Predicting treatment failure, death and drug resistance using a computed risk score among newly diagnosed TB patients in Tamaulipas, Mexico

Abstract

The purpose of this study was to develop a method for identifying newly diagnosed tuberculosis (TB) patients at risk for TB adverse events in Tamaulipas, Mexico. Surveillance data between 2006 and 2013 (8431 subjects) was used to develop risk scores based on predictive modelling. The final models revealed that TB patients failing their treatment regimen were more likely to have at most a primary school education, multi-drug resistance (MDR)-TB, and few to moderate bacilli on acid-fast bacilli smear. TB patients who died were more likely to be older males with MDR-TB, HIV, malnutrition, and reporting excessive alcohol use. Modified risk scores were developed with strong predictability for treatment failure and death (c-statistic 0·65 and 0·70, respectively), and moderate predictability for drug resistance (c-statistic 0·57). Among TB patients with diabetes, risk scores showed moderate predictability for death (c-statistic 0·68). Our findings suggest that in the clinical setting, the use of our risk scores for TB treatment failure or death will help identify these individuals for tailored management to prevent these adverse events. In contrast, the available variables in the TB surveillance dataset are not robust predictors of drug resistance, indicating the need for prompt testing at time of diagnosis.

Keywords: Public health; risk assessment; treatment outcomes in TB; tuberculosis (TB).

Fig. 1.

Rates of adverse events using the modified risk scores. Data from 4215 TB patients in the validation subset were used to assess rates of treatment failure (a), death (b), and drug resistance (c). Data from 2121 TB–DM patients were used to assess rates of death.

Fig. 2.

Diagrammatic scheme for adverse outcomes and drug resistance risk profiles and predictors likelihood for all TB patients in Tamaulipas Mexico. Likelihood ratios (LRs) and c-statistics are based on the multivariable models in Tables 2 and 3.