Abstract:

Objective To establish a diagnostic model for new patients with pathogen-negative pulmonary tuberculosis and improve the standardization and accuracy of clinical diagnosis. Methods From August 2017 to July 2019, the case information of 200 new patients with pathogen-negative pulmonary tuberculosis and 212 patients with non-tuberculous pulmonary disease who had been confirmed in Hunan Chest Hospital were collected. Inclusion criteria of patients were as follows: (1) age ≥15 years old; (2) pathogen-negative Mycobacterium tuberculosis; (3) never been treated with antituberculosis drugs or nonstandard chemotherapy for less than one month. These patients were divided into training and verification groups by a simple random number table, with the proportion of 3∶1. Univariate and multivariate unconditional logistic regressions were used to analyze main clinical observation indicators of training samples, and then the independent predictors were screened out and the diagnostic model was established. The receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the model, taking the clinical diagnosis results of the training samples as the gold standard. A prospective assessment of the established diagnostic model was performed using the validation samples. Results Multivariate unconditional logistic regression analysis showed that serum albumin/globulin ratio (A/G)>1.2 (Wald χ ²=7.264, P=0.007, OR (95%CI)=3.433 (1.400-8.417)), serum carbohydrate antigen-125 (CA-125)>35 kU/L (Wald χ ²=17.114, P=0.000, OR (95%CI)=6.980 (2.780-17.524)), positive interferon-gamma release assay (IGRA) for whole-blood Mycobacterium tuberculosis (Wald χ ²=63.643, P=0.000, OR (95%CI)=19.283 (9.320-39.894)), coughing time ≥2 weeks (Wald χ ²=5.557, P=0.018, OR (95%CI)=0.434 (0.217-0.869)), hemoptysis or bloody phlegm (Wald χ ²=6.237, P=0.013, OR (95%CI)=0.255 (0.087-0.745)), poisoning symptoms of tuberculosis (Wald χ ²=24.930, P=0.000, OR (95%CI)=0.126 (0.056-0.284)), complicating with other diseases (Wald χ ²=12.062, P=0.001, OR (95%CI)=10.139 (2.743-37.746)), lesion site in the posterior segment of the upper lobe tip and/or the dorsal portion of the lower lobe on one or both sides of the lung (Wald χ ²=16.224, P=0.000, OR (95%CI)=4.428 (2.147-9.135)), and cavity formation (Wald χ ²=16.228, P=0.000, OR (95%CI)=14.437 (3.939-52.919)) were independent factors in the identification of pathogen-negative pulmonary tuberculosis and non-tuberculous pulmonary disease. The ROC curve analysis showed that area under the ROC curve (AUC) was 0.881 (95%CI: 0.839-0.915), the optimal cut-off value was 0.42, and the sensitivity and specificity for the diagnosis of pathogen-negative pulmonary tuberculosis were 86.67% and 76.10%, respectively. The total accurate discrimination rate of the diagnostic model in the validation group was 79.61% (82/103). Conclusion The diagnostic model shows high sensitivity and accuracy, and can be used as a reference tool for clinicians to diagnose new patients with pathogen-negative pulmonary tuberculosis.

Key words: Tuberculosis, pulmonary, Diagnosis, differential, Factor analysis, statistical, Models, theoretical, Evaluation studies