Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan.
Graduate Institute of Medical Science, National Defense Medical Center, Taipei City, Taiwan.
J Med Internet Res. 2024 Nov 7;26:e58039. doi: 10.2196/58039.
The rising prevalence and swift spread of multidrug-resistant gram-negative bacteria (MDR-GNB), especially Klebsiella pneumoniae (KP), present a critical global health threat highlighted by the World Health Organization, with mortality rates soaring approximately 50% with inappropriate antimicrobial treatment.
This study aims to advance a novel strategy to develop an artificial intelligence-clinical decision support system (AI-CDSS) that combines machine learning (ML) with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), aiming to significantly improve the accuracy and speed of diagnosing antibiotic resistance, directly addressing the grave health risks posed by the widespread dissemination of pan drug-resistant gram-negative bacteria across numerous countries.
A comprehensive dataset comprising 165,299 bacterial specimens and 11,996 KP isolates was meticulously analyzed using MALDI-TOF MS technology. Advanced ML algorithms were harnessed to sculpt predictive models that ascertain resistance to quintessential antibiotics, particularly levofloxacin and ciprofloxacin, by using the amassed spectral data.
Our ML models revealed remarkable proficiency in forecasting antibiotic resistance, with the random forest classifier emerging as particularly effective in predicting resistance to both levofloxacin and ciprofloxacin, achieving the highest area under the curve of 0.95. Performance metrics across different models, including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F-score, were detailed, underlining the potential of these algorithms in aiding the development of precision treatment strategies.
This investigation highlights the synergy between MALDI-TOF MS and ML as a beacon of hope against the escalating threat of antibiotic resistance. The advent of AI-CDSS heralds a new era in clinical diagnostics, promising a future in which rapid and accurate resistance prediction becomes a cornerstone in combating infectious diseases. Through this innovative approach, we answered the challenge posed by KP and other multidrug-resistant pathogens, marking a significant milestone in our journey toward global health security.
多药耐药革兰氏阴性菌（MDR-GNB），尤其是肺炎克雷伯菌（KP）的患病率不断上升且迅速传播，对全球健康构成了严重威胁，世界卫生组织（WHO）强调，不适当的抗菌治疗可使死亡率飙升约 50%。
本研究旨在提出一种新策略，开发一种人工智能-临床决策支持系统（AI-CDSS），将机器学习（ML）与基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF MS）相结合，旨在显著提高诊断抗生素耐药性的准确性和速度，直接解决了泛耐药革兰氏阴性菌在多个国家广泛传播所带来的严重健康风险。
使用 MALDI-TOF MS 技术对包含 165299 个细菌标本和 11996 个 KP 分离株的综合数据集进行了细致分析。利用积累的光谱数据，利用先进的 ML 算法来构建预测模型，以确定对主要抗生素（特别是左氧氟沙星和环丙沙星）的耐药性。
我们的 ML 模型在预测抗生素耐药性方面表现出了很高的准确率，随机森林分类器在预测左氧氟沙星和环丙沙星的耐药性方面表现尤为出色，曲线下面积最高为 0.95。详细介绍了不同模型的性能指标，包括准确性、敏感性、特异性、阳性预测值、阴性预测值和 F 分数，强调了这些算法在辅助制定精准治疗策略方面的潜力。
本研究强调了 MALDI-TOF MS 与 ML 之间的协同作用，是对抗抗生素耐药性不断升级威胁的希望之光。人工智能-CDSS 的出现标志着临床诊断的新纪元，有望实现快速准确的耐药预测成为抗击传染病的基石。通过这种创新方法，我们应对了 KP 和其他多药耐药病原体带来的挑战，在实现全球健康安全的道路上迈出了重要一步。
