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Blue Protocol and Eko Artificial Intelligence Are Best (BEA-BEST)

Blue Protocol and Eko Artificial Intelligence Are Best (BEA-BEST)

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05144633
Acronym
BEA-BEST
Enrollment
100
Registered
2021-12-03
Start date
2021-09-09
Completion date
2025-04-30
Last updated
2025-01-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Acute Respiratory Failure

Brief summary

This is an observational study that will be enrolling University of Louisville patients who present to the Emergency Department in Acute Respiratory Failure. This study will be to determine if the addition of Eko AI-assisted lung auscultation examination to a standard of care Pulmonary POCUS + assists with acute respiratory failure diagnosis.

Detailed description

Once identified and consented, the subject will undergo both POCUS and AI-assisted lung auscultation using an Eko CORE stethoscope. The POCUS protocol described here is the standard of care for patients who present in acute respiratory failure to the Emergency Room and both groups will receive this standardized care during the study. Upon hospital discharge, the final discharge diagnosis will be used as the ground truth for assessing the accuracy of the POCUS protocol. The POCUS exam and the EkoAI exam may be performed wherever the subject is located in the hospital when first enrolled in the study. If possible, both exams should be performed immediately, in either order. They will be performed by the same examiner. Both the POCUS and the EkoAI exam will be analysed by an investigator blinded to the final diagnosis.

Interventions

This study will be to determine if the addition of Eko AI-assisted lung auscultation examination to a standard of care Pulmonary POCUS + assists with acute respiratory failure diagnosis.

Sponsors

Eko Devices, Inc.
CollaboratorINDUSTRY
University of Louisville
Lead SponsorOTHER

Study design

Observational model
CASE_ONLY
Time perspective
CROSS_SECTIONAL

Eligibility

Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No

Inclusion criteria

* Adults aged 18 years or older presenting to the ED or admitted to the hospital within 12 hours of enrollment * Acute respiratory failure defined as new onset shortness of breath initiated within the last 7 days, and new or increasing need for oxygen therapy * The patient or patient's legal health care proxy consents to participation

Exclusion criteria

Unwillingness to consent * Patients with trauma as the cause of ARF * Patients with pneumothorax as the cause of ARF * Inability to perform pulmonary POCUS or lung auscultation (e.g. dressing on the chest) * Unwilling or unable to complete the minimum of 12 lung sound stethoscope recordings.

Design outcomes

Primary

MeasureTime frameDescription
Measure the accuracy of POCUS plus Eko for the diagnosis of acute respiratory failure. AI-assisted lung auscultation for the diagnosis of the case of acute respiratory failure.2 yearsPOCUS was described above. Ekos consists of a digital stethoscope. The lung auscultation pattern will be used to determine the cause of acute respiratory failure. POCUS and Eko will be integrated to establish the cause of acute respiratory failure. The reference standard will be the final diagnosis by the clinician.
Agreement between POCUS and Eko CORE2 yearsWe will measure Kappa statistic.
Measure the accuracy of POCUS for the diagnosis of acute respiratory failure2 yearsPOCUS consists of an ultrasound examination at bedside. We will perform lung ultrasound and the pattern of lung ultrasound will be used to diagnose the cause of acute respiratory failure. The reference standard will be the the final diagnosis by the clinician. The accuracy measures will include sensitivity, specificity, positive predictive value, and negative predictive value.

Countries

United States

Contacts

Primary ContactAndrea M Reyes Vega, M.D.
a0reye02@louisville.edu5028528884
Backup ContactRodrigo Cavallazzi, M.D.
rodrigo.cavallazzi@louisville.edu

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026