Predictive value of a Five-Biomarker Signature to Diagnose Active Pulmonary Tuberculosis Patients

Abstract

Background: To improve the diagnosis accuracy of active pulmonary tuberculosis (TB) from pneumonia (PN) in low income and inadequate facilities areas is one of the biggest problems facing public health today. Most TB patients are difficult to diagnose, especially those who are acid-fast bacillus smear-negative (AFB-) but IGRA®.TB test positive (IGRA+). Thus, we aim to develop a low-cost and rapid risk model for the diagnosis of TB patients with AFB- IGRA+ TB from PN.Methods: We retrospectively analyzed 41 laboratory variables of 204 AFB- IGRA+ TB and 156 PN participants. Candidate variables were identified by t-statistic test and univariate logistic model. The logistic regression analysis was used to construct the multivariate risk model and nomogram with internal and external validation.Results: There were differential correlations between variable pairs in AFB- IGRA+ TB and PN. We found several significant variables in TB compared with PN. Among them, uric acid (UA) was up-elevated , acting as a protective factor with an odds ratio (OR) < 1. By integrating five variables, including Age, UA, albumin (ALB), hemoglobin (Hb) and white blood cell counts (WBC), we constructed a multivariate risk model with a concordance index (C-index) of 0.7 (95% CI: 0.61, 0.8). Nomogram showed that UA and Hb had the protection effect, while Age, WBC and ALB acted as risk factors on TB occurrence. Internal and external validation revealed a good agreement between nomogran prediction and actual observations.Conclusions: Differential correlations existed between variable pairs in AFB- IGRA+ TB and PN. An integration of five biomarkers (Age, UA, ALB, Hb and WBC) can be used to predict TB in AFB- IGRA+ clinical samples from PN.