Analysis of T-lymphocyte subsets and risk factors in children with tuberculosis

Abstract

Background

Tuberculosis (TB) is not only related to infection but also involves immune factors. This study explores the changes in T-lymphocyte subsets in children with TB who are human immunodeficiency virus (HIV)-negative and examines their relationship using chest computed tomography (CT) scans. Additionally, the study identifies risk factors for severe TB (STB) in children and establishes relevant risk prediction models.

Methods

We recruited 235 participants between 2018 and 2022, comprising 176 paediatric patients with TB who were HIV-negative and 59 age-matched children with bacterial community-acquired pneumonia (CAP). We quantitatively analysed and compared T-lymphocyte subsets between the two groups and among different types of TB infection. Both univariate and multivariate analyses of clinical and laboratory characteristics were conducted to identify independent risk factors for STB in children and to establish a risk prediction model.

Results

The absolute counts of CD3, CD4 and CD8 T-cells in children with TB infection decreased significantly compared with bacterial CAP. The percentage of CD8 T-cells increased, whereas the percentage of CD4 T-cells did not change significantly. The absolute count of CD3, CD4 and CD8 T-cells in extrapulmonary TB (EPTB) was significantly higher than in extra-respiratory TB, with unchanged subset percentages. According to chest CT lesion classification, CD4 T-cell counts decreased significantly in S3 compared with S1 or S2, with no significant change in CD3 and CD8 T-cell counts and percentages. No significant differences were observed in lymphocyte subset counts and percentages between S1 and S2. Univariate analyses indicated that factors such as age, symptom duration, white blood cell count, platelet count, neutrophil-to-lymphocyte ratio (NLR), erythrocyte sedimentation rate, prealbumin level, albumin level, globulin level, albumin/globulin (A/G) ratio, high-sensitivity C-reactive protein (Hs-CRP) level and CD4 and CD8 T-cell counts are associated with STB. Multivariate logistic regression analysis revealed that age, Hs-CRP level, NLR, symptom duration and A/G ratio are independent risk factors for STB in children. Increased age, Hs-CRP levels and NLR, along with decreased A/G, correlate with increased susceptibility to STB. A nomogram model, based on these independent risk factors, demonstrated an area under the receiver operating characteristics curve of 0.867 (95% CI: 0.813–0.921). Internal verification confirmed the model's accuracy, with the calibration curve approaching the ideal and the Hosmer–Lemeshow goodness-of-fit test showing consistent results (χ² = 12.212, p = 0.142).

Conclusion

In paediatric patients with TB, the absolute counts of all lymphocyte subsets were considerably reduced compared with those in patients with bacterial CAP. Clinicians should consider the possibility of EPTB infection in addition to respiratory infections in children with TB who have higher CD3, CD4 and CD8 T-cell counts than the ERTB group. Furthermore, CD4 T-cell counts correlated closely with the severity of chest CT lesions. Age, symptom duration, A/G ratio, Hs-CRP level and NLR were established as independent risk factors for STB. The nomogram model, based on these factors, offers effective discrimination and calibration in predicting STB in children.