Predictive nomogram of high-risk patients with active tuberculosis in latent tuberculosis infection.

INTRODUCTION: The absence of predictive models for early latent tuberculosis infection (LTBI) progression persists. This study aimed to create a screening model to identify high-risk LTBI patients prome to active tuberculosis (ATB) reactivation. METHODOLOGY: Patients with confirmed ATB were enrolled alongside LTBI individuals as a reference, with relevant clinical data gathered. LASSO regression cross-validation reduced data dimensionality. A nomogram was developed using multiple logistic regression, internally validated with Bootstrap resampling. Evaluation included C-index, receiver operating characteristic (ROC) curve, and calibration curves, with clinical utility assessed through decision curve analysis. RESULTS: The final nomogram incorporated serum albumin (OR = 1.337, p = 0.046), CD4+ (OR = 1.010, p = 0.004), and CD64 index (OR = 0.009, p = 0.020). The model achieved a C-index of 0.964, an area under the ROC curve of 0.962 (95% CI: 0.926-0.997), sensitivity of 0.971, and specificity of 0.910. Internal validation showed a mean absolute error of 0.013 and 86.4% identification accuracy. The decision curve indicated substantial net benefit at a risk threshold exceeding 10% (1: 9). CONCLUSIONS: This study established a biologically-rooted nomogram for high-risk LTBI patients prone to ATB reactivation, offering strong predictability, concordance, and clinical value. It serves as a personalized risk assessment tool, accurately identifying patients necessitating priority prophylactic treatment, complementing existing host risk factors effectively.

Changes in T-lymphocyte subsets and risk factors in human immunodeficiency virus-negative patients with active tuberculosis.

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 Mycobacteriumtuberculosis-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.