Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant Bacteria

测量和预测对抗耐药细菌的适当抗生素使用

基本信息

批准号：
10720073
负责人：
JONATHAN H. CHEN
金额：
$ 79.45万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2028-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10720073
关键词：
Accounting Adverse effects Agreement Antibiotic Resistance Antibiotic susceptibility Antibiotics Bacteria Bacterial Antibiotic Resistance Bacterial Infections Bacteriuria Centers for Disease Control and Prevention (U.S.)Cessation of life Clinical Clinical Data Clinical Decision Support Systems Clinical Microbiology Clinical Trials Collaborations Collection Combating Antibiotic Resistant Bacteria Communities Computers Consultations Data Databases Diagnostic Electronic Health Record Electronics Evaluation FAIR principles Feedback Guidelines Health Health Care Costs Hospitalization Human Individual Infection Learning Machine Learning Manuals Measures Methods Modeling Natural Language Processing Outcome Measure Patients Pattern Phenotype Predisposition Process Prospective Studies Real-Time Systems Recommendation Reference Standards Reproducibility Research Resistance Risk Selection Bias Site Specificity Symptoms Systems Integration Test Result Testing Time Training Translating Urinary tract infection Urine Validation Work antimicrobial resistant infection application programming interface automated algorithm bacterial resistance clinical decision support combat cost data harmonization data sharing data standards electronic data sharing electronic medical record system experience improved innovation machine learning method machine learning model microbial model development novel personalized predictions phenotyping algorithm point of care predictive modeling prospective prototype routine care statistical learning statistics tool treatment risk

项目摘要

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

项目摘要：测量和预测适当的抗生素使用以对抗抗性细菌抗菌素抗性感染每年已经导致超过280万疾病和24,000人死亡美国一个人。疾病控制与预防中心（CDC）确定了抗生素处方管理是缓慢抗性感染的最重要动作。我们的目标是生产临床决策支持系统的方法，以减少过度和在使用广谱抗生素的情况下。我们将测试新方法，以测量和预测更好的抗生素尿路感染的选择（UTI），这是最常见的人类细菌感染，占25-- 普通抗生素的抗生素处方的50％已经超过20％。关键挑战是抗生素的处方几乎总是在确定测试之前猜测结果可用。这个可行，任意和可确定的过程，在其中一个重要的决定（抗生素处方）取决于人类预测可验证的结果（诊断培养结果）是理想情况下适用于创新的机器学习，可以产生个性化的抗生素图来预测抗生素基于从大量先前示例中学到的模式的个人敏感性。打击抗生素耐药细菌的进步的主要科学障碍包括有限传统工具的个性化规定指导，过于乐观的回顾性评估预测模型，以及缺乏有效诊断抗生素处方决策的措施。与我们的多站点团队（斯坦福大学，犹他州西南部，哈佛）的联合专业知识，我们将克服这些通过以下目的实现障碍并实现该提案的目标：（1a）可疑UTI的电子健康记录的多站点数据协调和共享（1B）开发和验证微生物培养结果的个性化抗体预测模型（2）具有实时电子健康记录整合的抗体图模型的前瞻性验证（3）开发和验证电子表型UTI的自动化方法（4）开发和验证抗生素适当性和可取性的度量