{"title":"对抗超级细菌的创新战略：开发用于精确治疗嗜麦芽单胞菌的人工智能-CDSS。","authors":"","doi":"10.1016/j.jgar.2024.06.004","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>The World Health Organization <em>named Stenotrophomonas maltophilia</em> (SM) a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing methods require up to 96 h, including 72 h for bacterial growth, identification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) through protein profile analysis, and 24 h for antibiotic susceptibility testing. In this study, we aimed at developing an artificial intelligence-clinical decision support system (AI-CDSS) by integrating MALDI-TOF MS and machine learning to quickly identify levofloxacin and trimethoprim/sulfamethoxazole resistance in SM, optimizing treatment decisions.</p></div><div><h3>Methods</h3><p>We selected 8,662 SM from 165,299 MALDI-TOF MS-analysed bacterial specimens, collected from a major medical centre and four secondary hospitals. We exported mass-to-charge values and intensity spectral profiles from MALDI-TOF MS .mzML files to predict antibiotic susceptibility testing results, obtained with the VITEK-2 system using machine learning algorithms. We optimized the models with GridSearchCV and 5-fold cross-validation.</p></div><div><h3>Results</h3><p>We identified distinct spectral differences between resistant and susceptible SM strains, demonstrating crucial resistance features. The machine learning models, including random forest, light-gradient boosting machine, and XGBoost, exhibited high accuracy. We established an AI-CDSS to offer healthcare professionals swift, data-driven advice on antibiotic use.</p></div><div><h3>Conclusions</h3><p>MALDI-TOF MS and machine learning integration into an AI-CDSS significantly improved rapid SM resistance detection. This system reduced the identification time of resistant strains from 24 h to minutes after MALDI-TOF MS identification, providing timely and data-driven guidance. Combining MALDI-TOF MS with machine learning could enhance clinical decision-making and improve SM infection treatment outcomes.</p></div>","PeriodicalId":15936,"journal":{"name":"Journal of global antimicrobial resistance","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213716524001139/pdfft?md5=6f8ab4db3e8e5f4719e61107d8ebd613&pid=1-s2.0-S2213716524001139-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Innovative strategies against superbugs: Developing an AI-CDSS for precise Stenotrophomonas maltophilia treatment\",\"authors\":\"\",\"doi\":\"10.1016/j.jgar.2024.06.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>The World Health Organization <em>named Stenotrophomonas maltophilia</em> (SM) a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing methods require up to 96 h, including 72 h for bacterial growth, identification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) through protein profile analysis, and 24 h for antibiotic susceptibility testing. In this study, we aimed at developing an artificial intelligence-clinical decision support system (AI-CDSS) by integrating MALDI-TOF MS and machine learning to quickly identify levofloxacin and trimethoprim/sulfamethoxazole resistance in SM, optimizing treatment decisions.</p></div><div><h3>Methods</h3><p>We selected 8,662 SM from 165,299 MALDI-TOF MS-analysed bacterial specimens, collected from a major medical centre and four secondary hospitals. We exported mass-to-charge values and intensity spectral profiles from MALDI-TOF MS .mzML files to predict antibiotic susceptibility testing results, obtained with the VITEK-2 system using machine learning algorithms. We optimized the models with GridSearchCV and 5-fold cross-validation.</p></div><div><h3>Results</h3><p>We identified distinct spectral differences between resistant and susceptible SM strains, demonstrating crucial resistance features. The machine learning models, including random forest, light-gradient boosting machine, and XGBoost, exhibited high accuracy. We established an AI-CDSS to offer healthcare professionals swift, data-driven advice on antibiotic use.</p></div><div><h3>Conclusions</h3><p>MALDI-TOF MS and machine learning integration into an AI-CDSS significantly improved rapid SM resistance detection. This system reduced the identification time of resistant strains from 24 h to minutes after MALDI-TOF MS identification, providing timely and data-driven guidance. Combining MALDI-TOF MS with machine learning could enhance clinical decision-making and improve SM infection treatment outcomes.