ContinuOuS Monitoring Tool for Delayed Cerebral IsChemia (COSMIC)
迟发性脑缺血持续监测工具 (COSMIC)
基本信息
- 批准号:10736589
- 负责人:
- 金额:$ 63.66万
- 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:2023-08-01 至 2028-05-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAgeAge of OnsetAlgorithmsAmericanAneurysmal Subarachnoid HemorrhagesArchitectureArtificial IntelligenceAuthorization documentationBig Data to KnowledgeBiological AvailabilityBrainBrain InjuriesCerebral IschemiaClassificationClinicalClinical TrialsCodeCollaborationsCommunitiesCustomDataDecision MakingDeliriumDevicesDiagnosisDiagnostic testsEnsureEthicsEvaluationFast Healthcare Interoperability ResourcesFutureGenerationsHealthcareHeartHemorrhageHospitalizationHospitalsHourInflammatoryInformaticsInstitutionIntensive Care UnitsInterventionLaboratoriesLifeMethodologyModelingMonitorMorbidity - disease rateNatureNervous System TraumaObservational StudyOutcomePatient CarePatient-Focused OutcomesPatientsPerformancePersonsPhysiologicalPhysiologyPlayProbabilityProceduresProductionProductivityResearch DesignResourcesRiskRisk AssessmentRuptured AneurysmSafetySeizuresSignal TransductionSigns and SymptomsSiteSpecialistStrokeStroke preventionSubarachnoid HemorrhageSymptomsSyndromeTechnologyTestingTimeTranslatingTranslationsTrustUnited States National Institutes of HealthUpdateValidationVirginiaWorld Health Organizationaggressive therapyauthorityclinical decision supportclinical diagnosisclinical practiceclinical riskcomputer human interactiondesigndiagnostic strategydisabilityeffectiveness studyeffectiveness testingexperimental studyhealth information technologyimprovedindividual patientinnovationinteroperabilityiterative designloss of functionmachine learning modelmultidisciplinarynew technologynovelopen sourcepredictive modelingprospectiveprototypereal time modelsimulationskillsstroke symptomsupport toolstooluser centered design
项目摘要
Project Summary/Abstract
Approximately 30,000 Americans suffer an aneurysmal subarachnoid hemorrhage (SAH) each year, at a mean
age in the mid-50s leading to many years of lost productivity. Delayed cerebral ischemia (DCI) occurs in every
fifth patient with SAH with onset between 3-7 days after aneurysm rupture, and is the leading cause of
morbidity. Identifying the onset of DCI is challenging even though patients are closely monitored in intensive
care units, and too often DCI is only recognized in retrospect. There are several reasons for this: (1) cerebral
ischemia results in loss of function and is not passively observable in a neurologically injured patient, (2) can
be mistaken for mimics such as seizure or delirium and delay diagnosis, (3) confirmatory testing is resource
heavy and carries potential risk which necessitates surpassing a high threshold of suspicion. Existing DCI
prediction models do not offer the necessary timeliness nor precision. Improving timeliness and precision of
DCI prediction would enable interventions to prevent strokes in patients with SAH as well as reduce
overly aggressive treatment. Leveraging the impact that the inflammatory pathomechanism of DCI has on
systemic physiology, we created an artificial intelligence (AI) risk score for DCI using features derived from
universally available vital signs that updates with new information. In a pseudo-prospective experiment on data
from external institutions, this risk score uniquely met the criteria for an ideal situational monitor that does not
yet exist: continuous, non-invasive, independent of pretest probability, operator-independent, quantitative, and
timely (12 hours before clinical diagnosis). The World Health Organization standard of ethics for AI in
healthcare decrees that algorithms should be tested rigorously in the setting in which the technology will be
used, and ensure that it meets standards of safety and efficacy. The risks of an untested AI based clinical
decision support are misinterpretation and over-trusting with harm to patients at worst, and inconsequence at
best. This proposal encompasses the necessary steps to translate this promising model into a tool that can be
integrated into clinical practice. In Aim 1, we will perform a Silent Validation and Simulation Study to evaluate
the accuracy and acceptance of this novel AI technology in a realistic clinical setting. In Aim 2, we will use
Contextual Design methodology for user-centered participatory design and rapid agile prototyping to refine the
optimal implementation in clinician workflow. In Aim 3, we will produce an open standards-based interoperable
architecture that will be plug and play for implementation at external institutions. The translation of a DCI risk
model into a continuous monitor fills an important gap in the management of patients with SAH, and an open
standards architecture enables affordable and rapidly achievable dissemination of this novel technology, while
providing an essential validation for the standards community.
项目总结/文摘
项目成果
期刊论文数量(0)
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