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 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<0.05). LASSO regression and multiple regression analysis identified five significant variables-SII [odds ratio (OR) 0.84, P<0.05], age, race, education level, and exposure to a household TB case-which were incorporated into a predictive model visualized as a nomogram. The area under curve (AUC) of the ROC curves for this model in both the training and validation sets were 0.746 and 0.734, respectively. Finally, in our clinical data of ICU patients with TB, higher SII levels were significantly associated with increased mortality (P<0.05), with an optimal predictive value of 1,460.25 (1,000 cells/µL). 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.