T-BACCO SCORE: A predictive scoring tool for tuberculosis (TB) loss to follow-up among TB smokers

Abstract

Introduction: 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, which lead to a higher rate of LTFU. We aim to develop a prognostic scoring tool to predict LTFU among TB patients who smoke to improve successful TB treatment outcomes.

Materials and methods: The development of the prognostic model utilized prospectively collected longitudinal data of adult TB patients who smoked in the state of Selangor between 2013 until 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. 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 plot.

Results: 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 prognostic 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). The model 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.

Conclusion: Predicting LTFU among TB patients who smoke in the early phase of TB treatment is achievable using this simple T-BACCO SCORE. The applicability of the tool in clinical settings helps health care professionals manage TB smokers based on their risk scores. Further external validation should be carried out prior to use.

Fig 1. Data processing flow chart.

Fig 2. Comparison of the ROC curve between development cohort and internal validation cohort.

The blue line represents the ROC curve of the development cohort, and the dotted green line is the ROC curve of the internal validation cohort. AUC for development cohort: 0.681 (95% CI 0.657–0.713). AUC for internal validation cohort: 0.668 (95% CI 0.639–0.698).

Fig 3. Calibration curve of the final and internal validation model.

The straight line represents a perfect prediction by an ideal model, and the blue line represents the performance in the final model (A) and internal validation model (B). This visual curve suggests a good calibration between the predicted and the actual outcome in both models.

Fig 4. Comparison of the ROC curve between different model in the development phase.

The blue line represents the ROC curve of the final model (Model1), the dotted green line represents the model that includes variable ‘DOTS during intensive phase’ (Model2), and the dotted orange line represent the model that include variables ‘DOTS during the intensive phase’ and ‘DOTS during the maintenance phase’ (Model3). The AUC value for the model were 0.681, 0.763 and 0.936 respectively. ROC, receiver operating characteristic; AUC, area under the curve; DOTS, directly observe therapy short course.