Dynamic, quantitative ESAT6-CFP10 skin test for tuberculosis risk prediction: a large-scale, multi-center, prospective cohort study

Background: ) infection will progress to active disease remains challenging. We evaluated whether a pragmatic serial, quantitative ESAT6-CFP10 (EC) antigen-based skin test strategy improves risk stratification for targeted prevention in a large-scale prospective cohort. Methods: We enrolled 73, 761 contacts identified during school-based tuberculosis outbreaks in Jiangsu, China, from 2020 to 2024. Participants underwent baseline EC test, chest radiography, and symptom screening. EC-negative individuals were retested after 8-12 weeks. Incident tuberculosis was identified through active surveillance and registry linkage. We compared single vs. serial test strategies using Cox models, receiver operating characteristic (ROC) curves and precision-recall (PR) curves. Findings: Among 73, 761 close contacts, 108 had prevalent tuberculosis and 190 developed incident cases (overall incidence 151. 2 per 100, 000 person-years). EC response size predicted incident tuberculosis in a steep, dose-dependent manner. Each 1-mm increase in the maximum response diameter was associated with a 7% higher hazard. The serial test strategy, utilizing the maximum response from two measurements, substantially outperformed single test C-statistic: 0. 806 vs. 0. 722. At the ≥5 mm threshold, this combined strategy yielded a sensitivity of 65. 0% and specificity of 96. 1%. The subgroup of recent converters had a PPV of 3. 4% (95% CI: 2. 8-4. 1), corresponding to an NNT of approximately 30, and a hazard ratio (HR) of 45. 12 (95% CI: 32. 50-62. 63). Preventive treatment completion was strongly protective (aHR 0. 17; 95% CI: 0. 11-0. 25). Interpretation: The "test twice, take maximum" EC strategy provides superior risk stratification for tuberculosis prevention. This approach identifies high-risk contacts for targeted intervention. Despite limited sensitivity, these results suggest that quantitative EC skin testing can provide a practical alternative for programmatic risk stratification. In settings where IGRAs are constrained by cost or infrastructure, this approach may enable more efficient targeting of preventive treatment. Funding: National Natural Science Foundation of China (82504476, 82473693, 82574173) ; Jiangsu Province Preventive Medicine Research Project (Ym2023039) ; The Special Scientific Research Project for Talent Introduction of the First Affiliated Hospital of Wannan Medical College (KY2960YR2530) ; Jiangsu Province Postgraduate Research and Innovation Project (KYCX24₂061).