Sepsis phenotypes at risk for infections caused by multidrug resistant Gram-negative bacilli: elucidating the impact of sepsis definition and patient case mix on prediction performance

脓毒症表型面临由多重耐药革兰氏阴性杆菌引起的感染风险：阐明脓毒症定义和患者病例组合对预测性能的影响

• 批准号: 10412800
• 负责人: Maria Cristina Vazquez Guillamet
• 金额: $ 13.81万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-10 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10412800
• 关键词: Accreditation; Algorithms; Antibiotics; Antimicrobial Resistance; Artificial Intelligence; Award; Bacillus; Big Data; Case Mixes; Cause of Death; Characteristics; Clinical; Clinical Data; Clinical Medicine; Cognition; Collaborations; Communicable Diseases; Communities; Complement; Critical Care; Data; Data Element; Data Engineering; Data Scientist; Data Set; Development Plans; Diagnosis; Early Diagnosis; Electronic Health Record; Ensure; Environment; Ethnic Origin; Ethnic group; FAIR principles; Funding; Gender; Goals; Health Care Costs; Healthcare Systems; Hospitals; Human; Image; Individual; Infection; Informatics; Label; Laboratories; Link; Machine Learning; Medicine; Mentors; Mentorship; Metadata; Methods; Minority Groups; Modeling; Morbidity - disease rate; Multi-Drug Resistance; National Institute of General Medical Sciences; Nursing Homes; Outcome; Parents; Patients; Pattern; Performance; Pharmaceutical Preparations; Phenotype; Population; Problem Solving; Race; Readiness; Reporting; Reproducibility; Research; Research Personnel; Resistance; Risk; Risk Estimate; Risk Factors; Rural; Sepsis; Standardization; Structure; Subgroup; Symptoms; Syndrome; System; TRUST principles; Testing; Text; Time; Training; United States National Institutes of Health; Universities; Urban Hospitals; Washington; base; career development; clinical application; cohort; comorbidity; cost; data dictionary; data mining; data tools; design; emerging antimicrobial resistance; experience; image processing; improved; individualized medicine; infection risk; innovation; interest; machine learning algorithm; machine learning method; mathematical model; mortality risk; multidisciplinary; patient population; predictive modeling; primary outcome; residence; risk prediction; socioeconomics; stem; structured data; suburb; tool; unstructured data; unsupervised learning

SUPPLEMENT ABSTRACT Sepsis is a devastating syndrome that represents a leading cause of death, morbidity, and healthcare costs. Its impact is amplified by rising rates of antimicrobial resistance. Improving sepsis outcomes primarily results from prescribing timely antibiotics based on the estimated risk of multidrug resistance (MDR). Artificial intelligence (AI) and machine learning (ML) are data- driven approaches looking for patterns in massive datasets. While the AI/ ML algorithms rapidly advanced and built successful imaging processing applications, the promise of AI/ML in sepsis and antimicrobial resistance research remains largely unfulfilled. The main reasons stem from deficient, inaccessible and poorly labeled clinical data allowing for only a small portion of the electronic health records (EHR) data to be used. More so, clinical narratives such as notes and imaging reports which contain unstructured data elements in free text format are almost never used. Our parent K08 award aims to identify sepsis phenotypes at risk for MDR GNB that will enable better antibiotic prescribing practices and standardize comparisons across hospitals. We propose to accomplish our goal by leveraging big data and using innovative methods such as ML methods. This supplement will strengthen our project by analyzing in detail the barriers to efficiently using EHR data including unstructured data elements and providing data engineering solutions. The objective is to provide the framework for ML use in sepsis research. Demonstrating reproducibility and rigor of our ML methods and making the algorithms and datasets accessible per FAIR and TRUST principles will be responsive to NIGMS and broader NIH priorities. Our aims reflect these priorities: 1) Analyze barriers to use of EHR structured data and provide data engineering solutions for data enrichment, 2) Extract and assess the importance of unstructured data in developing ML sepsis models, and 3) Compare the ML sepsis models using unstructured and structured data VS structured data only and ensure algorithm fairness by testing it across subgroups of interest based on gender and race. We will incorporate clinical data from the 15 hospitals in our healthcare system serving an ethnically and socioeconomically diverse patient population in rural, suburban and urban hospitals. Dr. Vazquez Guillamet has training in Infectious Diseases and Critical Care Medicine and experience in sepsis research. This supplement complements and broadens the initial K08 award. It serves as the natural next step in deepening her expertise in innovative methods. This supplement will provide the opportunity for meaningful collaborations with data scientists with ample expertise in unstructured data methods and data engineers specialized in ML methods. It will help Dr. Vazquez Guillamet to promote clinically applicable algorithms for challenging problems such as sepsis treatment. For this supplement, Dr. Vazquez Guillamet will continue the collaboration with her multidisciplinary team of mentors and add data engineering support. An accredited course in unsupervised machine learning will be added to her career development plan. She will continue her path to becoming an analytics translator at the intersection of clinical medicine and clinical applied informatics. The fertile research environment at Washington University in St. Louis with focus on data availability, the experienced mentorship team now incorporating data engineering expertise and a well-crafted career development plan will enable Dr. Vazquez Guillamet to achieve her long-term goal of becoming an independently funded clinician-investigator utilizing big data to develop applications for risk prediction, surveillance, and outcome comparisons in sepsis and antimicrobial resistance.

