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基于转录特征、γ干扰素反应和中性粒细胞的列线图用于结核病诊断的研究

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

作者信息

Liu Yan-Hua, Su Jin-Wen, Jiang Jing, Yang Bing-Fen, Cao Zhi-Hong, Zhai Fei, Sun Wen-Na, Zhang Ling-Xia, Cheng Xiao-Xing

机构信息

Beijing 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.

Division of Critical Care Medicine, Senior Department of Tuberculosis, the Eighth Medical Center of PLA General Hospital, Beijing, 100091, People's Republic of China.

出版信息

J Inflamm Res. 2024 Nov 13;17:8799-8811. doi: 10.2147/JIR.S480173. eCollection 2024.

Abstract

PURPOSE

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.

摘要

目的

结核病是全球主要的健康威胁,其诊断仍具有挑战性。本研究旨在开发一种整合外周血转录特征和其他血液检测指标的列线图,用于结核病的诊断。

患者与方法

回顾性纳入2017年5月至2018年4月期间的结核病患者、其他明确肺部疾病(OPD)患者、潜伏性结核感染(LTBI)个体和健康对照(HC)。从病历中获取干扰素-γ释放试验(IGRA)结果和血细胞计数,并使用逆转录聚合酶链反应(RT-PCR)检测10个基因的转录本。采用最小绝对收缩和选择算子回归(LASSO)进行变量选择,并进行多因素逻辑回归以建立具有向后方向的最优预测模型。该模型以列线图形式展示,并对其判别能力、校准能力和临床实用性进行评估。使用自助重采样对预测模型进行内部验证。

结果

共纳入185名参与者,包括84名结核病患者和101名对照。建立了由IGRA、中性粒细胞百分比以及CD64、颗粒酶A(GZMA)和PR/SET结构域1(PRDM1)表达水平组成的预测列线图。该列线图显示出良好的判别能力,未调整的曲线下面积(AUC)为0.914(95%CI:(0.875 - 0.954)),经自助校正后的AUC为0.914(95%CI:(0.874 - 0.947))。截断值为0.519时,区分肺结核与对照的敏感性和特异性分别为0.81和0.871。列线图在Hosmer-Lemeshow检验中也显示出良好的校准((P = 0.58)),决策曲线分析显示出良好的临床实用性。

结论

建立了由IGRA、中性粒细胞百分比以及CD64、GZMA和PRDM1表达组成的列线图。该列线图区分结核病与对照的敏感性和特异性分别为81%和87%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec74/11570532/933c563bb6fd/JIR-17-8799-g0001.jpg

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