Department of Medical Microbiology and Immunology, Diakonessenhuis Hospitalgrid.413681.9, Utrecht, The Netherlands.
Centre for Infectious Diseases Research, Diagnostics and Laboratory Surveillance, Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
Microbiol Spectr. 2022 Jun 29;10(3):e0006122. doi: 10.1128/spectrum.00061-22. Epub 2022 Jun 21.
Laboratory diagnosis of Lyme neuroborreliosis (LNB) is challenging, and validated diagnostic algorithms are lacking. Therefore, this retrospective cross-sectional study aimed to compare the diagnostic performance of seven commercial antibody assays for LNB diagnosis. Random forest (RF) modeling was conducted to investigate whether the diagnostic performance using the antibody assays could be improved by including several routine cerebrospinal fluid (CSF) parameters (i.e., leukocyte count, total protein, blood-CSF barrier functionality, and intrathecal total antibody synthesis), two-tier serology on serum, the CSF level of the B-cell chemokine (C-X-C motif) ligand 13 (CXCL13), and a species PCR on CSF. In total, 156 patients were included who were classified as definite LNB ( = 10), possible LNB ( = 7), or non-LNB patient ( = 139) according to the criteria of the European Federation of Neurological Societies using a consensus strategy for intrathecal -specific antibody synthesis. The seven antibody assays showed sensitivities ranging from 47.1% to 100% and specificities ranging from 95.7% to 100%. RF modeling demonstrated that the sensitivities of most antibody assays could be improved by including other parameters to the diagnostic repertoire for diagnosing LNB (range: 94.1% to 100%), although with slightly lower specificities (range: 92.8% to 96.4%). The most important parameters for LNB diagnosis are the detection of intrathecally produced specific antibodies, two-tier serology on serum, CSF-CXCL13, Reibergram classification, and pleocytosis. In conclusion, this study shows that LNB diagnosis is best supported using multiparameter analysis. Furthermore, a collaborative prospective study is proposed to investigate if a standardized diagnostic algorithm can be developed for improved LNB diagnosis. The diagnosis of LNB is established by clinical symptoms, pleocytosis, and proof of intrathecal synthesis of -specific antibodies. Laboratory diagnosis of LNB is challenging, and validated diagnostic algorithms are lacking. Therefore, this retrospective cross-sectional study aimed to compare the diagnostic performance of seven commercial antibody assays for LNB diagnosis. Multiparameter analysis was conducted to investigate whether the diagnostic performance using the antibody assays could be improved by including several routine (CSF) parameters. The results of this study show that LNB diagnosis is best supported using the detection of intrathecally produced -specific antibodies, two-tier serology on serum, CSF-CXCL13, Reibergram classification, and pleocytosis. Furthermore, we propose a collaborative prospective study to investigate the potential role of constructing a diagnostic algorithm using multiparameter analysis for improved LNB diagnosis.
实验室诊断莱姆神经Borreliosis(LNB)具有挑战性,并且缺乏经过验证的诊断算法。因此,本回顾性横断面研究旨在比较七种商业抗体检测试剂盒在 LNB 诊断中的诊断性能。进行随机森林(RF)建模,以调查通过包括几种常规脑脊液(CSF)参数(即白细胞计数、总蛋白、血脑屏障功能和鞘内总抗体合成)、血清的两阶段血清学、B 细胞趋化因子(C-X-C 基序)配体 13(CXCL13)的 CSF 水平和 CSF 中的物种 PCR,是否可以改善使用抗体检测试剂盒的诊断性能。总共纳入了 156 名患者,根据欧洲神经病学会联合会的标准,使用鞘内特异性抗体合成的共识策略,将这些患者分为明确的 LNB(= 10)、可能的 LNB(= 7)或非 LNB 患者(= 139)。七种抗体检测试剂盒的敏感性范围为 47.1%至 100%,特异性范围为 95.7%至 100%。RF 建模表明,通过将其他参数纳入诊断方案,大多数抗体检测试剂盒的敏感性可以提高用于诊断 LNB(范围:94.1%至 100%),尽管特异性略低(范围:92.8%至 96.4%)。用于 LNB 诊断的最重要参数是检测鞘内产生的特异性抗体、血清的两阶段血清学、CSF-CXCL13、Reibergram 分类和白细胞增多。总之,本研究表明,LNB 诊断最好通过多参数分析来支持。此外,提出了一项合作前瞻性研究,以调查是否可以开发标准化的诊断算法来改善 LNB 诊断。LNB 的诊断是通过临床症状、白细胞增多和证明鞘内产生 -特异性抗体来建立的。实验室诊断 LNB 具有挑战性,并且缺乏经过验证的诊断算法。因此,本回顾性横断面研究旨在比较七种商业抗体检测试剂盒在 LNB 诊断中的诊断性能。进行多参数分析,以调查通过包括几种常规(CSF)参数是否可以改善使用抗体检测试剂盒的诊断性能。这项研究的结果表明,LNB 诊断最好通过检测鞘内产生的 -特异性抗体、血清的两阶段血清学、CSF-CXCL13、Reibergram 分类和白细胞增多来支持。此外,我们提出了一项合作前瞻性研究,以调查使用多参数分析构建诊断算法对改善 LNB 诊断的潜在作用。