Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform

Alexander Sturm (), Grzegorz Jóźwiak, Marta Pla Verge, Laura Munch, Gino Cathomen, Anthony Vocat, Amanda Luraschi-Eggemann, Clara Orlando, Katja Fromm, Eric Delarze, Michał Świątkowski, Grzegorz Wielgoszewski, Roxana M. Totu, María García-Castillo, Alexandre Delfino, Florian Tagini, Sandor Kasas, Cornelia Lass-Flörl, Ronald Gstir, Rafael Cantón, Gilbert Greub and Danuta Cichocka
Additional contact information
Alexander Sturm: Resistell AG, Hofackerstrasse 40
Grzegorz Jóźwiak: Resistell AG, Hofackerstrasse 40
Marta Pla Verge: Resistell AG, Hofackerstrasse 40
Laura Munch: Resistell AG, Hofackerstrasse 40
Gino Cathomen: Resistell AG, Hofackerstrasse 40
Anthony Vocat: Resistell AG, Hofackerstrasse 40
Amanda Luraschi-Eggemann: Resistell AG, Hofackerstrasse 40
Clara Orlando: Resistell AG, Hofackerstrasse 40
Katja Fromm: Resistell AG, Hofackerstrasse 40
Eric Delarze: Resistell AG, Hofackerstrasse 40
Michał Świątkowski: Resistell AG, Hofackerstrasse 40
Grzegorz Wielgoszewski: Resistell AG, Hofackerstrasse 40
Roxana M. Totu: Resistell AG, Hofackerstrasse 40
María García-Castillo: Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Carretera de Colmenar Km 9,1
Alexandre Delfino: Lausanne University Hospital (CHUV) & University of Lausanne (UNIL)
Florian Tagini: Lausanne University Hospital (CHUV) & University of Lausanne (UNIL)
Sandor Kasas: École Polytechnique Fédérale de Lausanne (EPFL) and University of Lausanne (UNIL)
Cornelia Lass-Flörl: Medizinische Universität Innsbruck, Schöpfstraße 41
Ronald Gstir: Medizinische Universität Innsbruck, Schöpfstraße 41
Rafael Cantón: Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Carretera de Colmenar Km 9,1
Gilbert Greub: Lausanne University Hospital (CHUV) & University of Lausanne (UNIL)
Danuta Cichocka: Resistell AG, Hofackerstrasse 40

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner. We describe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are applied to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performances of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical setting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9%. The study shows the potential of this nanomotion platform for future bacterial phenotype delineation.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-46213-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46213-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-46213-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().