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

使用电子健康记录和连续监测传感器进行通用脓毒症表型分析 (GRASP)

• 批准号: 10277331
• 负责人: SHAMIM NEMATI
• 金额: $ 39.5万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10277331
• 关键词: Address; Algorithms; Antibiotics; Blood Pressure; Blood Vessels; Caregivers; Caring; Cessation of life; Clinical; Clinical Data; Clinical Research; Collaborations; Computers; Data; Data Analytics; Data Sources; Deterioration; Development; Devices; Dialysis procedure; Dissemination and Implementation; Education; Electronic Health Record; Ensure; Fast Healthcare Interoperability Resources; Healthcare Systems; Hospitals; Hour; Hypotension; Infection; Inflammation; Injury to Kidney; Inpatients; Internet of Things; K-Series Research Career Programs; Life; Liquid substance; Liver; Lung; Machine Learning; Medical; Methods; Modeling; Monitor; Outcome; Patient Care; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Predictive Analytics; Prevention; Pump; Research; Research Personnel; Resolution; Resources; Risk; Risk Estimate; Savings; Sepsis; Septic Shock; Syndrome; Therapeutic Intervention; Time; Validation; Vasoconstrictor Agents; Ventilator; Work; advanced analytics; analytical tool; base; data integration; implementation study; improved; insight; interoperability; mortality; multi-task learning; next generation; organ injury; personalized therapeutic; portability; programs; response; sensor; usability; wearable sensor technology

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.

抽象的 败血症，一种由身体引起的全身炎症为特征的异质综合征 对感染的反应是医院中最昂贵，最致命的病情，超过270,000 仅在美国，与败血症有关的案件。未经处理的败血症可能导致血液渗透和漏水 血管和严重的低血压需要血管活性药物（又称败血性休克），最终受伤 死亡率超过40％的肾脏，肺和肝脏（又名器官损伤）。成功的预防和 败血症，败血性休克和器官损伤的管理取决于临床医生预期和 估计风险，并管理正确的救生治疗方法（例如抗生素，液体和加速器） 在正确的时间。近年来，数据驱动的建模已被证明可以早期预测败血症 并揭示败血症的簇（或表型），这可能有助于个性化治疗性 干预措施。但是，跨越临床研究与改善患者之间的翻译鸿沟 护理还需要通过更好的数据集成，在不同级别的护理水平上解决1）“数据荒漠”， 更明智的实验室订购，并利用连续监视可穿戴传感器； 2）互操作性和 临床数据和分析的可移植性； 3）原则传播和实施研究；和4） 对下一代护理人员有效利用先进的分析工具的教育。 拟议的研究计划基于PI的K01早期职业发展奖的重点是 败血症预测分析算法的多中心开发和验证（包括小时的EHR数据 跨越五个地区的500,000名住院患者的ED和住院接触 医疗保健系统）。从域适应和多任务学习的最新进展中汲取见解 （机器学习的子场），该项目旨在发现可概括的动态表型 与败血症，化脓性休克和下游器官损伤的预测和管理直接相关。 我们建议通过使用床头设备的高分辨率数据来增强基于EHR的分析（例如 监测器，呼吸机，透析和IV泵）和可穿戴设备（例如，连续血压和乳酸 传感器）解决监视中现有差距。此外，该计划旨在推进FHIR（快速 医疗保健互操作性资源）和OMOP（观察医学成果伙伴关系） 通过实施高分辨率数据源的特定资源，互操作性标准。 最后，该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，障碍 实施和有效的教育，以最大程度地提高临床影响的潜力。