Development and validation of T-BACCO SCORE a prognostic tuberculosis (TB) loss to follow-up scoring tool among TB smokers / Dr. Zatil Zahidah Mohd Sharani

Loss to follow-up (LTFU) and smoking during TB treatment are major challenges for TB control programs. Smoking increases the severity and prolongs TB treatment duration, leading to a higher LTFU rate. We aim to develop a prognostic scoring tool to predict LTFU among TB patients who smoke to improve successful TB treatment outcomes. The development of the predictive model utilized prospectively collected longitudinal data of adult TB patients who smoked in the state of Selangor between the years 2013 until the year 2017, which were obtained from the Malaysian Tuberculosis Information System (MyTB) database. Data were randomly split into development and internal validation cohorts. A simple prognostic score (T-BACCO SCORE) was constructed based on the regression coefficients of predictors in the final logistic model of the development cohort. The estimated missing data was 2.8% from the development cohort and was completely at random. Model discrimination was determined using c- statistics (AUCs), and calibration was based on the Hosmer and Lemeshow goodness of fit test and calibration curve. The model highlights several variables with different T-BACCO SCORE values as predictors for LTFU among TB patients who smoke (e.g., age group, ethnicity, locality, nationality, educational level, monthly income level, employment status, TB case category, TB detection methods, X-ray categories, HIV status, and sputum status). The scores were categorized into three groups that predict the risk for LTFU: low-risk (<15 points), medium-risk (15 to 25 points), and high-risk (> 25 points). T-BACCO SCORE exhibited fair discrimination with a c-statistic of 0.681 (95% CI 0.627-0.710) and good calibration with a nonsignificant chi-square Hosmer‒Lemeshow's goodness of fit test χ2=4.893 and accompanying p-value of 0.769. In the external validation of T-BACCO SCORE, the validation model provided a good discrimination ability with an AUC of 0.706 (95% CI 0.636-0.775), which is comparable with the discriminative performance of the development model AUC 0.681 (95% CI 0.652-0.710). This value indicates that the T-BACCO SCORE model was able to distinguish 70.6% correct of the LTFU outcome among TB patients who smoke. The validation model has a nonsignificant chi-square Hosmer‒Lemeshow goodness of fit test value, χ2=5.037, p-value = 0.754, and a satisfactory calibration curve. A larger population pool is needed for a strong calibration performance for model validation. In sum, predicting LTFU among TB patients who smoke in the early phase of TB treatment is achievable using this simple T-BACCO SCORE. The tool's applicability in clinical settings helps healthcare professionals manage TB smokers based on their risk scores.