Diagnosis-time Risk Stratification for Tuberculosis Mortality in a Nationwide Public-Private Mix Program.

OBJECTIVES: Mortality remains a major barrier to tuberculosis (TB) control, as many deaths occur shortly after diagnosis. We aimed to identify determinants of mortality and develop diagnosis-time risk-prediction models for patients with TB. METHODS: We conducted a nationwide population-based cohort study using data from South Korea's public-private mix TB program, including patients diagnosed between January 2019 and December 2022. Two models were developed in the 2019-2021 cohort and validated in the independent 2022 cohort: TREAT-TB, incorporating demographic, clinical, radiologic, and microbiological variables, and SCREEN-TB, based only on demographic characteristics, symptoms, and comorbidity data. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), Brier score, and calibration analysis. RESULTS: Among 24,745 patients, 2,667 (10.8%) died during treatment. Nearly half of the deaths occurred within two months of diagnosis. Older age, lower body mass index, and organ-specific comorbidities, particularly cardiovascular, neurological, and renal diseases, were associated with mortality. TREAT-TB and SCREEN-TB showed similar discrimination and good calibration in the validation cohort (AUCs 0.807 and 0.805, respectively), with mortality increasing across risk scores. CONCLUSIONS: Diagnosis-time risk stratification is feasible without radiologic or microbiological results and may support early triage and targeted monitoring within TB control programs.