Multicenter Studies to Evaluate the Performance of MALDI-TOF MS for Fungal Identification

评估 MALDI-TOF MS 真菌鉴定性能的多中心研究

• 批准号: 10019272
• 负责人: Anna Lau
• 金额: --
• 依托单位: CLINICAL CENTER
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10019272
• 关键词: Antifungal Therapy; Bacteria; Characteristics; Clinical; Clinical Microbiology; Computer software; Consumption; Controlled Study; DNA sequencing; Data; Databases; Detection; Diagnosis; Diagnostic; Evaluation; Geographic Locations; Growth; Individual; International; Laboratories; MALDI-TOF Mass Spectrometry; Manufacturer Name; Methods; Microbiology; Molds; Multicenter Studies; Mycoses; Performance; Phenotype; Procedures; Proteins; Publications; Reproducibility; Technology; Time; United States National Institutes of Health; Update; Yeasts; frontier; fungus; improved; instrument; mass spectrometer; targeted treatment

Traditional identification of molds is challenging, cumbersome, and time-consuming, relying on non-specific phenotypic characteristics from culture or expensive and specialized DNA sequencing methods. MALDI-TOF MS has revolutionized clinical microbiology laboratories worldwide, enabling rapid identification of bacteria and yeast. This technology, however, has not been readily applied to mold identification due to technical challenges in developing an efficient protein extraction method and the limited availability of clinically comprehensive databases. Recently, we developed and optimized a simple protein extraction procedure for molds and constructed a highly stringent database comprising 410 individual isolates representing 90 genera and 220 species for the rapid identification of molds by MALDI-TOF MS. The NIH Mold Database is the most comprehensive database developed to date for mold identification and has filled a huge diagnostic gap in the field of clinical microbiology. The NIH Mold Database has now been shared with >100 clinical microbiology laboratories worldwide in an effort to improve the diagnosis of invasive fungal infections by providing faster identification (within 30 minutes of growth detection as opposed to weeks) that in turn can guide optimal antifungal therapy. During this fiscal year, we completed a ten-center national controlled study that evaluated the clinical performance and accuracy of two fungal databases (the NIH Mold Database and the Bruker Fungi Database) against 80 clinical mold isolates has come to a close. This study has highlighted important (previously undetermined) factors such as spectral acquisition settings required to improve and achieve successful/uniform implementation of MALDI-TOF MS for routine mold identification in clinical microbiology laboratories. In fact, in August 2019, the manufacturer of the mass spectrometer (Bruker Daltonics) released updated software with specific changes to acquisition parameters to improve mold identification. Data regarding poor inter-instrument reproducibility is supported by two recent publications from different groups. This phenomenon has not been demonstrated previously for large-scale bacterial and yeast studies, and results are surprising and warrant further evaluation. The results from this study have been accepted for publication in Frontiers Microbiology (in press, Sep 2019).

霉菌的传统识别是具有挑战性，繁琐且耗时的，依赖于培养或昂贵且专业的DNA测序方法的非特异性表型特征。 MALDI-TOF MS已彻底改变了全球临床微生物学实验室，从而可以快速鉴定细菌和酵母。但是，由于开发有效的蛋白质提取方法和临床全面数据库的有限可用性，这项技术并不容易应用于霉菌识别。 最近，我们为霉菌开发并优化了一个简单的蛋白质提取程序，并构建了一个高度严格的数据库，该数据库包括410个单独的分离株，代表90属和220种，用于通过MALDI-TOF MS快速识别模具。 NIH模具数据库是迄今为止开发的最全面的数据库，用于模具识别，并填补了临床微生物学领域的巨大诊断差距。 NIH模具数据库现已与全球范围内的> 100个临床微生物学实验室共享，以通过提供更快的鉴定（在生长检测后的30分钟内，而不是几周）来改善侵入性真菌感染的诊断，这反过来又可以指导最佳抗真菌治疗。 在这个财政年度，我们完成了一项十个中心的国家对照研究，该研究评估了两个真菌数据库（NIH模具数据库和Bruker真菌数据库）的临床性能和准确性，对80个临床霉菌分离株已经结束。这项研究强调了重要的（以前尚不确定）的因素，例如改善和实现MALDI-TOF MS所需的光谱采集设置，以在临床微生物学实验室中进行常规霉菌识别。实际上，在2019年8月，质谱仪（Bruker Daltonics）的制造商发布了更新的软件，具有对采集参数的特定更改，以改善模具识别。关于较差的仪器间可重复性的数据得到了来自不同群体的两份最近出版物的支持。以前尚未证明这种现象用于大规模的细菌和酵母研究，结果令人惊讶，并保证进一步评估。 这项研究的结果已被接受用于Frontiers微生物学的发表（在《新闻》，2019年9月）中。