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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Nov 17, 2022
Date Accepted: Feb 28, 2023
Date Submitted to PubMed: Mar 7, 2023

The final, peer-reviewed published version of this preprint can be found here:

Development and Validation of a Respiratory-Responsive Vocal Biomarker–Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19

Kaur S, Larsen E, Harper J, Purandare B, Uluer A, Hasdianda MA, Umale N, Killeen J, Castillo E, Jariwala S

Development and Validation of a Respiratory-Responsive Vocal Biomarker–Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19

J Med Internet Res 2023;25:e44410

DOI: 10.2196/44410

PMID: 36881540

PMCID: 10131712

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Implementation and evaluation of a novel respiratory-responsive vocal biomarker to aid in the detection of COVID-19

  • Savneet Kaur; 
  • Erik Larsen; 
  • James Harper; 
  • Bharat Purandare; 
  • Ahmet Uluer; 
  • Mohammad Adrian Hasdianda; 
  • Nikita Umale; 
  • James Killeen; 
  • Edward Castillo; 
  • Sunit Jariwala

ABSTRACT

Background:

We present results of a multi-site validation trial of a respiratory-responsive vocal-biomarker (RRVB) platform, originally developed using an asthma dataset, to detect active COVID-19 infection in patients presenting to hospitals in the US and India.

Objective:

The RRVB model under study utilizes a briefly held vowel elicitation captured on patients’ smartphones and produces a risk score calibrated to estimate whether the speaker likely belongs to a group with an asthma diagnosis or a healthy control group.

Methods:

To determine whether this RRVB model can also detect patients with active COVID-19 infection, a total of 497 participants (COVID-19 positive, symptomatic but COVID-19 negative, and healthy controls) were enrolled across 4 clinical sites (DMH, Pune India; Montefiore Medical Center, Bronx NY; Brigham & Women’s Hospital, Boston MA; UCSD Health, San Diego CA) and provided voice samples and symptom reports on their personal smartphones.

Results:

Compared with clinical diagnosis of COVID-19 confirmed by RT-PCR, the RRVB model performed with sensitivity of 73.2%, specificity of 62.9%, and odds ratio of 4.64 (p<0.0001). Patients experiencing respiratory symptoms were detected more frequently vs. those not experiencing respiratory symptoms and completely asymptomatic patients (78.4% vs. 67.4% vs. 68.0%). This RRVB model is not a COVID-19 test, but these results demonstrate its meaningful potential to serve as a pre-screening tool that could, in combination with temperature and symptom reports, be used to identify subjects at risk for COVID-19 infection and encourage targeted testing.

Conclusions:

The generalizability of this model for detection of possible disease state for multiple conditions having in common respiratory symptoms across different linguistic and geographic contexts suggests a potential path to development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future


 Citation

Please cite as:

Kaur S, Larsen E, Harper J, Purandare B, Uluer A, Hasdianda MA, Umale N, Killeen J, Castillo E, Jariwala S

Development and Validation of a Respiratory-Responsive Vocal Biomarker–Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19

J Med Internet Res 2023;25:e44410

DOI: 10.2196/44410

PMID: 36881540

PMCID: 10131712

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