LncRNA and predictive model to improve the diagnosis of clinically diagnosed pulmonary tuberculosis.

Background Clinically diagnosed pulmonary tuberculosis (PTB) patients lack Mycobacterium tuberculosis (MTB) microbiologic evidence, and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of lncRNAs and corresponding predictive models to diagnose these patients. Methods We enrolled 1764 subjects, including clinically diagnosed PTB patients, microbiologically-confirmed PTB cases, non-TB disease controls and healthy controls, in three cohorts (Screening, Selection and Validation). Candidate lncRNAs differentially expressed in blood samples of the PTB and healthy control groups were identified by microarray and qRT-PCR in the Screening Cohort. Logistic regression models were developed using lncRNAs and/or electronic health records (EHRs) from clinically diagnosed PTB patients and non-TB disease controls in the Selection Cohort. These models were evaluated by AUC and decision curve analysis, and the optimal model was presented as a Web-based nomogram, which was evaluated in the Validation Cohort. Results Three differentially expressed lncRNAs (ENST00000497872, n333737, n335265) were identified. The optimal model (i.e., nomogram) incorporated these three lncRNAs and six EHRs (age, hemoglobin, weight loss, low-grade fever, CT calcification and TB-IGRA). The nomogram showed an AUC of 0.89, sensitivity of 0.86 and specificity of 0.82 in differentiating clinically diagnosed PTB from non-TB disease controls of the Validation Cohort, which demonstrated better discrimination and clinical net benefit than the EHR model. The nomogram also had a discriminative power (AUC: 0.90, sensitivity 0.85, specificity 0.81) in identifying microbiologically-confirmed PTB patients. Conclusions LncRNAs and the user-friendly nomogram could facilitate the early identification of PTB cases among suspected patients with negative MTB microbiologic evidence.