Treatment failure of methicillin-resistant Staphylococcus aureus (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 (n=284) in a teaching hospital (SRRSH-MRSA). PENC susceptibility genotypes were analysed using a published genotyping scheme based on the mecA sequence. mecA 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 mecA genotypes but PENC-resistant phenotypes; these isolates expressed mecA at higher levels than did S2 MRSA (2.61 vs 0.98, P<0.05), indicating the limitation of using a single factor for predicting drug susceptibility. Using the data of selected UK-sourced MRSA (n=74) and MRSA collected in a previous national survey (NA-MRSA, n=471) as a training set, we built a model with accuracies of 0.94 and 0.93 for SRRSH-MRSA and UK-sourced MRSA (n=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.

中文翻译：

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基于 MRSA 核心基因组等位基因谱的随机森林模型用于青霉素加克拉维酸钾敏感性预测

由于治疗选择有限，耐甲氧西林金黄色葡萄球菌(MRSA) 感染的治疗失败在临床实践中仍然存在问题。青霉素加克拉维酸钾组合（PENC）被证明具有治疗某些 MRSA 感染的潜力。我们调查了 MRSA 分离株的敏感性，并为 PENC 的表型构建了药物敏感性预测模型。我们确定了教学医院 (SRRSH-MRSA) 中MRSA ( n = 284)的最低 PENC 抑制浓度。使用基于mecA序列的已发表基因分型方案分析 PENC 易感性基因型。甲酸 通过 qPCR 分析 MRSA 分离株中的表达。我们建立了一个随机森林模型，使用来自 cgMLST 分析的核心基因组等位基因谱来预测 PENC 易感表型。我们鉴定了具有易感mecA基因型但具有 PE​​NC 抗性表型的S2-R 分离株；这些分离株的mecA表达水平高于 S2 MRSA（2.61 vs 0.98，P <0.05），表明使用单一因素预测药物敏感性的局限性。使用选定的英国来源 MRSA（n = 74）和在之前的全国调查（NA-MRSA，n = 471）中收集的 MRSA 的数据作为训练集，我们建立了 SRRSH-MRSA 的准确度为 0.94 和 0.93 的模型和英国来源的 MRSA（n=287，NAM-MRSA) 验证集。该模型对 SRRSH-MRSA 和 NAM-MRSA 的 AUROC 分别为 0.96 和 0.97。尽管训练集数据的来源会影响预测模型的应用范围，但我们的数据证明了机器学习方法在从 cgMLST 结果预测易感性方面的能力。