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Volume 7, Issue 9

A random forest model based on core genome allelic profiles of MRSA for penicillin plus potassium clavulanate susceptibility prediction Open Access

Abstract

Treatment failure of methicillin-resistant (MRSA) infections remains problematic in clinical practice because therapeutic options are limited. Penicillin plus potassium clavulanate combination (PENC) was shown to have potential for treating some MRSA infections. We investigated the susceptibility of MRSA isolates and constructed a drug susceptibility prediction model for the phenotype of the PENC. We determined the minimum inhibitory concentration of PENC for MRSA (=284) in a teaching hospital (SRRSH-MRSA). PENC susceptibility genotypes were analysed using a published genotyping scheme based on the sequence. expression in MRSA isolates was analysed by qPCR. We established a random forest model for predicting PENC-susceptible phenotypes using core genome allelic profiles from cgMLST analysis. We identified S2-R isolates with susceptible genotypes but PENC-resistant phenotypes; these isolates expressed at higher levels than did S2 MRSA (2.61 vs 0.98, <0.05), indicating the limitation of using a single factor for predicting drug susceptibility. Using the data of selected UK-sourced MRSA (=74) and MRSA collected in a previous national survey (NA-MRSA, =471) as a training set, we built a model with accuracies of 0.94 and 0.93 for SRRSH-MRSA and UK-sourced MRSA (=287, NAM-MRSA) validation sets. The AUROC of this model for SRRSH-MRSA and NAM-MRSA was 0.96 and 0.97. Although the source of the training set data affects the scope of application of the prediction model, our data demonstrated the power of the machine learning approach in predicting susceptibility from cgMLST results.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): CC59 , machine learning , mecA , methicillin-resistant Staphylococcus aureus , penicillin and random forest
Funding
This study was supported by the:
  • National Science and Technology Planning Project (Award 2018ZX10714002)
    • Principle Award Recipient: YanJiang
  • Natural Science Foundation of Zhejiang Province (Award LQ20H190005)
    • Principle Award Recipient: LuSun
  • National Natural Science Foundation of China (Award 81971977)
    • Principle Award Recipient: YanChen
© 2021 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
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2021-09-23
2024-04-24
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A random forest model based on core genome allelic profiles of MRSA for penicillin plus potassium clavulanate susceptibility prediction
M Gen 7, 000610 (2021); https://doi.org/10.1099/mgen.0.000610
/content/journal/mgen/10.1099/mgen.0.000610
/content/journal/mgen/10.1099/mgen.0.000610
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