Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel.
Department of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel.
Nat Med. 2019 Jul;25(7):1143-1152. doi: 10.1038/s41591-019-0503-6. Epub 2019 Jul 4.
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.
抗生素耐药性在引起尿路感染的细菌病原体中很常见。然而，在缺乏抗生素药敏试验的情况下，经常会根据经验进行抗菌治疗，这可能会导致治疗不匹配，从而无效。在这里，我们将一个长达 10 年的超过 70 万例社区获得性尿路感染的纵向数据集与超过 500 万例个体解决的抗生素购买记录相关联，确定了抗生素耐药性与患者人口统计学特征、过去尿液培养记录和药物购买史之间的强关联。当这些关联结合在一起时，它们允许基于机器学习的个性化药物特异性抗生素耐药性预测，从而能够为每个样本的预期耐药性匹配抗生素治疗建议的药物处方算法。通过将这些算法应用于回顾性分析，在为期 1 年的测试期间，我们发现与当前的护理标准相比，它们大大降低了治疗不匹配的风险。此类算法的临床应用可能有助于提高抗菌治疗的效果。
