Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant Bacteria

项目总结：测量和预测抗生素的适当使用以对抗耐药细菌 抗生素耐药性感染每年已导致超过280万人患病和2.4万人死亡 仅美国一国。疾病控制和预防中心(CDC)确定抗生素处方 管理是减缓耐药感染的最重要的行动。 我们的目标是为临床决策支持系统提供方法，以减少过度和 在广谱抗生素的使用下。我们将测试新的方法来测量和预测更好的抗生素 关于尿路感染(UTI)的选择，这是人类最常见的细菌感染，占25- 50%的抗生素处方对常见抗生素的耐药性已经超过20%。 关键的挑战是抗生素的处方几乎总是在确定测试之前进行猜测 结果是可用的。这一可操作的、任意的和可确定的过程中，一个重要的决定 (抗生素处方)取决于人类预测一个可验证的结果(诊断培养结果)是理想的 适用于创新的机器学习，可以产生预测抗生素的个性化抗生素图谱 根据从大量以前的例子中学习的模式，对个人的易感性。 在抗击抗生素耐药性细菌方面取得进展的主要科学障碍包括有限的 处方指导的传统工具的个性化，过于乐观的回顾评估 预测模型，以及缺乏有效的诊断抗生素处方决定的措施。与 结合我们多站点团队(斯坦福大学、德克萨斯大学西南分校、哈佛大学)的专业知识，我们将克服这些 障碍，并通过以下目标实现本提案的目标： (1)统一和共享疑似尿路感染电子健康记录的多地点数据 (1B)开发和验证微生物培养结果的个性化抗菌图谱预测模型 (2)具有实时电子健康记录集成的抗菌图谱模型的前瞻性验证 (3)开发和验证电子表型UTIS的自动化方法 (4)制定和验证抗生素适宜性和可取性的衡量标准

Project Summary: Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant Bacteria Antimicrobial resistant infections already cause over 2.8 million illnesses and 24,000 deaths per year in the US alone. The Centers for Disease Control and Prevention (CDC) identify antibiotic prescribing stewardship as the most important action to slow resistant infections. Our objective is to produce the methods for clinical decision support systems to reduce both over and under use of broad-spectrum antibiotics. We will test novel methods to measure and predict better antibiotic choices on urinary tract infections (UTIs), the most common human bacterial infection that accounts for 25- 50% of antibiotic prescriptions with resistance already exceeding 20% for common antibiotics. The key challenge is that prescriptions for antibiotics are almost always guesses before definitive test results are available. This actionable, arbitrary, and ascertainable process where an important decision (antibiotic prescribing) depends on humans predicting a verifiable result (diagnostic culture results) is ideally suited for innovative machine learning that can produce Personalized Antibiograms that predict antibiotic susceptibility for individuals based on patterns learned from large collections of prior examples. Major scientific barriers to progress in combating antibiotic resistant bacteria include the limited personalization of conventional tools for prescribing guidance, overly optimistic retrospective evaluations of predictive models, and the lack of measures for effective diagnostic antibiotic prescribing decisions. With the combined expertise of our multi-site team (Stanford, UT Southwestern, Harvard), we will overcome these barriers and achieve the objectives of this proposal through the following aims: (1a) Multi-site data harmonization and sharing of electronic health records for suspected UTIs (1b) Develop and validate Personalized Antibiogram prediction models for microbial culture results (2) Prospective validation of antibiogram models with real-time electronic health record integration (3) Develop and validate automated methods for electronic phenotyping UTIs (4) Develop and validate a measure of antibiotic appropriateness and desirability