Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland.
Bacteriology Laboratory, Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, 4 Rue Gabrielle-Perret-Gentil, 1211, Geneva 14, Switzerland.
Eur J Clin Microbiol Infect Dis. 2022 Oct;41(10):1227-1235. doi: 10.1007/s10096-022-04488-3. Epub 2022 Sep 1.
The purpose of this study is to identify predictive factors associated with missed diagnosis of B. pertussis-B. holmesii co-infection by assessing the analytical performance of a commercially available multiplexed PCR assay and by building a prediction model based on clinical signs and symptoms for detecting co-infections. This is a retrospective study on the electronic health records of all clinical samples that tested positive to either B. pertussis or B. holmesii from January 2015 to January 2018 at Geneva University Hospitals. Multivariate logistic regression was used to build a model for co-infection prediction based on the electronic health record chart review. Limit of detection was determined for all targets of the commercial multiplexed PCR assay used on respiratory samples. A regression model, developed from clinical symptoms and signs, predicted B. pertussis and B. holmesii co-infection with an accuracy of 82.9% (95% CI 67.9-92.8%, p value = .012), for respiratory samples positive with any of the two tested Bordetella species. We found that the LOD of the PCR reaction targeting ptxS1 is higher than that reported by the manufacturer by a factor 10. The current testing strategy misses B. pertussis and B. holmesii co-infections by reporting only B. holmesii infections. Thus, we advocate to perform serological testing for detecting a response against pertussis toxin whenever a sample is found positive for B. holmesii. These findings are important, both from a clinical and epidemiological point of view, as the former impacts the choice of antimicrobial drugs and the latter biases surveillance data, by underestimating B. pertussis infections during co-infections.
本研究旨在通过评估商业上可用的多重 PCR 检测方法的分析性能，并通过建立基于临床症状和体征的预测模型来检测合并感染，从而确定与百日咳博德特氏菌-霍氏博德特氏菌合并感染漏诊相关的预测因素。这是一项回顾性研究，研究对象为 2015 年 1 月至 2018 年 1 月期间在日内瓦大学附属医院检测出百日咳博德特氏菌或霍氏博德特氏菌阳性的所有临床样本的电子健康记录。采用多变量逻辑回归方法，根据电子健康记录图表回顾，建立合并感染预测模型。确定了用于呼吸道样本的商业多重 PCR 检测方法中所有靶标的检测限。从临床症状和体征中开发的回归模型预测了 82.9%（95%CI 67.9-92.8%，p 值 = .012）的百日咳博德特氏菌和霍氏博德特氏菌合并感染，这些样本均为两种测试的博德特氏菌中的任何一种阳性的呼吸道样本。我们发现，针对 ptxS1 的 PCR 反应的检测限比制造商报告的检测限高 10 倍。目前的检测策略仅报告霍氏博德特氏菌感染，从而错过了百日咳博德特氏菌和霍氏博德特氏菌的合并感染。因此，我们建议在检测到霍氏博德特氏菌阳性的样本时，应进行血清学检测，以检测针对百日咳毒素的反应。这些发现从临床和流行病学的角度来看都很重要，因为前者会影响抗生素药物的选择，后者会通过低估合并感染期间的百日咳博德特氏菌感染，从而影响监测数据。
