TB Research

Quantitative investigation of factors relevant to the T cell spot test for tuberculosis infection in active tuberculosis

Kui Li, Caiyong Yang, Zicheng Jiang, Shengxi Liu, Jun Liu, Chuanqi Fan, Tao Li, Xuemin Dong

bioRxiv (Cold Spring Harbor Laboratory) · 2019-02

Abstract

Abstract Background Previous qualitative studies suggested that the false negative rate of T cell spot test for tuberculosis infection (T-SPOT.TB) is associated with many risk factors in tuberculosis patients; However, more precise quantitative studies are not well known. Objective To investigate the factors affecting quantified T-SPOT.TB in patients with active tuberculosis. Methods We retrospectively analyzed the data of 360 patients who met the inclusion criteria. Using the levels of early secreted antigenic target 6 kDa (ESAT-6) and culture filtrate protein 10 kDa (CFP-10) as dependent variables, variables with statistical significance in the univariate analysis were subjected to optimal scaling regression analysis. Results The results showed that the ESAT-6 regression model had statistical significance ( P -trend < 0.001) and that previously treated cases, CD4+ and platelet count were its independent risk factors (all P -trend < 0.05); their importance levels were 0.095, 0.596 and 0.100, respectively, with a total of 0.791. The CFP-10 regression model also had statistical significance (P-trend < 0.001); platelet distribution width and alpha-2 globulin were its independent risk factors (all P -trend < 0.05), their importance levels were 0.287 and 0.247, respectively, with a total of 0.534. The quantification graph showed that quantified T-SPOT.TB levels had a linear correlation with risk factors. Conclusion The test results of T-SPOT.TB should be given more precise explanations, especially in patients with low levels of CD4+, platelet, alpha-2 globulin and high platelet distribution width.

MeSH terms

  • Tuberculosis
  • Statistical significance
  • Internal medicine
  • Univariate
  • Regression analysis
  • Medicine
  • Univariate analysis
  • Linear regression
  • Immunology