Abstract
Tuberculosis (TB) remains a global and national public health concern, with mortality posing a significant challenge in treatment programs. The aim of this study was to develop a simple risk-scoring system to predict mortality among TB patients and assess its applicability in resource-limited settings. Data from TB patient registries in Phichit Province, Thailand, covering from January 1, 2017, to December 31, 2020, were used. Eligible participants were aged ≥18 years, having completed treatment or death. A risk score was developed and internally validated using logistic regression. Coefficients were used to assign weighted points to predictors and applied to a validation cohort to assess diagnostic performance. The performance was evaluated by generating a receiver operating characteristic (ROC) curve. The study included 2,196 participants, randomly allocated into derivation (n=1,600) and validation (n=596) cohorts. The risk score included Charlson Comorbidity Index scores (1-2 points and ≥3 points) and TB meningitis. It showed an area under ROC curve (AuROC) of 74.34% (95%CI: 70.80-77.88%) with good calibration (Hosmer-Lemeshow χ(2): 0.53; p= 0.97). Positive likelihood ratios for low (≤3) and high (≥6) risk were 1.06 (95%CI: 1.03-1.09) and 31.62 (95%CI: 7.23-138.37), respectively. In the validation cohort, AuROC was 79.50% (95%CI: 74.40-84.60%), with 75% and 100% certainty in low- and high-risk groups. In conclusion, this simple risk score, using routine data and two predictors, can predict mortality in TB patients. It may aid clinicians in planning appropriate care strategies. Nevertheless, the tool should undergo external validation before being implemented in clinical practice.