Development of prognostic scoring system for predicting 1-year mortality among pulmonary tuberculosis patients in South India

Background Development of a prediction model using baseline characteristics of tuberculosis (TB) patients at the time of diagnosis will aid us in early identification of the high-risk groups and devise pertinent strategies accordingly. Hence, we did this study to develop a prognostic-scoring model for predicting the death among newly diagnosed drug sensitive pulmonary TB patients in South India. Methods We undertook a longitudinal analysis of cohort data under the Regional Prospective Observational Research for Tuberculosis India consortium. Multivariable cox regression using the stepwise backward elimination procedure was used to select variables for the model building and the nomogram-scoring system was developed with the final selected model. Results In total, 54 (4.6%) out of the 1181 patients had died during the 1-year follow-up period. The TB mortality rate was 0.20 per 1000 person-days. Eight variables (age, gender, functional limitation, anemia, leukopenia, thrombocytopenia, diabetes, neutrophil-lymphocyte ratio) were selected and a nomogram was built using these variables. The discriminatory power was 0.81 (95% confidence interval: 0.75-0.86) and this model was well-calibrated. Decision curve analysis showed that the model is beneficial at a threshold probability similar to 15-65%. Conclusions This scoring system could help the clinicians and policy makers to devise targeted interventions and in turn reduce the TB mortality in India.

Automated summary

A prognostic-scoring model was developed to predict death among newly diagnosed drug sensitive pulmonary TB patients in South India. The model, which had a discriminative power of 0.81, could aid clinicians and policy makers in devising targeted interventions to reduce TB mortality.