Validation of a novel EC and PPD-based decision tree model for tuberculosis screening in Tibetan adolescent students

To evaluate the utility and effectiveness of the recombinant Mycobacterium tuberculosis fusion protein (EC) skin test for tuberculosis (TB) screening among student populations in high-altitude regions and to provide evidence-based recommendations for optimizing epidemic control strategies.

A total of 1,047 primary and secondary school students in Seda County were enrolled. Both the tuberculin skin test (TST/PPD) and EC skin test were administered to all participants. Data analysis was performed using R 4.3.0 and Python 12.0 statistical software. Descriptive analyses included skewed continuous data expressed as median (Q₁, Q₃) and analyzed using the Kruskal-Wallis test, while categorical data were presented as n (%) and analyzed using Chi-square or Fisher's exact tests. Model construction and performance evaluation were implemented in Python, utilizing packages such as graphviz, matplotlib, and scikit-plot for visualization and metrics calculation.

Based on expert consensus, participants were stratified into three groups: BCG vaccination (n = 29, 2.77%), uninfected (n = 975, 93.12%), and at least latent infection (including both latent TB infection and active TB, n = 43, 4.11%). The PPD test showed significant intergroup differences (p < 0.001), with AUC values of 0.98 (BCG vaccination), 0.92 (uninfected), and 0.83 (at least latent infection), and an overall Kappa coefficient of 0.59. The EC test demonstrated perfect performance in identifying latent infections (precision, recall, F1-score, and AUC = 1.00) but failed to distinguish BCG-vaccinated individuals (all metrics = 0). A decision tree model combining EC + PPD demonstrated perfect classification performance on the current dataset, achieving accuracy, recall, and AUC values of 1.00 across all classifications, with a micro-average AUC of 1.00 and a Kappa coefficient of 1.00.

While the EC skin test exhibits 100% sensitivity for latent TB infection, it cannot differentiate between persistent post-vaccination positivity and true uninfected status. The EC + PPD decision tree model synergistically optimizes multi-dimensional metrics, enabling high-sensitivity detection of latent infections and precise exclusion of false positives, thereby improving overall diagnostic performance. This integrated approach could improve TB screening accuracy in high-altitude student populations, inform targeted public health interventions, and warrants further validation. While this study was conducted in a high-altitude region, the combined EC + PPD approach warrants evaluation in other settings with high BCG vaccination rates.