Malnutrition is Associated with an Increased Risk of Death in Hospitalized Patients with Active Pulmonary Tuberculosis: A Propensity Score Matched Retrospective Cohort Study

Background: This study aimed to investigate whether nutrition levels in patients with active pulmonary tuberculosis (TB) affect their risk of all-cause mortality during hospitalization and to further evaluate the predictive ability of Geriatric Nutritional Risk Index (GNRI) and Body Mass Index (BMI) for risk of all-cause mortality. Methods: Patients from January 1, 2020 to December 31, 2021 were retrieved, and a total of 1847 were included. The primary outcome was all-cause mortality. Propensity score matching (PSM) was performed for risk adjustment, and receiver operating characteristic (ROC) curve analysis was performed to assess the predictive ability of GNRI and BMI for all-cause mortality. Results: Malnourished TB patients were older, had more congestive heart failure, and had more chronic obstructive pulmonary disease or asthma. Under the nutrition level grouping defined by GNRI, the all-cause mortality in the malnourished group did not appear to reach a statistical difference compared with the nonmalnourished group (P = 0.078). When grouped by level of nutrition as defined by BMI, the all-cause mortality was higher in the malnourished group (P = 0.009), and multivariate logistic regression analysis revealed that malnutrition was an independent risk factor for all-cause mortality. After propensity score matching, the results showed that the all-cause mortality was higher in the malnutrition group, regardless of BMI or GNRI defined nutrition level grouping, compared with the control group (both P < 0.001). The ROC curve analysis revealed that the area under the curve (AUC) was 0.811 ([95% confidence interval (CI) 0.701-0.922], P < 0.001) for GNRI and 0.728 ([95% CI 0.588-0.869], P = 0.001) for BMI. Conclusion: In the clinical treatment of patients with active TB, more attention should be paid to the management of nutritional risk. GNRI may be a highly effective and easy method for predicting short-term outcomes in patients with active pulmonary TB.