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| Name | Class |
|---|---|
| Sunrise Research Institute | UNKNOWN |
| Analytic Solutions Group | INDUSTRY |
| Kelley Medical Consultants LLC | UNKNOWN |
| R. P. Chiacchierini Consulting, LLC |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Trial Population | The trial population will be enrolled from adults presenting for elective, outpatient COVID-19 testing at a single center, potentially with multiple testing locations (subject to local needs at the time of the trial). The investigational device will be provided to Participants via a cell phone preloaded with Common off-the-shelf original equipment manufacturer (COTS OEM) software and the investigational Dx SaMD. The investigational device will be evaluated during a single encounter in which an FCV-SDS will be collected. No follow-up visits or participant contacts will be involved in this trial. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Diagnostic Test: Diagnostic Software as Medical Device | Device | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 | 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. | 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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Inclusion Criteria:
Exclusion Criteria:
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Adults seeking elective, outpatient COVID RT-PCR testing will be included.
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| Name | Affiliation | Role |
|---|---|---|
| Karl Kelley, MD | RAIsonance, Inc. | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sunrise Research Institute | Sunrise | Florida | 33325 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27654978 | Background | Amoh J, Odame K. Deep Neural Networks for Identifying Cough Sounds. IEEE Trans Biomed Circuits Syst. 2016 Oct;10(5):1003-1011. doi: 10.1109/TBCAS.2016.2598794. Epub 2016 Sep 16. | |
| 33301459 | Background | Arevalo-Rodriguez I, Buitrago-Garcia D, Simancas-Racines D, Zambrano-Achig P, Del Campo R, Ciapponi A, Sued O, Martinez-Garcia L, Rutjes AW, Low N, Bossuyt PM, Perez-Molina JA, Zamora J. False-negative results of initial RT-PCR assays for COVID-19: A systematic review. PLoS One. 2020 Dec 10;15(12):e0242958. doi: 10.1371/journal.pone.0242958. eCollection 2020. |
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| INDUSTRY |
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|
| 32774118 | Background | Assandri R, Canetta C, Vigano G, Buscarini E, Scartabellati A, Montanelli A. Laboratory markers included in the Corona Score can identify false negative results on COVID-19 RT-PCR in the emergency room. Biochem Med (Zagreb). 2020 Oct 15;30(3):030402. doi: 10.11613/BM.2020.030402. Epub 2020 Aug 5. |
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| 33187528 | Background | Chaimayo C, Kaewnaphan B, Tanlieng N, Athipanyasilp N, Sirijatuphat R, Chayakulkeeree M, Angkasekwinai N, Sutthent R, Puangpunngam N, Tharmviboonsri T, Pongraweewan O, Chuthapisith S, Sirivatanauksorn Y, Kantakamalakul W, Horthongkham N. Rapid SARS-CoV-2 antigen detection assay in comparison with real-time RT-PCR assay for laboratory diagnosis of COVID-19 in Thailand. Virol J. 2020 Nov 13;17(1):177. doi: 10.1186/s12985-020-01452-5. |
| 15057876 | Background | Chen YH, DeMets DL, Lan KK. Increasing the sample size when the unblinded interim result is promising. Stat Med. 2004 Apr 15;23(7):1023-38. doi: 10.1002/sim.1688. |
| 32839734 | Background | Imran A, Posokhova I, Qureshi HN, Masood U, Riaz MS, Ali K, John CN, Hussain MI, Nabeel M. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform Med Unlocked. 2020;20:100378. doi: 10.1016/j.imu.2020.100378. Epub 2020 Jun 26. |
| 32530131 | Background | Katz AP, Civantos FJ, Sargi Z, Leibowitz JM, Nicolli EA, Weed D, Moskovitz AE, Civantos AM, Andrews DM, Martinez O, Thomas GR. False-positive reverse transcriptase polymerase chain reaction screening for SARS-CoV-2 in the setting of urgent head and neck surgery and otolaryngologic emergencies during the pandemic: Clinical implications. Head Neck. 2020 Jul;42(7):1621-1628. doi: 10.1002/hed.26317. Epub 2020 Jun 12. |
| 25532164 | Background | Kosasih K, Abeyratne UR, Swarnkar V, Triasih R. Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis. IEEE Trans Biomed Eng. 2015 Apr;62(4):1185-94. doi: 10.1109/TBME.2014.2381214. Epub 2014 Dec 18. |
| 32422057 | Background | Kucirka LM, Lauer SA, Laeyendecker O, Boon D, Lessler J. Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure. Ann Intern Med. 2020 Aug 18;173(4):262-267. doi: 10.7326/M20-1495. Epub 2020 May 13. |
| 34812418 | Background | Laguarta J, Hueto F, Subirana B. COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings. IEEE Open J Eng Med Biol. 2020 Sep 29;1:275-281. doi: 10.1109/OJEMB.2020.3026928. eCollection 2020. |
| 26606168 | Background | Liu JM, You M, Wang Z, Li GZ, Xu X, Qiu Z. Cough event classification by pretrained deep neural network. BMC Med Inform Decis Mak. 2015;15 Suppl 4(Suppl 4):S2. doi: 10.1186/1472-6947-15-S4-S2. Epub 2015 Nov 25. |
| 22105690 | Background | Mehta CR, Pocock SJ. Adaptive increase in sample size when interim results are promising: a practical guide with examples. Stat Med. 2011 Dec 10;30(28):3267-84. doi: 10.1002/sim.4102. Epub 2010 Nov 30. |
| 32461287 | Background | Moore NM, Li H, Schejbal D, Lindsley J, Hayden MK. Comparison of Two Commercial Molecular Tests and a Laboratory-Developed Modification of the CDC 2019-nCoV Reverse Transcriptase PCR Assay for the Detection of SARS-CoV-2. J Clin Microbiol. 2020 Jul 23;58(8):e00938-20. doi: 10.1128/JCM.00938-20. Print 2020 Jul 23. |
| 33017966 | Background | Nemati E, Rahman MM, Nathan V, Vatanparvar K, Kuang J. A Comprehensive Approach for Classification of the Cough Type. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:208-212. doi: 10.1109/EMBC44109.2020.9175345. |
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| 32221523 | Background | Yu F, Yan L, Wang N, Yang S, Wang L, Tang Y, Gao G, Wang S, Ma C, Xie R, Wang F, Tan C, Zhu L, Guo Y, Zhang F. Quantitative Detection and Viral Load Analysis of SARS-CoV-2 in Infected Patients. Clin Infect Dis. 2020 Jul 28;71(15):793-798. doi: 10.1093/cid/ciaa345. |
| Background | Khomsay, S., Vanijjirattikhan, R., & Suwatthikul, J. (2019). Cough detection using PCA and Deep Learning. Paper presented at the 2019 International Conference on Information and Communication Technology Convergence (ICTC) |
| Background | Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105. |
| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
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| ID | Term |
|---|---|
| D004864 | Equipment and Supplies |
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