Gomez-Buendia Alberto, Pozo Pilar, Picasso-Risso Catalina, Branscum Adam, Perez Andres, Alvarez Julio
VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid, Spain.
Veterinary Population Medicine Department, University of Minnesota, St Paul, Minnesota, USA.
Transbound Emerg Dis. 2023 Feb 21;2023:7615716. doi: 10.1155/2023/7615716. eCollection 2023.
The last decades have seen major efforts to develop new and improved tools to maximize our ability to detect tuberculosis-infected animals and advance towards the objective of disease control and ultimately eradication. Nevertheless, there is still uncertainty regarding test performance due to the wide range of specificity and especially sensitivity estimates published in the scientific literature. Here, we performed a systematic review of the literature on studies that evaluated the performance of tuberculosis diagnostic tests used in animals through Bayesian Latent Class Models (BLCMs), which do not require the application of a (fallible) reference procedure to classify animals as infected with tuberculosis or not. BLCM-based sensitivity and specificity estimates deviated from those obtained using a reference procedure for certain antemortem tests: an overall lower sensitivity of skin tests and serology and a higher sensitivity of interferon-gamma (IFN-) assays was reported. In the case of postmortem diagnostic tests, sensitivity estimates from BLCMs were similar to estimates from studies based on other methodologies. For specificity, the range of BLCM-based estimates was narrower than those based on a reference test, reaching values close to 100% (but lower in the case of IFN- assays). In conclusion, Bayesian methods have been increasingly applied for the evaluation of tuberculosis diagnostic tests in animals, yielding results that differ (sometimes substantially) from previously reported test performance in the literature, particularly for in vivo tests and sensitivity estimates. Newly developed models that allow adjustment for relevant factors (e.g., age, breed, region, and herd size) can contribute to the generation of more unbiased estimates of test performance. Nevertheless, although BLCMs for tuberculosis do not require the use of an imperfect reference procedure and are therefore not influenced by its limited performance, they require careful implementation, and transparent systematic reporting should be the norm.
在过去几十年里，人们付出了巨大努力来开发新的和改进的工具，以最大限度地提高我们检测感染结核病动物的能力，并朝着疾病控制乃至最终根除的目标迈进。然而，由于科学文献中发表的特异性范围广泛，尤其是敏感性估计值，测试性能仍存在不确定性。在这里，我们通过贝叶斯潜在类别模型（BLCMs）对评估动物结核病诊断测试性能的研究文献进行了系统综述，该模型不需要应用（可能有误的）参考程序来将动物分类为是否感染结核病。基于BLCMs的敏感性和特异性估计值与某些生前测试使用参考程序获得的估计值有所偏差：皮肤测试和血清学的总体敏感性较低，而干扰素-γ（IFN-）检测的敏感性较高。对于死后诊断测试，基于BLCMs的敏感性估计值与基于其他方法的研究估计值相似。对于特异性，基于BLCMs的估计值范围比基于参考测试的范围更窄，接近100%（但IFN-检测的情况较低）。总之，贝叶斯方法越来越多地应用于评估动物结核病诊断测试，其结果与文献中先前报道的测试性能有所不同（有时差异很大），特别是对于体内测试和敏感性估计。新开发的允许对相关因素（如年龄、品种、地区和畜群规模）进行调整的模型有助于生成更无偏的测试性能估计值。然而，尽管用于结核病的BLCMs不需要使用不完善的参考程序，因此不受其有限性能的影响，但它们需要仔细实施，透明的系统报告应该成为常态。
