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

Abstract

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. 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. 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). 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.