The patients provided a full medical history, participated in regular physical examinations, and underwent routine investigations, including acquired immunodeficiency syndrome (AIDS) serology, chest radiography, IGRA, and microbiological sputum examination, where possible.

The inclusion criteria for PN participants were as follows: i) Meeting the diagnostic criteria of ‘The People's Republic of China health industry standard (WS 382-2012; http://www.nhc.gov.cn/wjw/s9494/201209/110b324c465740169a863d57a78c18a6.shtml)’. The clinical diagnosis can be established by any of (a), (b), (c), (d) plus (e) and excluding TB, lung tumor, non-infectious interstitial lung disease, pulmonary edema, pulmonary atelectasis, pulmonary embolism, pulmonary eosinophilic infiltrates, pulmonary vasculitis, etc. The elements of (a) to (e) are as follows: a) Newly developed cough and sputum, or aggravation of existing respiratory symptoms with purulent sputum, with or without chest pain; b) fever; c) solid lung signs and/or wet rales; d) peripheral blood WBC >10x10⁹/l or <4x10⁹/l with or without leftward nuclear shift; and e) chest X-ray showing new lamellar or patchy infiltrative shadows or interstitial changes with or without pleural effusion. ii) Sputum culture with a bacterial pathogenic basis or effective antimicrobial therapy, such as marked improvement in symptoms such as cough, yellow sputum and significant uptake of chest imaging. iii) Exclusion of viral, atypical pathogens, fungal, and Mtb infections. The inclusion criteria for IGRA⁺ TB participants were as follows: Meeting the diagnostic criteria for TB including positive sputum, bronchoscopic lavage, brush examination, smear microscopy of biopsy specimens for antacid bacilli, isolation and culture of Mtb, or positive nucleic acid test and IGRA for Mtb, or positive pathology of lung tissue biopsy. The inclusion criteria for AFB^- TB participants were as follows: There were no sputum or negative bacilli smear and negative Mtb, but there were chest computed tomographic (CT) scans or chest X-ray evidence and symptoms responding to TB treatment.

The exclusion criteria were as follows: i) Use of antimicrobial drugs for >24 h; (2) suffering from diseases that can affect the total number and classification of blood leukocytes, including leukemia, chronic inflammatory states, etc.; iii) having a recent (within 3 months) history of glucocorticoid application or ongoing hormone use; and iv) clearly diagnosed or highly suspected pulmonary edema, pulmonary embolism, pulmonary atelectasis, bronchial asthma, viral PN, fungal PN, atypical pathogenic PN, pulmonary eosinophilic infiltrates and lung cancer.

The involved participants were divided into two groups: The discovery cohort and the external validation cohort. For the discovery cohort, participants were enrolled at Ganzhou Fifth Hospital (Ganzhou, China) between August 2018 and August 2020, including 748 AFB^- TB participants and 531 PN participants. As the predefined goal was to assess the ability of laboratory biomarkers to distinguish IGRA⁺ patients presenting with AFB^- TB, 287 participants with IGRA⁺ TB were subjected for further analysis (AFB^- IGRA⁺ TB). Participants with >50% of laboratory data missing were excluded, thus making a total of 89 TB and 38 PN participants with recorded biomarker values used to construct the risk model. The external validation cohort of 134 participants in the study was collected from Shenzhen Third People's Hospital (Shenzhen, China) from June 2018 to June 2019 (Fig. 1). Among them, 15 were excluded due to missing variables, therefore the external validation cohort consisted of 77 AFB^- IGRA⁺ TB and 42 PN participants.

The study protocol was approved by the Ethics Committee and the Institutional Review Board of Ganzhou Fifth People's Hospital (registration no. 2020-10) to allow retrospective access to the records and files of patients. Written informed consent was waived by the Ethics Committee as this was an observational and retrospective analysis.

The medical records of all participants were reviewed by experienced TB clinicians, including medical history, symptoms, clinical signs, microbiological tests, laboratory findings, chest CT chest X-rays, and treatment measures. A total of 41 laboratory biomarkers were assessed by differential statistics and odds ratio (OR) calculation for variable selection.

