23Background 24 Previous qualitative studies suggested that the false negative rate of T cell spot test 25 for tuberculosis infection (T-SPOT.TB) is associated with many risk factors in 26 tuberculosis patients; However, more precise quantitative studies are not well known. 27 Objective 28 To investigate the factors affecting quantified T-SPOT.TB in patients with active 29 tuberculosis.30Methods 31 We retrospectively analyzed the data of 360 patients who met the inclusion criteria. 32 Using the levels of early secreted antigenic target 6 kDa (ESAT-6) and culture filtrate 33 protein 10 kDa (CFP-10) as dependent variables, variables with statistical 34 significance in the univariate analysis were subjected to optimal scaling regression 35 analysis. 36 Results 37The results showed that the ESAT-6 regression model had statistical significance 38 (P-trend < 0.001) and that previously treated cases, CD4+ and platelet count were its 39 independent risk factors (all P-trend < 0.05); their importance levels were 0.095, 40 0.596 and 0.100, respectively, with a total of 0.791. The CFP-10 regression model 41 also had statistical significance (P-trend < 0.001); platelet distribution width and 42 alpha-2 globulin were its independent risk factors (all P-trend < 0.05), their 43 importance levels were 0.287 and 0.247, respectively, with a total of 0.534. The 44 quantification graph showed that quantified T-SPOT.TB levels had a linear correlation 45 3 with risk factors. 46 Conclusion 47 The test results of T-SPOT.TB should be given more precise explanations, especially 48 in patients with low levels of CD4+, platelet, alpha-2 globulin and high platelet 49 distribution width. 50 51 Introduction 52 The interferon-gamma release assay (IGRA) represents one of the most important 53 advances in the immunodiagnosis of Mycobacterium tuberculosis (MTB) infection in 54 the past two decades. As a new adjuvant method for the diagnosis of MTB infection, 55 IGRA has been widely applied and accepted clinically. In principle, IGRA determines 56 whether the subject is infected with MTB by examination of the levels of released 57 γ -interferon (IFN-γ) after stimulation of whole blood or peripheral blood mononuclear 58 cells (PBMCs) with MTB-specific antigen. This test is not affected by Bacillus 59 Calmette-Guerin (BCG) vaccination [1], a feature that is very beneficial in countries 60 such as China in which general BCG vaccination is practiced. Currently, the T cell 61 spot test for tuberculosis infection (T-SPOT.TB) is the main IGRA test method; it 62 provides intuitive and reproducible results and quantitatively reflects the number of 63 IFN-γ secreting cells in preparations of PBMCs [2]. 64 IGRA still has a certain false negative rate among patients with tuberculosis (TB). 65 Previous studies reported that negative bacteria in sputum [3-5], hypoproteinemia 66 [6-8], combined HIV infection [4, 7, 9], anti-TB treatment [10, 11], medical history [8, 67 4 12], anemia [6, 13], diabetes [14], parasitic infections [13], noncavitary lesion...
Background Previous qualitative studies suggested that the false negative rate of the 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 lacking. The purpose of this study was to investigate the factors affecting quantified spot-forming cells (SFCs) to early secreted antigenic target 6 kDa (ESAT-6) or culture filtrate protein 10 kDa (CFP-10) in patients with active tuberculosis. Methods We retrospectively analyzed the data of 360 patients who met the inclusion criteria. Using the SFCs to ESAT-6 or CFP-10 levels as dependent variables, variables with statistical significance in the univariate analysis were subjected to optimal scaling regression analysis. The combination of ESAT-6 and CFP-10 (i.e., T-SPOT. TB ) was analyzed by the exact logistic regression model. Results The results showed that the SFCs to ESAT-6 regression model had statistical significance ( P < 0.001) and that previous treatment and CD4+ and platelet counts were its independent risk factors (all P < 0.05). Their importance levels were 0.095, 0.596 and 0.100, respectively, with a total of 0.791. The SFCs to CFP-10 regression model also had statistical significance ( P < 0.001); platelet distribution width and alpha-2 globulin were its independent risk factors (all P < 0.05). Their importance levels were 0.287 and 0.247, respectively, with a total of 0.534. The quantification graph showed that quantified SFCs to ESAT-6 or CFP-10 grading had a linear correlation with risk factors. Albumin-globulin ratio, CD4+ and CD8+ were independent risk factors for false negative T-SPOT. TB (all P < 0.05). Conclusions In T-SPOT. TB -assisted diagnosis of patients with active tuberculosis, previous treatment, decreased CD4+ and platelet count might lead to the decreased SFCs to ESAT-6, decreased alpha-2 globulin and high platelet distribution width might lead to the decreased SFCs to CFP-10, decreased albumin-globulin ratio, CD4+ and CD8+ might lead to an increase in the false negative rate of the T-SPOT. TB . Electronic supplementary material The online version of this article (10.1186/s12879-019-4310-y) contains supplementary material, which is available to authorized users.
Objectives This study aimed to use the results of routine blood tests and relevant parameters to construct models for the prediction of active tuberculosis (ATB) and drug-resistant tuberculosis (DRTB) and to assess the diagnostic values of these models. Methods We performed logistic regression analysis to generate models of plateletcrit-albumin scoring (PAS) and platelet distribution width-treatment-sputum scoring (PTS). Area under the curve (AUC) analysis was used to analyze the diagnostic values of these curves. Finally, we performed model validation and application assessment. Results In the training cohort, for the PAS model, the AUC for diagnosing ATB was 0.902, sensitivity was 82.75%, specificity was 82.20%, accuracy rate was 81.00%, and optimal threshold value was 0.199. For the PTS model, the AUC for diagnosing DRTB was 0.700, sensitivity was 63.64%, specificity was 73.53%, accuracy rate was 89.00%, and optimal threshold value was −2.202. These two models showed significant differences in the AUC analysis, compared with single-factor models. Results in the validation cohort were similar. Conclusions The PAS model had high sensitivity and specificity for the diagnosis of ATB, and the PTS model had strong predictive potential for the diagnosis of DRTB.
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