Kong Po-Hsin, Chiang Cheng-Hsiung, Lin Ting-Chia, Kuo Shu-Chen, Li Chien-Feng, Hsiung Chao A, Shiue Yow-Ling, Chiou Hung-Yi, Wu Li-Ching, Tsou Hsiao-Hui
Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 80424, Taiwan.
Center for Precision Medicine, Chi Mei Medical Center, Tainan 71004, Taiwan.
Pathogens. 2022 May 16;11(5):586. doi: 10.3390/pathogens11050586.
Early administration of proper antibiotics is considered to improve the clinical outcomes of bacteremia (SAB), but routine clinical antimicrobial susceptibility testing takes an additional 24 h after species identification. Recent studies elucidated matrix-assisted laser desorption/ionization time-of-flight mass spectra to discriminate methicillin-resistant strains (MRSA) or even incorporated with machine learning (ML) techniques. However, no universally applicable mass peaks were revealed, which means that the discrimination model might need to be established or calibrated by local strains' data. Here, a clinically feasible workflow was provided. We collected mass spectra from SAB patients over an 8-month duration and preprocessed by binning with reference peaks. Machine learning models were trained and tested by samples independently of the first six months and the following two months, respectively. The ML models were optimized by genetic algorithm (GA). The accuracy, sensitivity, specificity, and AUC of the independent testing of the best model, i.e., SVM, under the optimal parameters were 87%, 75%, 95%, and 87%, respectively. In summary, almost all resistant results were truly resistant, implying that physicians might escalate antibiotics for MRSA 24 h earlier. This report presents an attainable method for clinical laboratories to build an MRSA model and boost the performance using their local data.
早期给予适当的抗生素被认为可改善血流感染（SAB）的临床结局，但常规临床抗菌药物敏感性测试在菌种鉴定后还需要额外24小时。最近的研究阐明了基质辅助激光解吸/电离飞行时间质谱以鉴别耐甲氧西林菌株（MRSA），甚至结合了机器学习（ML）技术。然而，尚未发现普遍适用的质谱峰，这意味着鉴别模型可能需要根据当地菌株的数据来建立或校准。在此，提供了一种临床可行的工作流程。我们收集了8个月期间SAB患者的质谱，并通过与参考峰进行分箱预处理。机器学习模型分别使用前六个月和接下来两个月的样本进行训练和测试。ML模型通过遗传算法（GA）进行优化。最佳模型即支持向量机（SVM）在最佳参数下独立测试的准确性、敏感性、特异性和曲线下面积（AUC）分别为87%、75%、95%和87%。总之，几乎所有耐药结果都是真正耐药的，这意味着医生可能会提前24小时升级针对MRSA的抗生素治疗。本报告提出了一种临床实验室可实现的方法，利用当地数据构建MRSA模型并提高其性能。