For laboratory results, continuous variables were preprocessed by log₂-transformation before analysis. The laboratory data were verified for skewed distribution using the Kolmogorov-Smirnov test. In the present study Wilcoxon rank-sum test was suitable for skewed distribution data (16-18). P-values were adjusted by false discovery rate (FDR). Variables between two conditions were defined as statistically significant when FDR <0.2(19).

A univariate logistic model (glm) was used to calculate the OR for each laboratory biomarker. The regression coefficient of the glm was regarded as the log OR. Variables with an FDR <0.2 or at a statistically significant level (P-value <0.05) in the glm analysis were candidates for the construction of a multivariate risk model (lrm) and nomogram, and the final variables were determined using Akaike's information criterion (AIC) as a stopping rule. The goodness of fit of the lrm model was calculated using Hosmer and Lemeshow C statistics test. The performance of the nomogram was evaluated by the concordance index (C-index) and assessed by comparing nomogram-predicted vs. actual observation, and bootstrapping with 1,000 resamples to decrease the overfit bias was applied for calibration (20,21). The glm was generated by glmnet package (version 4.1-4) (22) and the nomogram was generated by DynNom package (version 5.0.2) (23). All analyses and figures were generated in R version 4.0.3 (https://www.r-project.org/).

The characteristics of the AFB^- IGRA⁺ TB and PN participants are shown in Table I. No significant differences in age and sex were found in the first cohort. Males made up the majority in both cohorts. Clinical types of TB participants included 162 infiltrative pulmonary TB, 19 cavitary pulmonary TB, 8 secondary pulmonary TB, and 15 tuberculous pleurisy and empyema. Only 44 and 23 participants had received TB-DNA and TB-antibody examination, yielding 9.09% (4/44) and 13.04% (3/23) positive rates, respectively.

The significant variables between the two conditions were further explored. Using unpaired t-test and setting FDR <0.2, 13 variables with marked differences were identified (Table II). Only uric acid (UA) was elevated (fold change >1.2). Notably, five variables, including aspartate aminotransferase (AST), total bile acid (TBA), triglyceride (TG), alanine aminotransferase (ALT), and glutamyl transpeptidase (GGT), were reduced (fold change <0.83) in TB compared with PN (Fig. 2A). These data revealed variable expression differences between the two conditions, and that some variables may be useful to distinguish AFB^- IGRA⁺ TB from PN.

To find the odds that TB would progress or not be given exposure to these laboratory variables, OR was assessed for each variable by univariate logistic model. Notably, 11 variables significantly associated with TB progression (P<0.05; Table II) were identified. Among them, four variables indicated a protective effect in TB progression (OR <1), including UA, red blood cell (RBC), Hb, and ALB; while seven variables including GLU, AST, TBA, TG, ALT, GGT, and ALP, were revealed as risk factors for TB progression (OR >1).

Combined with the aforementioned results, and using AIC as a stopping rule, five laboratory variables (age, UA, ALB, Hb, and WBC) were finally selected to develop a multivariate risk model with 89 AFB^- IGRA⁺ TB and 38 PN participants. Notably, the expression distribution of variables in both groups revealed more unbalance in PN than AFB^- IGRA⁺ TB (Fig. 2B). The risk model yielded a C-index of 0.7 (95% CI: 0.61, 0.8), with P=0.01 (chi-square test) (data not shown). The calibration plot revealed a moderate agreement between the model prediction and the actual observation (Fig. 3A; P=0.18, Hosmer-Lemeshow test). Using the nomogram, the values for each variable were mapped to points on a scale axis ranging from 0 to 10. With a corresponding number of points assigned to given magnitudes of the variables, the risk probability was calculated by the corresponding cumulative point score for all the variables (25). It was revealed that UA had the most protective effect in TB progression, followed by Hb; while age, WBC and ALB were shown to be risk factors (Fig. 3C).

Next, an external validation cohort of 134 participants, consisting of 77 AFB^- IGRA⁺ TB and 42 PN participants were prospectively collected. The C-index of nomogram for predicting the external cohort was 0.77 (95% CI: 0.68, 0.86) (data not shown). The calibration plot also revealed consistent results between the prediction by nomogram and actual observation (Fig. 3B) with P=0.13 (Hosmer-Lemeshow test).