以色列理工学院Roy Kishony团队的一项最新研究认为，个人临床病史预测尿路感染的抗生素耐药性。 该研究于2019年7月发表于国际一流学术期刊《自然—医学》杂志上。

研究将70多万社区获得性尿路感染的10年纵向数据集，与500多万个人的抗生素购买记录联系起来，确定抗生素耐药性与患者的人口统计学、过去尿液培养记录和药物购买史之间的密切关系。当这些关联结合在一起时，使基于机器学习的个性化药物特异性抗生素耐药性预测成为可能，从而使药物处方算法能够将抗生素治疗建议与每个样本的预期耐药性匹配。回顾应用这些算法，在1年的测试期间，课题组发现，与当前的护理标准相比，它们大大降低了不匹配治疗的风险。这些算法的临床应用可能有助于提高抗菌治疗的有效性。

研究人员表示，抗生素耐药性在引起尿路感染的病原菌中普遍存在。然而，在缺乏抗生素敏感性测试的情况下，抗生素治疗通常是“凭经验”开出的，这可能导致治疗不匹配，从而导致治疗无效。

附：英文原文

Title: Personal clinical history predicts antibiotic resistance of urinary tract infections

Author: Idan Yelin, Olga Snitser, Gal Novich, Rachel Katz, Ofir Tal, Miriam Parizade, Gabriel Chodick, Gideon Koren, Varda Shalev, Roy Kishony

Issue&Volume: Volume 25 Issue 7, July 2019

Abstract: Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed empirically, in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10-year longitudinal data set of over 700,000 community-acquired urinary tract infections with over 5,000,000 individually resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine-learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match an antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a 1-year test period, we find that they greatly reduce the risk of mismatched treatment compared with the current standard of care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.

DOI: 10.1038/s41591-019-0503-6

Source:https://www.nature.com/articles/s41591-019-0503-6