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.

补充摘要 脓毒症是一种破坏性综合征，是导致死亡、发病和医疗费用的主要原因。它是 抗菌素耐药性的上升会放大影响。改善脓毒症结局主要是由于 根据估计的多重耐药性（MDR）风险及时开出抗生素处方。人工智能 (AI) 和机器学习 (ML) 是在海量数据集中寻找模式的数据驱动方法。尽管 AI/ML 算法迅速发展并构建了成功的图像处理应用程序， 脓毒症和抗菌素耐药性研究中的人工智能/机器学习在很大程度上尚未实现。主要原因源于 临床数据有缺陷、无法访问且标签不明确，仅适用于电子医疗的一小部分 要使用的记录（EHR）数据。更重要的是，临床叙述，例如笔记和影像报告，其中包含 自由文本格式的非结构化数据元素几乎从未被使用。我们的母公司 K08 奖项旨在确定 脓毒症表型面临 MDR GNB 的风险，这将有助于更好的抗生素处方实践和标准化 各医院之间的比较。我们建议通过利用大数据和创新来实现我们的目标 方法，例如 ML 方法。本补充材料将通过详细分析阻碍我们发展的障碍来加强我们的项目 有效地使用 EHR 数据（包括非结构化数据元素）并提供数据工程解决方案。这 目标是为脓毒症研究中的机器学习应用提供框架。展示重复性和严谨性 我们的机器学习方法以及根据公平和信任原则使算法和数据集可访问将是 响应 NIGMS 和更广泛的 NIH 优先事项。我们的目标反映了以下优先事项：1) 分析使用障碍 EHR 结构化数据并提供数据丰富的数据工程解决方案，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的脓毒症模型 VS 仅结构化数据并确保算法 通过基于性别和种族的兴趣小组进行测试来确保公平性。我们将结合临床 来自我们医疗保健系统中 15 家医院的数据，为种族和社会经济多元化的人群提供服务 农村、郊区和城市医院的患者人数。 Vazquez Guillamet 博士接受过传染病和重症监护医学培训，并在脓毒症方面拥有丰富经验 研究。该补充补充并扩大了最初的 K08 奖项。这是自然的下一步 加深她在创新方法方面的专业知识。该补充品将提供有意义的机会 与在非结构化数据方法方面拥有丰富专业知识的数据科学家和数据工程师合作 专门研究机器学习方法。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，例如脓毒症治疗。 对于本补充品，Vazquez Guillamet 博士将继续与她的多学科团队合作 导师并添加数据工程支持。无监督机器学习的认可课程将是 添加到她的职业发展计划中。她将继续成为一名分析翻译员 临床医学和临床应用信息学的交叉点。肥沃的研究环境 圣路易斯华盛顿大学专注于数据可用性，现在经验丰富的导师团队 结合数据工程专业知识和精心设计的职业发展计划将使 Vazquez 博士能够 Guillamet 利用大笔资金实现了成为一名独立资助的临床医生研究员的长期目标 数据来开发脓毒症风险预测、监测和结果比较的应用程序 抗菌素耐药性。