Transforming Resuscitation through Artificial INtelligence (TRAIN Study)
通过人工智能改变复苏(TRAIN 研究)
基本信息
- 批准号:10712407
- 负责人:
- 金额:$ 71.34万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-07-01 至 2028-05-31
- 项目状态:未结题
- 来源:
- 关键词:AccountingAcuteAdultAffectAlgorithmsAnti-Arrhythmia AgentsArtificial IntelligenceBlindedCarbon DioxideCaringCessation of lifeChestChildhoodClinicalDecision MakingDefibrillatorsDoseElectric CountershockElectrocardiogramExperimental ModelsFunctional disorderGoalsHeart ArrestHeterogeneityHospitalsHypoxiaImpedance CardiographyIndividualIntelligenceInterruptionInvestigationMeasuresMonitorMorphologic artifactsMorphologyMyocardial IschemiaOutcomePatientsPerformancePerfusionPhenotypePhysiologic pulsePhysiologicalPhysiologyPopulationProcessPrognosisProtocols documentationProviderPublic HealthResearchResuscitationSignal TransductionSystemTechniquesTechnologyTestingTimeTranslatingUnited StatesVasoconstrictor AgentsVentricular FibrillationVital Statusartificial intelligence methodblindclinical applicationclinical phenotypecomputerized data processingdata resourcedigitalelectric impedanceimprovedindividual patientindividualized medicineinnovationmortalitymultidisciplinarynovel strategiesoptimal treatmentsout-of-hospital cardiac arrestpersonalized approachprecision medicinepreventprognosticprospectiverandomized trialsignal processing
项目摘要
Project Summary
Mortality from out-of-hospital sudden cardiac arrest (OHCA) is a large public health burden, accounting for
approximately 10% of all deaths in the US. Because OHCA is a leading cause of mortality, advances in
resuscitation have the potential to improve public health. Currently resuscitation protocols use a one-size-fits-
all approach. However, we now understand that OHCA occurs via heterogeneous mechanisms and manifests
a time-dependent pathophysiology, which influences prognosis. The heterogeneity suggests discrete clinical
phenotypes and an opportunity for individualized therapy. Distinguishing information about patient physiology
can be harnessed from the defibrillator. Continuous bio-measures of ECG, end-tidal carbon dioxide (ETCO2),
and transthoracic impedance (TI) can determine physiologic status and potentially guide optimal treatment.
However, a real-time continuous approach to characterize a patient’s physiology and prognosis by
accurately determining the underlying rhythm and its vitality is not presently feasible without repeatedly
interrupting CPR. CPR interruption is required because chest compressions introduce ECG artifact that
prevents rhythm identification, prognostic assessment of rhythm morphology, and a patient’s underlying vital
status (vitality). However, CPR interruption is harmful since it disrupts perfusion in the otherwise pulseless
OHCA victim. Consequently, the current protocol is a compromise: CPR is interrupted every 2 minutes to help
inform care decisions though treatment proceeds empirically as CPR resumes and providers are typically
“blinded” to the actual underlying rhythm and vital status.
Emerging evidence from the proposal team highlight the ability to use signal processing techniques to
investigate the ECG, ETCO2, and TI defibrillator signals during CPR to improve OHCA resuscitation. These
investigations use artificial intelligence (AI) methods to determine a patient’s instantaneous physiological status
and predict downstream resuscitation outcomes. We propose an investigative plan that will:
1. Derive and validate an integrated ventricular fibrillation (VF) OHCA algorithm that incorporates and builds
upon previously-validated modular algorithms, using artificial intelligence (AI) methods that process and
integrate ECG, TI, and ETCO2 bio-signals during active CPR.
2. Prospectively evaluate the integrated algorithms and their validated building block components in distinct
adult and pediatric OHCA populations.
3. Conduct a simulated randomized trial among EMS to compare the described precision strategy to the
current-day, fixed protocol to understand how dynamic prompts of a precision strategy affect CPR metrics.
The project leverages an unparalleled data resource and a tested, multidisciplinary team with a track-record
of impactful resuscitation investigations involving novel approaches to AI and resuscitation. This consequent
precision strategy ultimately could transform resuscitation and improve public health.
项目总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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THOMAS D REA其他文献
THOMAS D REA的其他文献
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{{ truncateString('THOMAS D REA', 18)}}的其他基金
Human genetic variation and ventricular fibrillation resuscitation outcomes
人类遗传变异和心室颤动复苏结果
- 批准号:
7868043 - 财政年份:2008
- 资助金额:
$ 71.34万 - 项目类别:
Human genetic variation and ventricular fibrillation resuscitation outcomes
人类遗传变异和心室颤动复苏结果
- 批准号:
8079061 - 财政年份:2008
- 资助金额:
$ 71.34万 - 项目类别:
Human genetic variation and ventricular fibrillation resuscitation outcomes
人类遗传变异和心室颤动复苏结果
- 批准号:
7640734 - 财政年份:2008
- 资助金额:
$ 71.34万 - 项目类别:
Human genetic variation and ventricular fibrillation resuscitation outcomes
人类遗传变异和心室颤动复苏结果
- 批准号:
7380891 - 财政年份:2008
- 资助金额:
$ 71.34万 - 项目类别:
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