增刊摘要 败血症是一种毁灭性的综合征，是导致死亡、发病率和医疗费用的主要原因。它的 抗菌素耐药率的上升放大了影响。改善脓毒症预后的主要结果是 根据多药耐药(MDR)的估计风险，及时开出抗生素。人工智能 (AI)和机器学习(ML)是数据驱动的方法，在海量数据集中寻找模式。而当 AI/ML算法迅速发展并构建了成功的图像处理应用程序，承诺 AI/ML在脓毒症和抗菌药耐药性研究中的应用仍未完成。其主要原因是 有缺陷、无法访问和标签不佳的临床数据只允许电子健康的一小部分 要使用的记录(EHR)数据。更重要的是，临床描述，如笔记和成像报告，包含 自由文本格式的非结构化数据元素几乎从未使用过。我们的家长K08奖旨在确定 脓毒症表型面临MDR GNB的风险，这将使更好的抗生素处方实践和标准化 不同医院之间的比较。我们建议通过利用大数据和使用创新的 方法，如ML方法。本增刊将通过详细分析以下障碍来加强我们的项目 高效使用电子病历数据，包括非结构化数据元素，并提供数据工程解决方案。这个 目的为ML在脓毒症研究中的应用提供框架。展示可重复性和严密性 我们的ML方法和使算法和数据集根据公平和信任原则可访问将是 响应NIGMS和更广泛的NIH优先事项。我们的目标反映了这些优先事项：1)分析使用障碍 并为数据丰富提供数据工程解决方案，2)提取和 评估非结构化数据在开发ML脓毒症模型中的重要性，以及3)比较ML 使用非结构化和结构化数据与仅使用结构化数据的脓毒症模型，并确保算法 通过在基于性别和种族的兴趣小组中测试它的公平性。我们将纳入临床 来自我们医疗系统中15家医院的数据，这些医院为不同种族和社会经济背景的人提供服务 农村、郊区和城市医院的患者群体。 Vazquez Guillamet医生接受过传染病和重症监护医学方面的培训，并有脓毒症方面的经验 研究。这一补充补充和扩大了最初的K08奖项。这是自然而然的下一步 加深了她在创新方法方面的专业知识。这一补充将提供有意义的机会 与在非结构化数据方法和数据工程师方面拥有丰富专业知识的数据科学家合作 专门研究ML方法。它将帮助Vazquez Guillamet博士推广临床适用的算法 具有挑战性的问题，如脓毒症的治疗。 在本增刊中，Vazquez Guillamet博士将继续与她的多学科团队 指导，并增加数据工程支持。无监督机器学习的认证课程将是 添加到她的职业发展计划中。她将继续她的道路，成为一名分析翻译 临床医学与临床应用信息学的交叉。肥沃的研究环境 位于圣路易斯的华盛顿大学专注于数据可用性，经验丰富的指导团队现在 整合数据工程专业知识和精心设计的职业发展计划将使巴斯克斯博士 Guillamet将实现她的长期目标，即成为一名独立资助的临床医生-研究员，利用BIG 用于开发脓毒症风险预测、监测和结果比较应用程序的数据 抗菌素耐药性。