Published September 18, 2021 | Version v2
Dataset Open

Rapid identification of MRSA using mass spectrometry and machine learning from over 20000 clinical isolates

Authors/Creators

  • 1. AI Innovation Center, China Medical University Hospital, Taichung City, Taiwan
  • 2. Department of Laboratory Medicine, China Medical University Hospital, Taichung City, Taiwan
  • 3. Proteomics Core Laboratory, Department of Medical Research, China Medical University Hospital, Taichung City, Taiwan
  • 4. Department of Neurosurgery, China Medical University Hospital, Taichung City, Taiwan
  • 5. Laboratory Medicine, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung City, Taiwan
  • 6. Department of Laboratory Medicine, Taipei Medical University-Shuang Ho Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan
  • 7. Department of Laboratory Medicine, National Taiwan University Hospital Yunlin Branch, Yunlin County, Taiwan
  • 8. Department of Laboratory Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien County, Taiwan

Description

Rapidly identifying methicillin-resistant Staphylococcus aureus (MRSA) with high integration in the current workflow is critical in clinical practices. We proposed a MALDI-TOF MS based machine learning model for rapidly MRSA prediction, the model was evaluated on a prospective test and four external clinical sites. On the dataset comprising 20359 clinical isolates, the area under the receiver operating curve of the classification model was 0.78–0.88. Our MALDI–TOF MS-based ML model for the rapid MRSA identification can be easily integrated into the current clinical workflows and can further support physicians prescribe proper antibiotic treatments. 

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