Department of Bioengineering, University of California San Diego, San Diego, California, USA.
Department of Pathology, University of Texas Health Science Center, San Antonio, Texas, USA.
J Clin Microbiol. 2024 Jun 12;62(6):e0147623. doi: 10.1128/jcm.01476-23. Epub 2024 May 2.
Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (, , , and ) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be 8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRM's speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes.
Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.
侵袭性霉菌感染（IMI）与高发病率相关，特别是在免疫功能低下的患者中，死亡率在 40%至 80%之间。早期开始适当的抗真菌治疗可以显著改善预后，但早期诊断仍然难以确定，通常需要多学科团队评估临床和影像学发现以及支持性的真菌学发现。通用数字高分辨率熔解（U-dHRM）分析可能能够快速、稳健地诊断 IMI。开发了一种通用真菌检测方法用于 U-dHRM，并用于生成 19 种临床相关真菌病原体的熔解曲线特征数据库。训练了一个机器学习算法（ML）来自动对这些病原体曲线进行分类并检测新的熔解曲线。在怀疑患有 IMI 的 73 例临床支气管肺泡灌洗样本上评估了性能。通过微移液 U-dHRM 反应和 Sanger 测序扩增子来鉴定新的曲线。U-dHRM 实现了 97%的总体真菌鉴定准确性和~4 小时的周转时间。U-dHRM 在 30 份分类为 IMI 的样本中的 73%中检测到致病性霉菌（、、、和），包括混合感染。通过要求在样本中检测到的致病性霉菌曲线数量为 8 条且样本体积为 1 毫升来优化特异性，在没有 IMI 的 21 名高危患者中特异性达到 100%。U-dHRM 作为标准真菌学检测的单独或组合诊断方法具有潜力。U-dHRM 的速度、在多微生物样本中同时识别和定量临床相关霉菌病原体以及检测新兴机会性病原体的能力可能有助于治疗决策，改善患者预后。
迫切需要改进侵袭性霉菌感染的诊断方法。这项工作提出了一种新的分子检测方法，解决了技术和工作流程方面的挑战，提供了快速的病原体检测、鉴定和定量，这可以为治疗提供信息，改善患者预后。
