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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.

摘要

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.