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机器学习可从临床MALDI-TOF质谱中直接预测抗菌耐药性
作者:小柯机器人 发布时间:2022/1/12 15:09:35

瑞士巴塞尔大学Adrian Egli等研究人员合作利用机器学习从临床MALDI-TOF质谱中直接预测抗菌耐药性。相关论文于2022年1月10日在线发表于国际学术期刊《自然—医学》。

研究人员开发了一种新的机器学习方法,可直接从临床分离物的基质辅助激光解吸飞行时间质谱(MALDI-TOF)质谱图中预测抗菌耐药性。研究人员在一个新创建的公开数据库中训练了经过校准的分类器,该数据库中的质谱图来自于临床上最相关的分离物,具有相关的抗菌药敏表型。
 
这个数据集结合了来自四个医疗机构的30多万个质谱和75万个抗菌耐药表型。在临床上重要的病原体面板上进行验证,包括金黄色葡萄球菌、大肠杆菌和肺炎克雷伯氏菌,结果接收者操作特征曲线下的面积分别为0.80、0.74和0.74,这证明了使用机器学习大幅加快抗菌耐药性测定和改变临床管理的潜力。
 
此外,对63名患者的回顾性临床病例研究发现,实施这种方法会改变9个病例的临床治疗,这对8个病例(89%)是有益的。因此,基于MALDI-TOF质谱的机器学习可能是治疗优化和抗生素管理的一个重要新工具。
 
据了解,尽早使用有效的抗菌素治疗对于感染的结果和预防治疗的耐药性至关重要。抗菌耐药性测试能够选择最佳的抗生素治疗方法,但目前基于培养的技术可能需要72小时才能产生结果。
 
附:英文原文
 
Title: Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning

Author: Weis, Caroline, Cunod, Aline, Rieck, Bastian, Dubuis, Olivier, Graf, Susanne, Lang, Claudia, Oberle, Michael, Brackmann, Maximilian, Sgaard, Kirstine K., Osthoff, Michael, Borgwardt, Karsten, Egli, Adrian

Issue&Volume: 2022-01-10

Abstract: Early use of effective antimicrobial treatments is critical for the outcome of infections and the prevention of treatment resistance. Antimicrobial resistance testing enables the selection of optimal antibiotic treatments, but current culture-based techniques can take up to 72hours to generate results. We have developed a novel machine learning approach to predict antimicrobial resistance directly from matrix-assisted laser desorption/ionization–time of flight (MALDI-TOF) mass spectra profiles of clinical isolates. We trained calibrated classifiers on a newly created publicly available database of mass spectra profiles from the clinically most relevant isolates with linked antimicrobial susceptibility phenotypes. This dataset combines more than 300,000 mass spectra with more than 750,000 antimicrobial resistance phenotypes from four medical institutions. Validation on a panel of clinically important pathogens, including Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae, resulting in areas under the receiver operating characteristic curve of 0.80, 0.74 and 0.74, respectively, demonstrated the potential of using machine learning to substantially accelerate antimicrobial resistance determination and change of clinical management. Furthermore, a retrospective clinical case study of 63 patients found that implementing this approach would have changed the clinical treatment in nine cases, which would have been beneficial in eight cases (89%). MALDI-TOF mass spectra-based machine learning may thus be an important new tool for treatment optimization and antibiotic stewardship.

DOI: 10.1038/s41591-021-01619-9

Source: https://www.nature.com/articles/s41591-021-01619-9

期刊信息

Nature Medicine:《自然—医学》,创刊于1995年。隶属于施普林格·自然出版集团,最新IF:30.641
官方网址:https://www.nature.com/nm/
投稿链接:https://mts-nmed.nature.com/cgi-bin/main.plex