Automation and Multi-Site Validation of a Personalized Empiric Antibiotic Advisor

个性化经验性抗生素顾问的自动化和多站点验证

• 批准号: 9061594
• 负责人: Courtney L. Hebert
• 金额: $ 38.54万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9061594
• 关键词: Algorithms; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Automation; Centers for Disease Control and Prevention (U.S.); Clinical; Clinical Data; Communicable Diseases; Communities; Country; Data; Data Aggregation; Diarrhea; Early treatment; Electronic Health Record; Equilibrium; Future; Goals; Guidelines; Health; Health Care Costs; Hospitals; Incidence; Individual; Infection; Institution; Lead; Measures; Medical center; Methodology; Microbiology; Modeling; Multi-Drug Resistance; Ohio; Organism; Output; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Performance; Pharmacists; Pharmacy facility; Phase; Physicians; Process; Provider; Recommendation; Regimen; Reporting; Research Personnel; Resistance; Resources; Site; Syndrome; System; Testing; Universities; Validation; Weight; Work; bacterial resistance; base; cohort; computerized data processing; cost; data format; disorder prevention; drug resistant bacteria; improved; novel; point of care; predictive modeling; tool; user-friendly

 DESCRIPTION (provided by applicant): Poor antibiotic prescribing practices can increase bacterial resistance in the community and harm patients if an inappropriate antibiotic is chosen. The goal of this work is to automate and validate a tool intended to help providers better choose antibiotics before culture results are available. The tool uses microbiology data from the hospital and clinical information from the patient to predict which antibiotic regimen would be most likely to cover the patient's infection. The study will be done at two sites in order to improve the generalizability of the findings, and to create a tool that can be easily disseminated to other sites. The work will be accomplished in three aims. In the first aim, the microbiology data from two institutions will be formatted so that it can be used in predictive models. Experts including microbiologists, physicians, and pharmacists at both institutions will create rules that will be used to create an automated algorithm for formatting the data. In the second aim, predictive models will be developed and validated on a retrospective cohort at both institutions. Next, prescribing rules developed by subject matter experts will be added to the model output. This will allow the tool to output a recommended antibiotic regimen based on the rules and predictive modeling. The investigators will assess the quality of this recommendation by comparing it to the actual initial antibiotic given to each patient. The percentage of infections that were covered by each antibiotic regimen will be compared, as will the breadth of coverage and cost of each regimen. In the third aim, a user-friendly interface will be created within the electronic health record to display the outputs of the tool created in aim two. This work is the first phase of a larger project intended to study whether giving providers access to up-to-date, local, personalized microbiology data will improve prescribing practices and ultimately patient care and outcomes.

 描述（由申请人提供）：如果选择了不适当的抗生素，不良的抗生素处方实践可能会增加社区中的细菌耐药性并伤害患者。这项工作的目标是自动化和验证一种工具，旨在帮助提供者在培养结果可用之前更好地选择抗生素。该工具使用来自医院的微生物学数据 和来自患者的临床信息来预测哪种抗生素方案最有可能覆盖患者的感染。这项研究将在两个地点进行，以提高研究结果的普遍性，并创建一个易于传播到其他地点的工具。这项工作将在三个目标中完成。在第一个目标中，来自两个机构的微生物学数据将被格式化，以便它可以用于预测模型。两家机构的微生物学家、医生和药剂师等专家将制定规则，用于创建格式化数据的自动算法。在第二个目标中，将在两个机构的回顾性队列中开发和验证预测模型。接下来，主题专家制定的处方规则将添加到模型输出中。这将允许该工具基于规则和预测建模输出推荐的抗生素方案。研究人员将通过将该建议与每位患者实际使用的初始抗生素进行比较来评估该建议的质量。覆盖的感染百分比 将比较每种抗生素治疗方案，以及每种治疗方案的覆盖范围和成本。在第三个目标中，将在电子健康记录中创建一个用户友好的界面，以显示在目标二中创建的工具的输出。这项工作是一个更大项目的第一阶段，该项目旨在研究提供者获得最新的、本地的、个性化的微生物学数据是否会改善处方实践，并最终改善患者护理和结果。