PDF(Pubmed)
Abstract：
UNASSIGNED: The insufficient understanding and misdiagnosis of clinically diagnosed pulmonary tuberculosis (PTB) without an aetiological evidence is a major problem in the diagnosis of tuberculosis (TB). This study aims to confirm the value of Long non-coding RNA (lncRNA) n344917 in the diagnosis of PTB and construct a rapid, accurate, and universal prediction model.
UNASSIGNED: A total of 536 patients were prospectively and consecutively recruited, including clinically diagnosed PTB, PTB with an aetiological evidence and non-TB disease controls, who were admitted to West China hospital from Dec 2014 to Dec 2017. The expression levels of lncRNA n344917 of all patients were analyzed using reverse transcriptase quantitative real-time PCR. Then, the laboratory findings, electronic health record (EHR) information and expression levels of n344917 were used to construct a prediction model through the Least Absolute Shrinkage and Selection Operator algorithm and multivariate logistic regression.
UNASSIGNED: The factors of n344917, age, CT calcification, cough, TBIGRA, low-grade fever and weight loss were included in the prediction model. It had good discrimination (area under the curve = 0.88, cutoff = 0.657, sensitivity = 88.98%, specificity = 86.43%, positive predictive value = 85.61%, and negative predictive value = 89.63%), consistency and clinical availability. It also showed a good replicability in the validation cohort. Finally, it was encapsulated as an open-source and free web-based application for clinical use and is available online at https://ziruinptb.shinyapps.io/shiny/.
UNASSIGNED: Combining the novel potential molecular biomarker n344917, laboratory and EHR variables, this web-based prediction model could serve as a user-friendly, accurate platform to improve the clinical diagnosis of PTB.
UNASSIGNED: A total of 536 patients were prospectively and consecutively recruited, including clinically diagnosed PTB, PTB with an aetiological evidence and non-TB disease controls, who were admitted to West China hospital from Dec 2014 to Dec 2017. The expression levels of lncRNA n344917 of all patients were analyzed using reverse transcriptase quantitative real-time PCR. Then, the laboratory findings, electronic health record (EHR) information and expression levels of n344917 were used to construct a prediction model through the Least Absolute Shrinkage and Selection Operator algorithm and multivariate logistic regression.
UNASSIGNED: The factors of n344917, age, CT calcification, cough, TBIGRA, low-grade fever and weight loss were included in the prediction model. It had good discrimination (area under the curve = 0.88, cutoff = 0.657, sensitivity = 88.98%, specificity = 86.43%, positive predictive value = 85.61%, and negative predictive value = 89.63%), consistency and clinical availability. It also showed a good replicability in the validation cohort. Finally, it was encapsulated as an open-source and free web-based application for clinical use and is available online at https://ziruinptb.shinyapps.io/shiny/.
UNASSIGNED: Combining the novel potential molecular biomarker n344917, laboratory and EHR variables, this web-based prediction model could serve as a user-friendly, accurate platform to improve the clinical diagnosis of PTB.
摘要：
■在没有病因证据的情况下，对临床诊断的肺结核（PTB）的认识不足和误诊是结核病（TB）诊断中的主要问题。本研究旨在证实长链非编码RNA(lncRNA)n344917在PTB诊断中的价值,准确,和通用预测模型。
■前瞻性和连续招募了536名患者，包括临床诊断的PTB，有病因学证据和非结核病控制的PTB，他们于2014年12月至2017年12月入住华西医院。使用逆转录酶定量实时PCR分析所有患者的lncRNAn344917的表达水平。然后，实验室的发现,电子健康记录(EHR)信息和n344917的表达水平被用来通过最小绝对收缩和选择算子算法和多变量逻辑回归构建预测模型。
■n344917的因素，年龄，CT钙化,咳嗽,TBIGRA,低热和体重减轻包括在预测模型中.具有良好的辨别性（曲线下面积=0.88，截止值=0.657，灵敏度=88.98％，特异性=86.43%，阳性预测值=85.61%，阴性预测值=89.63%)，一致性和临床可用性。它在验证队列中也显示出良好的可复制性。最后,它被封装为一个开源和免费的基于Web的应用程序，用于临床使用，并可在https://ziruinptb在线获得。shinyapps.io/闪亮/。
■结合新的潜在分子生物标志物n344917，实验室和EHR变量，这种基于网络的预测模型可以作为一个用户友好的，准确的平台，提高PTB的临床诊断。
■前瞻性和连续招募了536名患者，包括临床诊断的PTB，有病因学证据和非结核病控制的PTB，他们于2014年12月至2017年12月入住华西医院。使用逆转录酶定量实时PCR分析所有患者的lncRNAn344917的表达水平。然后，实验室的发现,电子健康记录(EHR)信息和n344917的表达水平被用来通过最小绝对收缩和选择算子算法和多变量逻辑回归构建预测模型。
■n344917的因素，年龄，CT钙化,咳嗽,TBIGRA,低热和体重减轻包括在预测模型中.具有良好的辨别性（曲线下面积=0.88，截止值=0.657，灵敏度=88.98％，特异性=86.43%，阳性预测值=85.61%，阴性预测值=89.63%)，一致性和临床可用性。它在验证队列中也显示出良好的可复制性。最后,它被封装为一个开源和免费的基于Web的应用程序，用于临床使用，并可在https://ziruinptb在线获得。shinyapps.io/闪亮/。
■结合新的潜在分子生物标志物n344917，实验室和EHR变量，这种基于网络的预测模型可以作为一个用户友好的，准确的平台，提高PTB的临床诊断。
