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Direct prediction of carbapenem-resistant, carbapenemase-producing, and colistin-resistant Klebsiella pneumoniae isolates from routine MALDI-TOF mass spectra using machine learning and outcome evaluation.
Yu, Jiaxin; Lin, Yu-Tzu; Chen, Wei-Cheng; Tseng, Kun-Hao; Lin, Hsiu-Hsien; Tien, Ni; Cho, Chia-Fong; Huang, Jhao-Yu; Liang, Shinn-Jye; Ho, Lu-Ching; Hsieh, Yow-Wen; Hsu, Kai-Cheng; Ho, Mao-Wang; Hsueh, Po-Ren; Cho, Der-Yang.
Afiliación
  • Yu J; AI Center, China Medical University Hospital, Taichung, Taiwan.
  • Lin YT; Department of Medical Laboratory Science and Biotechnology, China Medical University, Taichung, Taiwan; Department of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
  • Chen WC; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan; Graduate Institute of Biomedical Sciences and School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.
  • Tseng KH; Department of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
  • Lin HH; Department of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
  • Tien N; Department of Medical Laboratory Science and Biotechnology, China Medical University, Taichung, Taiwan; Department of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
  • Cho CF; AI Center, China Medical University Hospital, Taichung, Taiwan.
  • Huang JY; AI Center, China Medical University Hospital, Taichung, Taiwan.
  • Liang SJ; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan.
  • Ho LC; Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan; School of Pharmacy, China Medical University, Taichung, Taiwan.
  • Hsieh YW; Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan; School of Pharmacy, China Medical University, Taichung, Taiwan.
  • Hsu KC; AI Center, China Medical University Hospital, Taichung, Taiwan; Department of Medicine, China Medical University, Taichung, Taiwan; Department of Neurology, China Medical University Hospital, Taichung, Taiwan.
  • Ho MW; Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
  • Hsueh PR; Department of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan; Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan. Electronic address: hsporen@gmail.com.
  • Cho DY; Department of Neurosurgery, China Medical University Hospital, Taichung, Taiwan. Electronic address: d5057@mail.cmuh.org.tw.
Int J Antimicrob Agents ; 61(6): 106799, 2023 Jun.
Article en En | MEDLINE | ID: mdl-37004755
The objective of this study was to develop a rapid prediction method for carbapenem-resistant Klebsiella pneumoniae (CRKP) and colistin-resistant K. pneumoniae (ColRKP) based on routine MALDI-TOF mass spectrometry (MS) results in order to formulate a suitable and rapid treatment strategy. A total of 830 CRKP and 1462 carbapenem-susceptible K. pneumoniae (CSKP) isolates were collected; 54 ColRKP isolates and 1592 colistin-intermediate K. pneumoniae (ColIKP) isolates were also included. Routine MALDI-TOF MS, antimicrobial susceptibility testing, NG-Test CARBA 5, and resistance gene detection were followed by machine learning (ML). Using the ML model, the accuracy and area under the curve for differentiating CRKP and CSKP were 0.8869 and 0.9551, respectively, and those for ColRKP and ColIKP were 0.8361 and 0.8447, respectively. The most important MS features of CRKP and ColRKP were m/z 4520-4529 and m/z 4170-4179, respectively. Of the CRKP isolates, MS m/z 4520-4529 was a potential biomarker for distinguishing KPC from OXA, NDM, IMP, and VIM. Of the 34 patients who received preliminary CRKP ML prediction results (by texting), 24 (70.6%) were confirmed to have CRKP infection. The mortality rate was lower in patients who received antibiotic regimen adjustment based on the preliminary ML prediction (4/14, 28.6%). In conclusion, the proposed model can provide rapid results for differentiating CRKP and CSKP, as well as ColRKP and ColIKP. The combination of ML-based CRKP with preliminary reporting of results can help physicians alter the regimen approximately 24 h earlier, resulting in improved survival of patients with timely antibiotic intervention.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infecciones por Klebsiella / Enterobacteriaceae Resistentes a los Carbapenémicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Int J Antimicrob Agents Año: 2023 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Países Bajos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infecciones por Klebsiella / Enterobacteriaceae Resistentes a los Carbapenémicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Int J Antimicrob Agents Año: 2023 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Países Bajos