Department of Mathematics & Statistics, Mount Holyoke College, South Hadley, Massachusetts, United States of America.
Department of Biostatistics, University of Iowa College of Public Health, Iowa City, Iowa, United States of America.
PLoS Negl Trop Dis. 2022 Mar 14;16(3):e0010236. doi: 10.1371/journal.pntd.0010236. eCollection 2022 Mar.
Like many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling has been proposed to estimate a latent gold standard for identifying disease. These proposed models for VL have leveraged information from diagnostic tests with dichotomous serological and PCR assays, but have not employed continuous diagnostic test information.
METHODS/PRINCIPAL FINDINGS: In this paper, we employ Bayesian latent class models to improve the identification of canine visceral leishmaniasis using the dichotomous PCR assay and the Dual Path Platform (DPP) serology test. The DPP test has historically been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Using data collected from a cohort of hunting dogs across the United States, which were identified as having either negative or symptomatic disease, we evaluate the impact of including numerical DPP reader information as a proxy for immune response. We find that inclusion of DPP reader information allows us to illustrate changes in immune response as a function of age.
CONCLUSIONS/SIGNIFICANCE: Utilization of continuous DPP reader information can improve the correct discrimination between individuals that are negative for disease and those with clinical VL. These models provide a promising avenue for diagnostic testing in contexts with multiple, imperfect diagnostic tests. Specifically, they can easily be applied to human visceral leishmaniasis when diagnostic test results are available. Also, appropriate diagnosis of canine visceral leishmaniasis has important consequences for curtailing spread of disease to humans.
与许多传染病一样，临床内脏利什曼病（VL）的诊断尚无实用的金标准。已经提出了潜在类别建模来估计用于识别疾病的潜在金标准。这些针对 VL 的拟议模型利用了来自具有二分类血清学和 PCR 检测的诊断测试的信息，但并未利用连续的诊断测试信息。
方法/主要发现：在本文中，我们使用贝叶斯潜在类别模型来改善使用二分类 PCR 检测和双路径平台（DPP）血清学检测对犬内脏利什曼病的识别。DPP 检测历来被用作二分类检测，但也可以通过 DPP 读取器提供数值信息。利用从美国各地的一群猎犬中收集的数据，这些猎犬被确定为具有阴性或有症状的疾病，我们评估了包含数值 DPP 读取器信息作为免疫反应替代物的影响。我们发现，包含 DPP 读取器信息使我们能够说明免疫反应随年龄变化的情况。
结论/意义：利用连续的 DPP 读取器信息可以改善对无疾病个体和具有临床 VL 个体的正确区分。这些模型为具有多个不完善诊断测试的情况下的诊断测试提供了有前途的途径。具体来说，当有诊断测试结果时，它们可以很容易地应用于人类内脏利什曼病。此外，对犬内脏利什曼病的适当诊断对遏制疾病向人类传播具有重要意义。
