摘要
目的分析涂阴肺结核患者的临床特征,建立涂阴肺结核的诊断预测模型。方法选取2018年12月-2021年1月于深圳市宝安区松岗人民医院住院治疗,且3次痰抗酸染色涂片为阴性的疑似肺结核患者118例,根据其最终诊断划分为结核组(n=68)与非结核组(n=50),收集患者的一般信息、症状、结核感染T细胞检测(T-SPOT.TB)、支气管镜肺泡灌洗液(BALF)抗酸染色涂片、BALF PCR-荧光法、结核分枝杆菌及利福平耐药实时荧光定量核酸扩增检测(Gene Xpert MTB/RIF)等临床资料,使用logistic回归分析涂阴肺结核临床特征,并根据回归结果构建列线图预测模型。结果单因素分析结果显示,结核组与非结核组年龄、体重、血沉、BALF PCR-荧光法、BALF Gene Xpert MTB/RIF、T-SPOT.TB、发热和咳嗽差异有统计学意义(P均<0.05)。多因素logistic回归分析结果显示,涂阴肺结核患者呈现为低体重、BALF PCR-荧光法(OR=25.887,95%CI:1.826~367.005)和Gene Xpert MTB/RIF阳性(OR=30.553,95%CI:2.078~449.199)、T-SPOT.TB阳性(OR=53.739,95%CI:4.547~635.121)及无咳嗽症状(OR=0.049,95%CI:0.004~0.599)。列线图预测模型的曲线下面积(AUC)为0.973,校准曲线基本与对角线重合,模型的预测值与实际观测值之间存在良好的一致性。采用Bootstrap自抽样法对模型进行内部验证后得到模型的AUC和C-index均为0.955。DCA曲线显示预测模型的临床获益性始终高于“全干预”和“全不干预”,模型具有良好的临床适用性。结论基于体重、BALF PCR-荧光法、BALF Gene Xpert MTB/RIF、T-SPOT.TB和咳嗽症状构建的涂阴肺结核诊断预测模型具有良好的区分度和准确度,可为临床鉴别涂阴肺结核提供参考。
Objective To analyze the clinical characteristics of smear-negative pulmonary tuberculosis and establish a prediction model.Methods A total of 118 patients with suspected pulmonary tuberculosis and sputum acid-fast staining smear negative for three times hospitalized in the Shenzhen Bao'an District Songgang People's Hospital from December 2018 to January 2021 were selected and divided into tuberculosis group(n=68)and non-tuberculosis group(n=50)according to final diagnosis.Clinical data including general information,symtoms,tuberculosis-specific enzyme-linked immunospot assay(T-SPOT.TB)results of bronchoalveolar lavage fluid(BALF)acid-fast staining smear,real-time PCR and gene Xpert Mycobacterium tuberculosis/Rifampin(Gene Xpert MTB/RIF)were collected.Logistic regression analysis was performed to analyze the clinical characteristics of smear-negative pulmonary tuberculosis and a prediction model was constructed based on the regression results.Results Based on the univariate analysis,significant differences were found in age,weight,erythrocyte sedimentation rate,BALF real-time PCR,BALF Gene Xpert MTB/RIF,T-SPOT.TB,fever and cough symptoms between the two groups.Multivariate logistic regression analysis showed that smear-negative pulmonary tuberculosis presented with low weight,positive BALF real-time PCR(OR=25.887,95%CI:1.826-367.005)and Gene Xpert MTB/RIF(OR=30.553,95%CI:2.078-449.199),positive T-SPOT.TB(OR=53.739,95%CI:4.547-635.121)and no cough symptom(OR=0.049,95%CI:0.004-0.599).The AUC and C-index of the model were 0.955 after internal validation of the model by bootstrap confidence analysis.The AUC of the nomogram prediction model was 0.973;the calibration curve basically coincided with the diagonal line,and there was good consistency between the predicted value of the model and the actual observed value.The DCA curve showed that the clinical benefit of the predictive model was always higher than that of“full intervention”and“no intervention”,showing that the model had good clinical applicability.Con
作者
曾冠盛
陈丽嫦
陈辉
刘文毅
杨泽文
ZENG Guansheng;CHEN Lichang;CHEN Hui;LIU Wenyi;YANG Zewen(Department of Pulmonary and Critical Care Medicine,Shenzhen Hospital,Southern Medical University,Shenzhen,Guangdong 518101,China;Department of Pulmonary and Critical Care Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,Guangdong 510260,China;Department of Pulmonary and Critical Care Medicine,Shenzhen Bao'an District Songgang People's Hospital,Shenzhen,Guangdong 518105,China;Central Laboratory,Shenzhen Bao'an District Songgang People's Hospital,Shenzhen,Guangdong 518105,China)
出处
《热带医学杂志》
CAS
2024年第1期59-64,共6页
Journal of Tropical Medicine
基金
深圳市宝安区科技计划——基础研究项目(医疗卫生类)(2021JD104)
关键词
涂阴
肺结核
列线图
Smear negative
Tuberculosis
Nomogram model
