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》。
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
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