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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)

基本信息

批准号：
10827775
负责人：
SHAMIM NEMATI
金额：
$ 7.15万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
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/10827775
关键词：
Address Algorithms Antibiotics Blood Pressure Blood Vessels Caregivers Caring Cessation of life Clinical Clinical Data Clinical Research Collaborations Computers Data Data Sources Deterioration Development Devices Dialysis procedure Dissemination and Implementation Education Electronic Health Record Ensure Fast Healthcare Interoperability Resources Healthcare Systems Hospitalization 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 Monitor Outcome Patient Care Patients Pattern Pharmaceutical Preparations Phenotype Predictive Analytics Prevention Pump Research Research Personnel Resolution Resources Risk Risk Estimate Sepsis Septic Shock Syndrome Therapeutic Intervention Time Validation Vasoconstrictor Agents Ventilator Work advanced analytics analytical tool data integration data-driven model implementation barriers implementation study improved insight interoperability mortality multi-task learning next generation organ injury personalized therapeutic portability programs response sensor usability wearable device wearable monitor wearable sensor technology

项目摘要

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.

抽象的脓毒症是一种异质综合征，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中最昂贵和最致命的疾病，超过 270,000 仅在美国就有与败血症相关的死亡病例。未经治疗的脓毒症可能会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（又名败血性休克），并最终受伤肾脏、肺和肝脏（又称器官损伤），死亡率超过 40%。成功预防和脓毒症、感染性休克和器官损伤的治疗依赖于临床医生预测和治疗的能力评估风险，并采取正确的救生治疗（例如抗生素、液体和血管加压药）在正确的时间。近年来，数据驱动建模已被证明可以实现脓毒症的早期预测并揭示脓毒症的集群（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转化鸿沟护理还需要通过更好的数据集成来解决不同护理级别的“数据荒漠”，更智能的实验室排序以及连续监控可穿戴传感器的利用； 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；和 4) 教育下一代护理人员有效利用先进的分析工具。拟议的研究计划以 PI 的 K01 早期职业发展奖为基础，重点关注脓毒症预测分析算法的多中心开发和验证（包括每小时的 EHR 数据）涵盖五个区超过 500,000 名住院患者的急诊室和住院患者就诊情况医疗保健系统）。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），该项目旨在发现可概括的动态表型与脓毒症、感染性休克和下游器官损伤的预测和治疗直接相关。我们建议利用来自床边设备的高分辨率数据来增强基于 EHR 的分析（例如，监护仪、呼吸机、透析和静脉泵）和可穿戴设备（例如连续血压和乳酸传感器）来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（观察医疗结果合作伙伴关系）通过实施高分辨率数据源的特定资源来制定互操作性标准。最后，该研究计划将与我们的传播和传播密切合作进行。实施和医院质量改进团队，确保及早评估可用性、障碍实施和有效的教育，以最大限度地发挥临床影响的潜力。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Predicting Hospital Readmission among Patients with Sepsis using Clinical and Wearable Data.

使用临床和可穿戴数据预测脓毒症患者的再入院率。

DOI：
10.1101/2023.04.10.23288368
发表时间：
2023
期刊：
medRxiv : the preprint server for health sciences
影响因子：
0
作者：
Amrollahi,Fatemeh;Shashikumar,SupreethPrajwal;Yhdego,Haben;Nayebnazar,Arshia;Yung,Nathan;Wardi,Gabriel;Nemati,Shamim
通讯作者：
Nemati,Shamim

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study.

DOI：
10.2196/43486
发表时间：
2023-02-13
期刊：
JOURNAL OF MEDICAL INTERNET RESEARCH
影响因子：
7.4
作者：
Rogers, Parker;Boussina, Aaron E.;Shashikumar, Supreeth P.;Wardi, Gabriel;Longhurst, Christopher A.;Nemati, Shamim
通讯作者：
Nemati, Shamim

Impact of a deep learning sepsis prediction model on quality of care and survival.