Development of a Nomogram Based on Transcriptional Signatures, IFN-γ Response and Neutrophils for Diagnosis of Tuberculosis

Abstract

Yan-Hua Liu,1,* Jin-Wen Su,2,* Jing Jiang,3 Bing-Fen Yang,1 Zhi-Hong Cao,1 Fei Zhai,1 Wen-Na Sun,1 Ling-Xia Zhang,1 Xiao-Xing Cheng1 1Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Institute of Tuberculosis Research, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, People’s Republic of China; 2Division of Critical Care Medicine, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, People’s Republic of China; 3Institute of Research, Beijing Key Laboratory of Organ Transplantation and Immune Regulation, Senior Department of Respiratory and Critical Care Medicine, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao-Xing Cheng; Ling-Xia Zhang, Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Institute of Tuberculosis Research, Senior Department of Tuberculosis, Eighth Medical Center of PLA General Hospital, 17 hei Shan Hu Road, Haidian, Beijing, 100091, People’s Republic of China, Fax [phone], Email [email protected]; [email protected]: Tuberculosis (TB) is a major global health threat and its diagnosis remains challenging. This study aimed to develop a nomogram that incorporated peripheral blood transcriptional signatures and other blood tests for the diagnosis of tuberculosis.Patients and Methods: Patients with TB, patients with other definite pulmonary diseases (OPD), individuals with latent tuberculosis infection (LTBI), and healthy controls (HC) were retrospectively enrolled between May 2017 and April 2018. The results of the interferon-γ release assay (IGRA) and blood counts were obtained from medical records, and the transcripts of 10 genes were detected using reverse transcription polymerase chain reaction (RT-PCR). Variable selection was performed using least absolute shrinkage and selection operator regression (LASSO) and multivariate logistic regression was performed for the optimal prediction model with backward direction. The model was displayed as a nomogram, and its performance was evaluated for discrimination ability, calibration ability, and clinical usefulness. Internal validation of the prediction model was conducted using bootstrap resampling.Results: A total of 185 participants were enrolled, including 84 patients with TB and 101 controls. A prediction nomogram composed of IGRA, percentage of neutrophils, and expression levels of CD64, granzyme A (GZMA), and PR/SET domain 1 (PRDM1) was established. The nomogram demonstrated good discrimination, with an unadjusted area under the curve (AUC) of 0.914 (95% CI: 0.875– 0.954) and a bootstrap-corrected AUC of 0.914 (95% CI: 0.874– 0.947). With a cutoff value of 0.519, the sensitivity and specificity for discriminating PTB from controls were 0.81 and 0.871, respectively. The nomogram also showed good calibration with the Hosmer–Lemeshow test (P=0.58) and good clinical practicality displayed by the decision curve analysis.Conclusion: A nomogram composed of IGRA, percentage of neutrophils, and expression of CD64, GZMA, and PRDM1 was established. The nomogram demonstrated a sensitivity and specificity of 81% and 87%, respectively, for differentiating TB from controls.Keywords: tuberculosis, prediction model, peripheral blood, interferon gamma release assay, transcripts of gene

Keywords

• tuberculosis
• prediction model
• peripheral blood
• interferon gamma release assay
• transcripts of gene