A PORTABLE MULTI-MODAL OPTICO-IMPEDANCE SYTEM FOR EARLY WARNING OF PROGRESSION IN STABLE COVID-19 PATIENTS
用于对稳定的 COVID-19 患者病情进展进行早期预警的便携式多模态光阻抗系统
基本信息
- 批准号:10188939
- 负责人:
- 金额:$ 15.53万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-07-01 至 2024-01-31
- 项目状态:已结题
- 来源:
- 关键词:Accident and Emergency departmentAddressAlgorithmsAnimalsAssisted Living FacilitiesBlood VolumeCOVID-19COVID-19 detectionCOVID-19 diagnosisCOVID-19 early detectionCOVID-19 mortalityCOVID-19 patientCardiac OutputCardiovascular systemChestClinicClinicalClinical TrialsCommunitiesComplexComputer softwareDataData DiscoveryData SetDetectionDeteriorationDevelopmentDevice or Instrument DevelopmentDevicesDiagnosisDiagnosticDiagnostic testsDiseaseDropsEquipment and supply inventoriesEvaluationExtravascular Lung WaterFamily suidaeFeedbackFluid BalanceFundingGoalsHealthHealth PersonnelHealth care facilityHealthcareHealthcare SystemsHemorrhageHospitalizationHospitalsIndividualInstructionLearningLegal patentLong-Term CareLower Body Negative PressureLungMeasurementMedicalModelingMonitorMorbidity - disease rateNursing HomesOpticsOutpatientsOxygenParentsPatient MonitoringPatient-Focused OutcomesPatientsPeripheralPhasePopulationPreparationPrimary Health CarePrisonsPulse OximetryResourcesRespiratory SystemRiskSARS-CoV-2 infectionShockSmall Business Technology Transfer ResearchSpectrum AnalysisSymptomsSystemSystemic diseaseTechnologyTimeWateracute carealgorithm developmentcohortcollegecommercializationcommunity livingcoronavirus diseasecostdensityelectric impedanceelectrical impedance tomographyhigh riskhuman studyhuman subjectimproved outcomelight weightlung volumemortalitymultimodalitynon-invasive monitorpandemic diseaseportabilityprecision medicineprognosticprogramsprototyperesponsescale upsensortissue oxygenationtomographytransmission process
项目摘要
Project Summary / Abstract:
COVID-19, the clinical presentation associated with SARS-CoV-2 infection, has already profoundly impacted
healthcare systems globally. Of particular note, communities such as long-term care facilities, assisted living
communities, and prisons, are being devastated because of their high density of vulnerable individuals.
Nursing home residents, which represent only 0.5% of the US population, account for 25% of COVID-19
deaths. Early detection of COVID-19 progression in these patients is critical to improving outcomes of patients
who are in an early stable condition but at risk of deteriorating, but must be balanced with efficient use of
primary care resources and adequate protection of healthcare workers. An early alert to progression with a
high sensitivity and an acceptable rate of false-negatives would save patient lives, reduce exposure of
healthcare workers, and would also facilitate resource-shifting in the face of a surge. The time, money and
effort saved by allowing medical resources to be applied more accurately is the essence of precision medicine.
During our current STTR efforts, we have developed and evaluated an opto-impedance system capable of
integrating and classifying optical, electrical impedance spectroscopy and tomography data to detect change
from baseline signatures of early ongoing hemorrhage with high accuracy. This proposal will (1) scale up our
hardware inventory, (2) deploy on COVID-positive patients to collect continuous multiplex data and (3) retrain
our algorithms using the data to detect associated deterioration due to progression of COVID symptoms. This
multivariate approach that has already been demonstrated in other pre-shock models, has the potential to
provide critical diagnostic and prognostic feedback in high-risk individuals.
项目概要/摘要:
COVID-19是与SARS-CoV-2感染相关的临床表现,
全球医疗保健系统。特别值得注意的是,社区,如长期护理设施,辅助生活,
社区和监狱由于弱势个人的高度密集而遭到破坏。
仅占美国人口0.5%的养老院居民,占COVID-19的25%
死亡早期发现这些患者的COVID-19进展对于改善患者的预后至关重要
处于早期稳定状态但有恶化风险的人,但必须与有效使用
初级保健资源和充分保护保健工作者。早期预警进展,
高灵敏度和可接受的假阴性率将挽救患者的生命,
医疗保健工作者,也将促进资源转移,面对激增。时间,金钱,
节省人力,让医疗资源更精准地运用,才是精准医疗的本质。
在我们目前的STTR工作中,我们开发并评估了一种光阻抗系统,
集成和分类光学、电阻抗谱和断层扫描数据以检测变化
与早期持续出血的基线特征相比具有高准确性。该提案将(1)扩大我们的
硬件库存,(2)部署在COVID阳性患者上以收集连续的多路复用数据,以及(3)重新训练
我们的算法使用数据来检测由于COVID症状进展而导致的相关恶化。这
多变量方法已经在其他冲击前模型中得到证明,有可能
为高风险个体提供关键的诊断和预后反馈。
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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Ryan Joseph Halter其他文献
Ryan Joseph Halter的其他文献
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{{ truncateString('Ryan Joseph Halter', 18)}}的其他基金
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Classifying Oral Lesions with Chip-on-tip Electrical Impedance Sensing
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