MacLean Emily L, Kohli Mikashmi, Köppel Lisa, Schiller Ian, Sharma Surendra K, Pai Madhukar, Denkinger Claudia M, Dendukuri Nandini
McGill International TB Centre, Research Institute of the McGill University Health Centre, Montréal, Canada.
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Canada.
Diagn Progn Res. 2022 Jun 16;6(1):11. doi: 10.1186/s41512-022-00125-x.
Evaluating the accuracy of extrapulmonary tuberculosis (TB) tests is challenging due to lack of a gold standard. Latent class analysis (LCA), a statistical modeling approach, can adjust for reference tests' imperfect accuracies to produce less biased test accuracy estimates than those produced by commonly used methods like composite reference standards (CRSs). Our objective is to illustrate how Bayesian LCA can address the problem of an unavailable gold standard and demonstrate how it compares to using CRSs for extrapulmonary TB tests.
We re-analyzed a dataset of presumptive extrapulmonary TB cases in New Delhi, India, for three forms of extrapulmonary TB. Results were available for culture, smear microscopy, Xpert MTB/RIF, and a non-microbiological test, cytopathology/histopathology, or adenosine deaminase (ADA). A diagram was used to define assumed relationships between observed tests and underlying latent variables in the Bayesian LCA with input from an inter-disciplinary team. We compared the results to estimates obtained from a sequence of CRSs defined by increasing numbers of positive reference tests necessary for positive disease status.
Data were available from 298, 388, and 230 individuals with presumptive TB lymphadenitis, meningitis, and pleuritis, respectively. Using Bayesian LCA, estimates were obtained for accuracy of all tests and for extrapulmonary TB prevalence. Xpert sensitivity neared that of culture for TB lymphadenitis and meningitis but was lower for TB pleuritis, and specificities of all microbiological tests approached 100%. Non-microbiological tests' sensitivities were high, but specificities were only moderate, preventing disease rule-in. CRSs' only provided estimates of Xpert and these varied widely per CRS definition. Accuracy of the CRSs also varied by definition, and no CRS was 100% accurate.
Unlike CRSs, Bayesian LCA takes into account known information about test performance resulting in accuracy estimates that are easier to interpret. LCA should receive greater consideration for evaluating extrapulmonary TB diagnostic tests.
由于缺乏金标准，评估肺外结核病（TB）检测的准确性具有挑战性。潜在类别分析（LCA）是一种统计建模方法，它可以针对参考检测的不完美准确性进行调整，从而产生比复合参考标准（CRS）等常用方法产生的偏差更小的检测准确性估计值。我们的目的是说明贝叶斯LCA如何解决缺乏金标准的问题，并展示它与使用CRS进行肺外TB检测的比较情况。
我们重新分析了印度新德里疑似肺外TB病例的数据集，涉及三种肺外TB形式。可获得培养、涂片显微镜检查、Xpert MTB/RIF以及一种非微生物检测（细胞病理学/组织病理学或腺苷脱氨酶（ADA））的结果。使用一个图表来定义贝叶斯LCA中观察到的检测与潜在潜在变量之间的假定关系，并得到了一个跨学科团队的输入。我们将结果与通过增加疾病阳性状态所需的阳性参考检测数量定义的一系列CRS获得的估计值进行了比较。
分别有298、388和230名疑似TB淋巴结炎、脑膜炎和胸膜炎的个体的数据。使用贝叶斯LCA，获得了所有检测的准确性以及肺外TB患病率的估计值。Xpert对TB淋巴结炎和脑膜炎的敏感性接近培养的敏感性，但对TB胸膜炎的敏感性较低，并且所有微生物检测的特异性接近100%。非微生物检测的敏感性较高，但特异性仅为中等，无法确诊疾病。CRS仅提供了Xpert的估计值，并且每个CRS定义的估计值差异很大。CRS的准确性也因定义而异，没有一个CRS是100%准确的。
与CRS不同，贝叶斯LCA考虑了关于检测性能的已知信息，从而产生更易于解释的准确性估计值。在评估肺外TB诊断检测时，LCA应得到更多考虑。
