GeneRAlizable Sepsis Phenotyping (GRASP) using Electronic Health Records and Continuous Monitoring Sensors

摘要 脓毒症是一种异质性综合征，其特征是由身体的炎症引起的全身炎症。 对感染的反应，是医院治疗的最昂贵和最致命的疾病， 仅在美国就有一例败血症相关的死亡病例。未经治疗的脓毒症可能导致血液扩张和渗漏 血管和严重低血压，需要血管活性药物（又名脓毒性休克），并最终损伤 肾脏、肺和肝脏（又称器官损伤），死亡率超过40%。成功预防和 脓毒症、脓毒性休克和器官损伤的管理依赖于临床医生预测和 估计风险，并实施正确的救生治疗（例如，抗生素、液体和血管加压药） 在正确的时间。近年来，数据驱动的建模已被证明能够早期预测脓毒症 并揭示败血症的集群（或表型），这可能有助于个性化治疗 干预措施。然而，跨越临床研究和改善患者之间的翻译鸿沟 护理还需要解决：1）通过更好的数据整合， 更智能的实验室订购，并利用持续监测可穿戴传感器; 2）互操作性和 临床数据和分析的可移植性; 3）原则性传播和实施研究;以及4） 教育下一代护理人员有效利用先进的分析工具。 拟议的研究计划建立在PI的K 01早期职业发展奖的基础上， 脓毒症预测分析算法（包括每小时EHR数据）的多中心开发和验证 涵盖五个地区超过50万名住院患者的艾德和住院患者 医疗保健系统）。从领域适应和多任务学习的最新进展中汲取见解 （机器学习的子领域），该项目旨在发现可推广的动态表型， 与脓毒症、脓毒性休克和下游器官损伤的预测和管理直接相关。 我们建议使用来自床边设备的高分辨率数据（例如， 监视器、透析器、透析和IV泵）和可穿戴设备（例如，连续血压和乳酸 传感器），以解决现有的监测差距。此外，该计划旨在推进FHIR（快速 医疗保健互操作性资源）和OMOP（观察性医学成果合作伙伴关系） 通过实施高分辨率数据源的特定资源，实现互操作性标准。 最后，这项研究计划将与我们的传播和 实施和医院质量改进团队，以确保早期评估可用性， 实施和有效的教育，以最大限度地发挥临床影响的潜力。

Abstract Sepsis, a heterogeneous syndrome characterized by whole-body inflammation caused by the body's response to an infection, is the most expensive and deadly condition treated in hospitals, with over 270,000 cases of sepsis-related deaths in the U.S. alone. Untreated sepsis may result in dilated and leaky blood vessels and severe hypotension requiring vasoactive medications (aka septic shock), and eventual injury to kidneys, lungs, and liver (aka organ injury) with mortality rates in excess of 40%. Successful prevention and management of sepsis, septic shock, and organ injury rely on the ability of clinicians to anticipate and estimate the risk, and administer the right life-saving treatments (e.g., antibiotics, fluids and vasopressors) at the right time. In recent years, data-driven modeling has been shown to enable early prediction of sepsis and to reveal clusters (or phenotypes) of sepsis, which may help with personalizing therapeutic interventions. However, crossing the translational chasm between clinical research and improving patient care also requires addressing 1) `data deserts' at different levels of care through better data integration, smarter lab ordering, and utilization of continuous monitoring wearable sensors; 2) interoperability and portability of clinical data and analytics; 3) principled dissemination and implementation studies; and 4) education of the next generation of caregivers to effectively utilize advanced analytical tools. The proposed research program builds upon PI's K01 early career development award focused on multicenter development and validation of sepsis predictive analytic algorithms (including hourly EHR data spanning ED and inpatient encounters from over 500,000 hospitalized patients across five district healthcare systems). Drawing insights from recent advances in domain adaptation and multi-task learning (sub-fields of machine learning), this project aims to discover generalizable dynamic phenotypes that are directly relevant to the prediction and management of sepsis, septic shock, and downstream organ injury. We propose to augment EHR-based analytics with high-resolution data from bedside devices (e.g., monitors, ventilators, dialysis, and IV pumps) and wearables (e.g., continuous blood pressure and lactate sensors) to address existing gaps in monitoring. Additionally, this program aims at advancing FHIR (Fast Healthcare Interoperability Resources) and OMOP (Observational Medical Outcomes Partnership) interoperability standards through the implementation of specific resources for high-resolution data sources. Finally, this research program will be conducted in close collaboration with our dissemination and implementation and hospital quality improvement teams to ensure early assessment of usability, barriers to implementation, and effective education to maximize the potential for clinical impact.