay, have great potential for revolutionizing the diagnosis of Rifampicin Resistance (RR). But, a positive RR result with this test should be carefully interpreted and take into consideration the risk of Multidrug-Resistant TB (MDR-TB) according to its prevalence, locally. Therefore, the development of screening approaches for DR/MDR-TB suspects would help to identify those should be tested by Xpert MTB/RIF. This work develops Artificial Neural Network (ANN) models considering data from presumed DR/MDR-TB subjects according to the National Guidelines at Rio de Janeiro/Brazil, attended in reference centers in Rio de Janeiro, from Feb 2011 and May 2013. Subjects aged 18 years or older, and results were compared with models based on Classification And Regression Trees (CART). Practical operation at different epidemiological scenarios are considered by constructing models using different variable selection criteria, so that environments with low resource conditions can be assisted. Among 280 presumed DR-TB cases included, 38 were DR-TB, 48-MDR, 32-Drug-Sensitive and 162 with no TB. Between DR-TB and non DR-TB, the sensitivity and specificity reached 95.1%(±5.0) and 85.0%(±4.9), respectively. The promising results of clinical score for DR/MDR-TB diagnosis indicate that this approach may be used in the evaluation of presumed DR/MDR-TB.