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

资料摘要 脓毒症是一种毁灭性的综合征，是死亡、发病和医疗费用的主要原因。其 抗生素耐药性的上升加剧了这种影响。改善脓毒症结局主要是由于 根据估计的多药耐药性（MDR）风险及时开具抗生素处方。人工智能 (AI)和机器学习（ML）是在大量数据集中寻找模式的数据驱动方法。而 AI/ML算法迅速发展并建立了成功的成像处理应用程序， AI/ML在脓毒症和抗生素耐药性研究中的应用在很大程度上仍未实现。主要原因来自于 缺乏、无法访问和标记不良的临床数据，仅允许一小部分电子健康 记录（EHR）数据。更重要的是，临床叙述，如笔记和成像报告，其中包含 几乎从不使用自由文本格式的非结构化数据元素。我们的K08奖项旨在确定 脓毒症表型存在MDR GNB的风险，这将使更好的抗生素处方实践和标准化成为可能。 比较各医院。我们建议通过利用大数据和使用创新技术来实现我们的目标。 例如ML方法。这一补充将通过详细分析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。的 目的是为ML在脓毒症研究中的应用提供框架。证明了可重复性和严谨性， 我们的机器学习方法，并使算法和数据集可根据公平和信任原则访问， 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 的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仅使用结构化数据并确保算法 通过在基于性别和种族的利益亚组中测试公平性。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， 农村、郊区和城市医院的患者人群。 博士Vazquez Guillamet接受过传染病和重症监护医学培训，并拥有败血症方面的经验 research.该补充并扩大了最初的K08奖项。这是自然的下一步 在深化她的专业知识，在创新的方法。这一补充将提供机会， 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 专注于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，如败血症治疗。 对于这一补充，Vazquez Guillamet博士将继续与她的多学科团队合作， 导师和添加数据工程支持。一个无监督机器学习的认证课程将在 加入到她的职业发展计划中。她将继续她的道路，成为一个分析翻译在 临床医学与临床应用信息学的交叉。肥沃的研究环境， 华盛顿大学圣路易斯分校，专注于数据可用性，经验丰富的导师团队现在 结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实现她的长期目标，成为一名独立资助的临床研究人员，利用大 数据开发应用程序，用于脓毒症的风险预测、监测和结果比较， 抗菌素耐药性

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.