Abstract
BackgroundTuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control.ObjectiveTo identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure.MethodsOn a multi-country dataset managed by the National Institute of Allergy and Infectious Diseases we applied various machine learning techniques to identify factors statistically associated with treatment failure and to predict treatment failure based on baseline demographic and clinical characteristics alone.ResultsThe complete-case analysis database consisted of 587 patients (68% males) with a median (p25-p75) age of 40 (30–51) years. Treatment failure occurred in approximately one fourth of the patients. The features most associated with treatment failure were patterns of drug sensitivity, imaging findings, findings in the microscopy Ziehl-Nielsen stain, education status, and employment status. The most predictive model was forward stepwise selection (AUC: 0.74), although most models performed at or above AUC 0.7. A sensitivity analysis using the 643 original patients filling the missing values with multiple imputation showed similar predictive features and generally increased predictive performance.ConclusionMachine learning can help to identify patients at higher risk of treatment failure. Closer monitoring of these patients may decrease treatment failure rates and prevent emergence of antibiotic resistance. The use of inexpensive basic demographic and clinical features makes this approach attractive in low and middle-income countries.
Highlights
Tuberculosis (TB) is a bacterial infectious disease that is endemic in many countries around the world
The features most associated with treatment failure were patterns of drug sensitivity, imaging findings, findings in the microscopy Ziehl-Nielsen stain, education status, and employment status
In order to give an approximate idea of clinical applicability, we provide sensitivities, specificities, positive predictive values (PPV), and negative predictive values (NPV) in our cohort
Summary
Tuberculosis (TB) is a bacterial infectious disease that is endemic in many countries around the world. The yearly death global toll of TB is estimated to be approximately 1.5 million. These figures are underestimations of the true burden of disease since many cases are not diagnosed or reported. There are many challenges for effective prevention, diagnosis, and treatment, with some of the most important being lack of funding, limited access to health resources (infrastructure, testing facilities, drug availability), stigmatization, poverty, and lack of compliance. These challenges are highly country dependent, explaining much of the regional variation. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control
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