0
research-article

Early Acoustic Warning for the Onset of Acute Chest Syndrome in Sickle Cell Patients

[+] Author and Article Information
Brian Henry

Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607
bhenry4@uic.edu

Gardner Yost

Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607
gardneryost@gmail.com

Robert Molokie

Department of Medicine, University of Illinois at Chicago, Chicago, IL 60612; Jesse Brown VA Medical Center, Chicago, IL 60612
remoloki@uic.edu

Thomas J. Royston

Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607
troyston@uic.edu

1Corresponding author.

ASME doi:10.1115/1.4039177 History: Received October 02, 2017; Revised January 15, 2018

Abstract

Acute chest syndrome (ACS) is a leading cause of death for those with sickle cell disease (SCD). ACS is defined by the development of a new pulmonary infiltrate on chest x-ray, with fever and respiratory symptoms. Efforts have been made to apply various technologies in the hospital setting to provide earlier detection of ACS than x-ray, but they are expensive, increase radiation exposure to the patient, and are not technologies that are easily transferrable for home use to help with early diagnosis. We present preliminary studies on patients suggesting that acoustical measurements recorded quantitatively with contact sensors (electronic stethoscopes) and analyzed using advanced computational analysis methods may provide an earlier diagnostic indicator of the onset of ACS than is possible with current clinical practice. In silico models of respiratory acoustics utilizing image-based and algorithmically developed lungs with full conducting airway trees support and help explain measured acoustic trends and provide guidance on the next steps in developing and translating a potentially life-saving diagnostic approach. More broadly, the experimental and computational techniques introduced herein, while focused on monitoring and predicting the onset of ACS, could catalyze further advances in mobile health (mhealth)-enabled, computer-based auscultative diagnoses for a wide range of cardiopulmonary pathologies.

Copyright (c) 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In