The AudibleHealth Dx is a diagnostic software as a medical device (Dx SaMD) consisting of an ensemble of software subroutines that interacts with a proprietary database of Signal Data Signatures (SDS), using Artificial Intelligence/Machine Learning (AI/ML) to analyze forced cough vocalization signal data signatures (FCV-SDS) for diagnostic purposes. This study will evaluate the performance of the AudibleHealth Dx in comparison to a standard of care Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID-19. A secondary purpose of the study will be usability testing of the device for participants and providers.
The study is a prospective, multi-site, non-inferiority trial comparing the AudibleHealth Dx to FDA approved COVID-19 RT-PCR testing to demonstrate non-inferiority of the PPA and NPA when using this device to diagnose COVID-19 illness. The AudibleHealth Dx test and the "BioFire Respiratory 2.1 (RP2.1)" (brand name) test will be performed for each participant during a single encounter. Participants and staff will be blinded to AudibleHealth Dx results and the RT-PCR status at the time of testing. No one will know both results in real-time except for the Site Coordinators and unblinded statistician specifically authorized to have these results for enrollment, audit, data tracking, and data compiling purposes. • Unblinding of the results will occur after the AudibleHealth Dx, RT-PCR, and the second RT-PCR results (if necessary for discordance) have been obtained. Results for the RT-PCR test will be received by the participant according to the clinical site's protocol. Target enrollment for this trial will be 65 COVID-19 positive cases and 152 COVID-19 negative cases, presuming a prevalence of 0.30 for a total of 217 subjects meeting all inclusion criteria.
Study Type
OBSERVATIONAL
Enrollment
514
AudibleHealth Dx is an investigational Dx SaMD consisting of an ensemble of software subroutines that interacts with a proprietary database of signal data signatures (SDS) using Artificial Intelligence/Machine Learning (AI/ML) to analyze forced cough vocalization signal data signatures (FCV-SDS) for diagnostic purposes. The intended use for the AudibleHealth Dx AI/ML-based Dx SaMD using FCV-SDS is for the diagnosis of acute and chronic illnesses, specifically COVID-19 illness for this study.
Sunrise Research Institute
Sunrise, Florida, United States
Non-inferiority of the positive percent agreement (PPA)
To demonstrate non-inferiority of the positive percent agreement (PPA) of the AudibleHealth Dx when compared to FDA approved SARS CoV-2 RT-PCR testing for the diagnosis of COVID-19 illness
Time frame: Participants will have a single encounter lasting less than one hour; anticipated study duration is 6 weeks. Target enrollment is 65 positive and 152 negative participants.
Non-inferiority of the negative percent agreement (NPA)
2\. To demonstrate non-inferiority of the negative percent agreement (NPA) of the AudibleHealth Dx when compared to FDA approved SARS-CoV-2 RT-PCR testing for the diagnosis of COVID-19 illness.
Time frame: Participants will have a single encounter lasting less than one hour; anticipated study duration is 6 weeks. Target enrollment is 65 positive and 152 negative participants.
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