Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data

Chia Ru Chung, Hsin Yao Wang, Chun Han Yao, Li Ching Wu, Jang Jih Lu, Jorng Tzong Horng, Tzong Yi Lee

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In clinical microbiology, matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms2logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights.

Original languageEnglish
JournalMicrobiology Spectrum
Volume11
Issue number3
DOIs
StatePublished - May 2023

Keywords

  • antimicrobial resistance
  • cephalosporin
  • cephalosporins
  • fluoroquinolones
  • machine learning
  • MALDI-TOF MS
  • matrix-assisted laser desorption ionization–time of flight mass spectrometry
  • penicillin
  • penicillins

Fingerprint

Dive into the research topics of 'Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data'. Together they form a unique fingerprint.

Cite this