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Publications

Journal Article Observational Study Research Support, Non-U.S. Gov't Epublish

PubMed: 40733560 DOI: 10.3390/v17070943

Evidence Generation for a Host-Response Biosignature of Respiratory Disease.

Viruses 17(7)

Authors: Kelly E Dooley, Michael Morimoto, Piotr Kaszuba, Margaret Krasne, Gigi Liu, Edward Fuchs, Peter Rexelius, Jerry Swan, Krzysztof Krawiec, Kevin Hammond, Stuart C Ray, Ryan Hafen, Andreas Schuh, Nelson L Shasha Jumbe

Affiliations

  • Vanderbilt University Medical Center, Nashville, TN 37232, USA.
  • Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
  • Level 42 AI, Inc. Mountain View, CA 94041, USA.
  • Hylomorph Solutions, Ltd. Glasgow G2 4JR, UK.
  • School of Computer Science, University of York, York YO10 5DD, UK.
  • Institute of Computing Science, Poznan University of Technology, 60-965 PoznaÅ„, Poland.

Abstract

BACKGROUND

In just twenty years, three dangerous human coronaviruses-SARS-CoV, MERS-CoV, and SARS-CoV-2 have exposed critical gaps in early detection of emerging viral threats. Current diagnostics remain pathogen-focused, often missing the earliest phase of infection. A virus-agnostic, host-based diagnostic capable of detecting responses to viral intrusion is urgently needed.

METHODS

We hypothesized that the lungs act as biomechanical instruments, with infection altering tissue tension, wave propagation, and flow dynamics in ways detectable through subaudible vibroacoustic signals. In a matched case-control study, we enrolled 19 RT-PCR-confirmed COVID-19 inpatients and 16 matched controls across two Johns Hopkins hospitals. Multimodal data were collected, including passive vibroacoustic auscultation, lung ultrasound, peak expiratory flow, and laboratory markers. Machine learning models were trained to identify host-response biosignatures from anterior chest recordings.

RESULTS

19 COVID-19 inpatients and 16 matched controls (mean BMI 32.4 kg/m 2, mean age 48.6 years) were successfully enrolled to the study. The top-performing, unoptimized, vibroacoustic-only model achieved an AUC of 0.84 (95% CI: 0.67-0.92). The host-covariate optimized model achieved an AUC of 1.0 (95% CI: 0.94-1.0), with 100% sensitivity (95% CI: 82-100%) and 99.6% specificity (95% CI: 85-100%). Vibroacoustic data from the anterior chest alone reliably distinguished COVID-19 cases from controls.

CONCLUSIONS

This proof-of-concept study demonstrates that passive, noninvasive vibroacoustic biosignatures can detect host response to viral infection in a hospitalized population and supports further testing of this modality in broader populations. These findings support the development of scalable, host-based diagnostics to enable early, agnostic detection of future pandemic threats (ClinicalTrials.gov number: NCT04556149).

MeSH

Lung, Virology, Viruses, Infections