Development and validation of a nomogram for predicting false negative IGRA results in pulmonary tuberculosis...

This study aims to explore factors influencing false-negative results in Interferon-Gamma Release Assay (IGRA) for patients with Pulmonary Tuberculosis (PTB), and develop a nomogram model to predict IGRA false negatives, thereby optimizing clinical diagnosis and treatment decisions.

Data were collected from January 2023 to September 2024 at the Second People's Hospital of Fuyang City, involving 143 PTB patients. Among them, 63 patients who were IGRA negative but pathogen positive formed the observation group, while 80 patients who were both IGRA and pathogen positive constituted the control group. Propensity Score Matching (PSM) was used to balance potential confounding factors between the two groups. Clinical characteristics and laboratory indicators were compared, followed by logistic regression analysis to identify independent risk factors affecting IGRA results. A nomogram model was constructed based on these factors and its predictive performance evaluated.

After PSM, each group consisted of 55 patients. The observation group showed significantly lower levels of white blood cell count (WBC), neutrophil count (NEUT), lymphocyte count (LYM), red blood cell count (RBC), hemoglobin (HGB), and albumin (ALB) compared to the control group (P < 0.05). Multivariate analysis ultimately identified RBC, ALB and NLR as independent predictors of IGRA false-negativity. The developed nomogram model demonstrated good calibration (χ² = 4.482, P = 0.811), with an area under the receiver operating characteristic curve (AUC) of 0.764 (95% CI: 0.675-0.853). Decision curve analysis indicated that the net benefit of predicting false-negative IGRA results using this nomogram model was greater than 0 when the threshold probability ranged from 0.15 to 0.95.

Decreased RBC/ALB and elevated NLR may be pivotal factors contributing to false-negative IGRA results in PTB patients. The three-variable nomogram shows enhanced predictive performance, serving as a quantitative tool to identify high-risk cases, particularly for patients with malnutrition or pronounced inflammatory status.