</p></div>\",\"PeriodicalId\":15936,\"journal\":{\"name\":\"Journal of global antimicrobial resistance\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2213716524001139/pdfft?md5=6f8ab4db3e8e5f4719e61107d8ebd613&pid=1-s2.0-S2213716524001139-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of global antimicrobial resistance\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213716524001139\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of global antimicrobial resistance","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213716524001139","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
引用次数: 0
摘要
目的：世界卫生组织（WHO）将嗜麦芽糖血单胞菌（Stenotrophomonas maltophilia）命名为严重的多重耐药性威胁，因此有必要采取快速诊断策略。传统的培养方法需要长达 96 小时的时间，其中包括 72 小时的细菌生长时间、通过蛋白质图谱分析进行基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF MS）鉴定，以及 24 小时的抗生素药敏试验。在本研究中，我们旨在通过整合 MALDI-TOF MS 和机器学习，开发一种人工智能-临床决策支持系统（AI-CDSS），以快速识别嗜麦芽糖酵母菌对左氧氟沙星和三甲双氨/磺胺甲恶唑的耐药性，优化治疗决策：我们从一家大型医疗中心和四家二级医院收集的 165299 份经 MALDI-TOF MS 分析的细菌标本中筛选出 8662 份嗜麦芽糖浆菌。我们从 MALDI-TOF MS .mzML 文件中导出质量电荷值和强度谱图，利用机器学习算法预测 VITEK-2 系统获得的抗生素药敏试验结果。我们利用 GridSearchCV 和 5 倍交叉验证对模型进行了优化：结果：我们确定了耐药和易感嗜麦芽糖酵母菌株之间明显的光谱差异，显示了关键的耐药性特征。包括随机森林、光梯度提升机和 XGBoost 在内的机器学习模型表现出很高的准确性。我们建立了一个人工智能CDSS，为医疗保健专业人员提供快速、数据驱动的抗生素使用建议：结论：将 MALDI-TOF MS 和机器学习整合到 AI-CDSS 中，可显著提高嗜麦芽糖酵母菌耐药性的快速检测能力。该系统将耐药菌株的鉴定时间从 MALDI-TOF MS 鉴定后的 24 小时缩短至几分钟，提供了及时和数据驱动的指导。将MALDI-TOF MS与机器学习相结合可提高临床决策水平，改善嗜麦芽糖病菌感染的治疗效果。
Innovative strategies against superbugs: Developing an AI-CDSS for precise Stenotrophomonas maltophilia treatment
Objectives
The World Health Organization named Stenotrophomonas maltophilia (SM) a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing methods require up to 96 h, including 72 h for bacterial growth, identification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) through protein profile analysis, and 24 h for antibiotic susceptibility testing. In this study, we aimed at developing an artificial intelligence-clinical decision support system (AI-CDSS) by integrating MALDI-TOF MS and machine learning to quickly identify levofloxacin and trimethoprim/sulfamethoxazole resistance in SM, optimizing treatment decisions.
Methods
We selected 8,662 SM from 165,299 MALDI-TOF MS-analysed bacterial specimens, collected from a major medical centre and four secondary hospitals. We exported mass-to-charge values and intensity spectral profiles from MALDI-TOF MS .mzML files to predict antibiotic susceptibility testing results, obtained with the VITEK-2 system using machine learning algorithms. We optimized the models with GridSearchCV and 5-fold cross-validation.
Results
We identified distinct spectral differences between resistant and susceptible SM strains, demonstrating crucial resistance features. The machine learning models, including random forest, light-gradient boosting machine, and XGBoost, exhibited high accuracy. We established an AI-CDSS to offer healthcare professionals swift, data-driven advice on antibiotic use.
Conclusions
MALDI-TOF MS and machine learning integration into an AI-CDSS significantly improved rapid SM resistance detection. This system reduced the identification time of resistant strains from 24 h to minutes after MALDI-TOF MS identification, providing timely and data-driven guidance. Combining MALDI-TOF MS with machine learning could enhance clinical decision-making and improve SM infection treatment outcomes.
期刊介绍：
The Journal of Global Antimicrobial Resistance (JGAR) is a quarterly online journal run by an international Editorial Board that focuses on the global spread of antibiotic-resistant microbes.
JGAR is a dedicated journal for all professionals working in research, health care, the environment and animal infection control, aiming to track the resistance threat worldwide and provides a single voice devoted to antimicrobial resistance (AMR).
Featuring peer-reviewed and up to date research articles, reviews, short notes and hot topics JGAR covers the key topics related to antibacterial, antiviral, antifungal and antiparasitic resistance.
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