Developing artificial intelligence (AI) for clinical antimicrobial stewardship in an era of increasing antimicrobial resistance (AMR).

在抗菌药物耐药性 (AMR) 不断增加的时代，开发用于临床抗菌药物管理的人工智能 (AI)。

• 批准号: MR/X005933/1
• 负责人: Christopher Green
• 金额: $ 10.95万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX005933%2F1
• 关键词: Developing; artificial; intelligence; AI; clinical

This project has a specific focus in managing the single greatest threat to global health, the increasing burden from infections caused by bacteria that are resistant to antibiotics (antimicrobial resistance, AMR).Doctors (humans) can't reliably know which antibiotic to administer in an emergency. In fact, from our earlier work they get it wrong about 20% of the time by prescribing an antibiotic that bacteria are resistant to for certain common types of infection. A serious bacterial infection will look the same whether the causative bacteria are resistant to certain antibiotics or not, and the first antibiotic must be selected on very limited information and be given the first 'golden' hour of sepsis management if the patient shows signs of an infection that has spread through the body. Understandably, this 'high stakes' uncertainty promotes the use of broad-spectrum antibiotics which should be held in reserve for known drug-resistant infections. Microbiological confirmation of which antibiotics are effective, when available, takes time (typically 2-3 days) which is too late for minimising un-necessary drug exposure and is often disregarded since the patient is 'getting better' on their broad-spectrum antibiotic. For many severe infections we simply never know if AMR was present, because biological samples are not taken or they were taken after the initiation of antibiotics that sterilise these samples. In the absence of knowing any alternatives, broad-spectrum antibiotics are often continued for fear of AMR being present. The emergency room decision and choice of first antibiotic seems to be the single-most important decision, not just for surviving sepsis but also for the antimicrobial stewardship needed to tackle the increasing problem of AMR. Computer science has the potential to safely unlock successful antimicrobial stewardship for AMR at the first dose. Most AMR infections arise from 'gram-negative' bacteria that live in the gut, biliary and urinary systems, so in earlier work we used linked clinical and microbiological datasets from patients who needed emergency hospital admission for these pathogens in the blood and urine. The first step was to look at which antibiotics were given at emergency presentation, how often a patient was prescribed an antibiotic that their bacterial infection was resistant to (under-prescribing), and how often a broad-spectrum antibiotic was used when another, narrow-spectrum, antibiotic alternative would have been equally effective (over-prescribing). Using a patient's electronic health record (EHR), a computer system (or artificial intelligence, AI) trained in finding patterns in vast amounts of data, that was allowed to under-prescribe at the same rate as doctors (about 20% of the time), could also reduce the use of broad-spectrum antibiotics by about 40% by anticipation of which patients were unlikely to have an AMR infection. This powerful proof-of-concept work shows the huge potential for AI in making stepwise changes towards personalised medicine and antimicrobial stewardship at the first and most important dose (full paper doi:10.1093/jac/dkaa222).Taking the next steps in AI for AMR. Many other ('gram-positive') bacteria that typically live on the skin and elsewhere are capable of serious infection and AMR. We can develop, test and model AI algorithms against these too, and broaden the potential for AI to help with more emergency hospital presentations. And even though many infections are not confirmed microbiologically, we can accurately infer information from the EHR and apply what an AI-supported prescribing pattern would look like. Linkage with clinical outcomes would also give a wider assessment of the impact AI might have on patient care and healthcare resources. Ultimately, we need a tool for front-line clinicians to safely prescribe but reduce any un-necessary initiation of broad-spectrum antibiotics.

该项目特别关注管理对全球健康的最大威胁，即由耐抗生素细菌（抗菌素耐药性，AMR）引起的感染所造成的日益增加的负担。医生（人类）无法可靠地知道在紧急情况下应使用哪种抗生素。事实上，从我们早期的工作来看，他们大约有20%的时间是错误的，因为他们开了一种细菌对某些常见类型的感染有抗药性的抗生素。严重的细菌感染无论致病菌是否对某些抗生素具有耐药性，看起来都是一样的，并且必须根据非常有限的信息选择第一种抗生素，并且如果患者显示出感染的迹象，则给予脓毒症管理的第一个“黄金”小时。可以理解的是，这种“高风险”的不确定性促进了广谱抗生素的使用，而这些抗生素应该被保留下来用于已知的耐药感染。当可用时，对哪些抗生素有效的微生物学确认需要时间（通常为2-3天），这对于最小化不必要的药物暴露来说为时已晚，并且通常被忽视，因为患者在广谱抗生素的治疗下“越来越好”。对于许多严重的感染，我们根本不知道是否存在AMR，因为没有采集生物样本，或者是在开始使用抗生素对这些样本进行灭菌后采集的。在不知道任何替代品的情况下，由于担心AMR的存在，广谱抗生素通常会继续使用。急诊室的决定和第一种抗生素的选择似乎是最重要的决定，不仅是为了生存败血症，而且是为了解决日益严重的AMR问题所需的抗菌药物管理。计算机科学有可能在第一次给药时安全地解锁AMR的成功抗菌管理。大多数AMR感染来自生活在肠道，胆道和泌尿系统中的“革兰氏阴性”细菌，因此在早期的工作中，我们使用了来自血液和尿液中这些病原体需要紧急入院的患者的相关临床和微生物学数据集。第一步是查看在急诊时给予哪些抗生素，患者多久开一次抗生素，他们的细菌感染是耐药的（开药不足），以及当另一种窄谱抗生素替代品同样有效时，使用广谱抗生素的频率（开药过量）。使用患者的电子健康记录（EHR），一种经过训练在大量数据中寻找模式的计算机系统（或人工智能，AI），被允许以与医生相同的速度开药（大约20%的时间），也可以减少约40%的广谱抗生素的使用，因为预计患者不太可能感染抗菌素耐药性。这项强大的概念验证工作显示了人工智能在逐步改变第一个也是最重要的剂量的个性化药物和抗菌药物管理方面的巨大潜力（论文全文doi：10.1093/jac/dkaa 222）。许多其他（“革兰氏阳性”）细菌，通常生活在皮肤和其他地方，能够严重感染和AMR。我们也可以开发、测试和模拟人工智能算法，并扩大人工智能的潜力，以帮助更多的紧急医院介绍。尽管许多感染没有得到微生物的证实，但我们可以准确地从EHR中推断出信息，并应用人工智能支持的处方模式。与临床结果的联系也将对人工智能可能对患者护理和医疗资源产生的影响进行更广泛的评估。最终，我们需要一种工具，让一线临床医生安全地开处方，但减少任何不必要的广谱抗生素的使用。