Purpose Immune function imbalance is closely associated with the occurrence and development of infectious diseases. We studied the characteristics of changes in T-lymphocyte subsets and their risk factors in HIV-negative patients with active tuberculosis (ATB). Methods T-lymphocyte subsets in 275 HIV-negative ATB patients were quantitatively analyzed and compared with an Mycobacterium tuberculosis -free control group. Single-factor and multifactor analyses of clinical and laboratory characteristics of patients were also conducted. Results In ATB patients, CD4 and CD8 T-cell counts decreased, and the levels were positively interrelated ( r = 0.655, P < 0.0001). After 4 weeks of antituberculosis treatment, CD4 and CD8 T-cell counts increased significantly but remained lower than in the control group. CD4 and CD8 cell counts were negatively associated with the extent of lesions detected in the chest by computed tomography (all P < 0.05). Although not reflected in the CD4/CD8 ratio, CD4 and CD8 cell counts differed between drug-resistant TB patients and drug-susceptible TB patients ( P = 0.030). The multivariate analysis showed prealbumin, alpha-1 globulin, body mass index, and platelet count were independent risk factors for decreased CD4 cell count (all P < 0.05), while age and platelet count were independent risk factors for decreased CD8 cell count (all P < 0.05). Conclusion CD4 and CD8 T-cell counts showed the evident value in predicting ATB severity. An increase in the CD4/CD8 ratio may be a critical clue of drug resistance in ATB. Although the factors influencing CD4 and CD8 are not identical, our results indicated the importance of serum protein and platelets to ATB patients’ immune function. Electronic supplementary material The online version of this article (10.1007/s15010-020-01451-2) 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.
Background Chest computed tomography (CT) has been accepted to provide reference for the diagnose and assessment the severity of Corona Virus Disease 2019 (COVID-19). Decrease in the counts of lymphocyte and leukocyte is used as the diagnostic indicator of suspected COVID-19 cases. However, there is few study on exploring the hysteresis of chest CT changes and the predictive role of lymphocyte count in peripheral blood before treatment in the severity of the disease. Methods A retrospective analysis was carried out focusing on the data of patients tested to be positive for RNA nucleic acid test of SARS-CoV-2 with nasopharyngeal swabs in 4 hospitals. An independent assessment was performed by one clinician using the DEXIN FACT Workstation Analysis System, and the assessment results were reviewed by another clinician. Furthermore, the mean hysteresis time was calculated according to the median time from progression to the most serious situation to improvement of chest CT in patients after fever relief. The optimal scaling regression analysis was performed by including variables with statistical significance in univariate analysis. In addition, a multivariate regression model was established to investigate the relationship of the percentage of lesion/total lung volume with lymphocyte and other variables. Results In the included 166 patients with COVID-19, the average value of the most serious percentage of lesion/total lung volume was 6.62, of which 90 patients with fever had an average hysteresis time of 4.5 days after symptom relief, with a similar trend observed in those without fever. Multivariate analysis revealed that lymphocyte count in peripheral blood and transcutaneous oxygen saturation decreased with the increase of the percentage of lesion/total lung volume. Meanwhile, age, fever and C-reactive protein exhibited no such effect in the established model. Conclusions There is a hysteresis effect in the improvement of chest CT image in relative to fever relief in patients with COVID-19. Besides, the percentage of lesion/total lung volume of chest CT correlates negatively with lymphocyte count in peripheral blood and transcutaneous oxygen saturation. Findings in our study may contribute to understanding the disease status of patients with COVID-19 and grasping the opportunity of treatment by clinicians.
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