Clinical feasibility of a non-invasive, low-cost wearable for measuring air trapping in COPD
Clinical feasibility of a non-invasive, low-cost wearable for measuring air trapping in COPD
批准号:
10080272
负责人:
Maria Artunduaga
金额:
$35.0万
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
项目状态:
已结题
起止时间:
2020-07-20 至 2023-06-30
关键词:
AcousticsActivities of Daily LivingAdultAirAlgorithmic AnalysisAlgorithmsAmericanAsthmaBluetoothBronchoconstrictionCaringCellular PhoneChronic BronchitisChronic Obstructive Airway DiseaseClinicClinicalClinical DataClinical ResearchClinical TrialsComputer softwareCross-Sectional StudiesDataData AnalysesDetectionDeteriorationDevelopmentDevice DesignsDevicesDiagnosisDisease ProgressionDyspneaEarly InterventionEdemaEmergency department visitExhalationExposure toFlareFrequenciesFunctional disorderFutureGeneral HospitalsGoalsHealthHealth Care CostsHeart RateHome environmentHospitalizationHospitalsHumidityIndividualInhalatorsInterventionLaboratoriesLungMachine LearningMeasurementMeasuresMedicalMedical HistoryMedical centerMethodsMilitary PersonnelMiniaturizationMinorMonitorMorbidity - disease rateMucous body substanceNoiseOralOxygen saturation measurementParticulatePatientsPharmaceutical PreparationsPhasePhysiciansPhysiologyPlethysmographyPopulationPulmonary EmphysemaPulmonary function testsPulse OximetryQuality of lifeQuestionnairesRefractoryRegulatory PathwayReportingRespiratory MusclesRespiratory physiologyRiskRunningSan FranciscoSecureSignal TransductionSmall Business Technology Transfer ResearchSmokerSpirometryStandardizationSymptomsSystemTechnologyTemperatureTestingTherapeutic InterventionTimeTranslatingUltrasonographyVeteransWhole Body PlethysmographyWireless TechnologyX-Ray Computed Tomographyaccurate diagnosisair monitoringbasecloud basedcohortcostdesignexperienceimprovedindexingmachine learning algorithmmetermicrophoneminiaturizemobile applicationmortalitynovelozone exposurepreventprototyperecruitrespiratoryscreeningsensorsmartphone Applicationsoftware developmentsuccesstoolusabilityuser-friendlywearable device
中文摘要
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英文摘要
ABSTRACT
Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of hospitalization in the US.
Exacerbations - a worsening or “flare up” of symptoms - cause most COPD hospitalizations. Since most
exacerbations can be treated with changes of inhalers and/or oral medications, at-home detection of lung
function deterioration may facilitate earlier intervention and help delay or prevent hospitalizations. The
Standards of Care for monitoring lung function are spirometry, plethysmography, and CT scan. However, these
are expensive methods and unsuited for continuous monitoring or at-home use. Various patient self-monitoring
approaches have been tried, for example, pulse oximetry, respiratory rate monitoring, and peak flow metering,
but their efficacy in reducing hospitalizations has been limited.
A common finding for all forms of COPD is air trapping, defined, as an abnormal increase in the volume
of air remaining in the lungs after exhalation is complete. A body of evidence definitively shows that air trapping
increases during exacerbations and decreases when exacerbations resolve. Recent reports show that air
trapping is an earlier harbinger of deteriorating lung function than spirometric changes, and can be measured
by low-frequency ultrasound (1-40 kHz). Acoustic monitoring of air trapping could provide clinicians with a non-
invasive tool when medical intervention is needed to avoid unnecessary ER visits and hospitalizations.
Respira Labs has developed a low-cost, non-invasive, acoustic-based wearable device that can
continually monitor lung resonance: Sylvee. The device uses known acoustic-based technology with machine-
learning algorithms to detect minor changes in lung resonance, which our preliminary results suggest
correspond to changes in air trapping. The overall objective of this project is to validate Sylvee's air trapping
algorithms in a cohort of 20 healthy controls and 40 COPD patients with and without air trapping, respectively.
In Aims 1 and 2, we will miniaturize and add sensors to the Sylvee device and develop a user interface (UI)
and a mobile application. In Aims 3 and 4, we will create an Air Trapping Index Report and validate it in a
cross-sectional study vis-à-vis whole body plethysmography as a control. Results of this project provide a
go/no-go development decision based on device function. We can apply these results in STTR Phase II, in a
larger clinical study to evaluate Sylvee as an at-home monitoring system, with a goal of reducing
hospitalizations by at least 30%. Ultimately, Sylvee will allow physicians to remotely monitor their patients' lung
function and adjust their medications to reduce healthcare costs and improve patients' quality of life.
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