Jian Ming-Jr, Lin Tai-Han, Chung Hsing-Yi, Chang Chih-Kai, Perng Cherng-Lih, Chang Feng-Yee, Shang Hung-Sheng
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan, Republic of China.
Graduate Institute of Medical Science, National Defense Medical Center, Taipei City, Taiwan, Republic of China.
Infect Drug Resist. 2024 Jul 10;17:2899-2912. doi: 10.2147/IDR.S470821. eCollection 2024.
The World Health Organization has identified (KP) as a significant threat to global public health. The rising threat of carbapenem-resistant (CRKP) leads to prolonged hospital stays and higher medical costs, necessitating faster diagnostic methods. Traditional antibiotic susceptibility testing (AST) methods demand at least 4 days, requiring 3 days on average for culturing and isolating the bacteria and identifying the species using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), plus an extra day for interpreting AST results. This lengthy process makes traditional methods too slow for urgent clinical situations requiring rapid decision-making, potentially hindering prompt treatment decisions, especially for fast-spreading infections such as those caused by CRKP. This research leverages a cutting-edge diagnostic method that utilizes an artificial intelligence-clinical decision support system (AI-CDSS). It incorporates machine learning algorithms for the swift and precise detection of carbapenem-resistant and colistin-resistant strains.
We selected 4307 KP samples out of a total of 52,827 bacterial samples due to concerns about multi-drug resistance using MALDI-TOF MS and Vitek-2 systems for AST. It involved thorough data preprocessing, feature extraction, and machine learning model training fine-tuned with GridSearchCV and 5-fold cross-validation, resulting in high predictive accuracy, as demonstrated by the receiver operating characteristic and area under the curve (AUC) scores, laying the groundwork for our AI-CDSS.
MALDI-TOF MS analysis revealed distinct intensity profiles differentiating CRKP and susceptible strains, as well as colistin-resistant (CoRKP) and susceptible strains. The Random Forest Classifier demonstrated superior discriminatory power, with an AUC of 0.96 for detecting CRKP and 0.98 for detecting CoRKP.
Integrating MALDI-TOF MS with machine learning in an AI-CDSS has greatly expedited the detection of KP resistance by approximately 1 day. This system offers timely guidance, potentially enhancing clinical decision-making and improving treatment outcomes for KP infections.
世界卫生组织已将碳青霉烯类耐药肺炎克雷伯菌（CRKP）确定为对全球公共卫生的重大威胁。碳青霉烯类耐药肺炎克雷伯菌日益增长的威胁导致住院时间延长和医疗成本增加，因此需要更快的诊断方法。传统的抗生素敏感性测试（AST）方法至少需要4天，平均需要3天来培养和分离细菌，并使用基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）鉴定菌种，另外还需要一天来解读AST结果。这个漫长的过程使得传统方法对于需要快速决策的紧急临床情况来说太慢了，可能会阻碍及时的治疗决策，特别是对于像CRKP引起的快速传播感染。本研究利用了一种前沿的诊断方法，即利用人工智能临床决策支持系统（AI-CDSS）。它结合了机器学习算法，用于快速准确地检测碳青霉烯类耐药和黏菌素耐药菌株。
由于担心多重耐药性，我们从总共52827个细菌样本中选择了4307个肺炎克雷伯菌样本，使用MALDI-TOF MS和Vitek-2系统进行AST。这涉及到全面的数据预处理、特征提取以及使用GridSearchCV和五折交叉验证进行微调的机器学习模型训练，从而实现了高预测准确性，如通过受试者工作特征曲线和曲线下面积（AUC）得分所证明的那样，为我们的AI-CDSS奠定了基础。
MALDI-TOF MS分析显示出区分CRKP和敏感菌株以及黏菌素耐药肺炎克雷伯菌（CoRKP）和敏感菌株的不同强度谱。随机森林分类器表现出卓越的区分能力，检测CRKP的AUC为0.96，检测CoRKP的AUC为0.98。
在AI-CDSS中将MALDI-TOF MS与机器学习相结合，已将肺炎克雷伯菌耐药性的检测速度大幅加快了约1天。该系统提供了及时的指导，有可能增强临床决策并改善肺炎克雷伯菌感染的治疗效果。
