Predictive role of systemic immune-inflammation index (SII) in tuberculosis infection morbidity and ICU tuberculosis patient mortality: an observational study

Background According to estimates from the World Health Organization (WHO), 25% of people worldwide are infected with Mycobacterium tuberculosis (MTB). The ability to identify tuberculosis infection (TBI) and then give pretreatment is an effective way to end tuberculosis (TB). The systemic immune-inflammation index (SII) is an inflammatory biomarker that reflects the relationship between immune response and systemic inflammation status in the host. However, its association with TBI remains unclear. This study aimed to assess the association between the SII and tuberculosis outcomes, including disease morbidity and in-hospital mortality. Methods This observational study used public data from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2012. We used multiple imputations for missing values. Next, Least Absolute Shrinkage and Selection Operator (LASSO) regression combined with multiple regression analyses were utilized to identify the risk factors correlated with the morbidity of TB infection and to construct a prediction model visualized by a nomogram. Subsequently, receiver operating characteristic (ROC) and decision curve analysis (DCA) curves were used to determine the clinical value of the constructed predictive model. We included 97 intensive care unit (ICU) TB patients from July 2021 to December 2023 and then used Cox regression to verify the prognostic value of the SII in severe TB. Results The SII was lower in patients with TB infection compared to those without TB infection (P Conclusions These findings suggest that the SII level has strong predictive value for the morbidity of TB infection and a close relationship with the mortality of ICU TB patients.