Meet Inspiring Speakers and Experts at our 3000+ Global Conference Series Events with over 1000+ Conferences, 1000+ Symposiums
and 1000+ Workshops on Medical, Pharma, Engineering, Science, Technology and Business.

Explore and learn more about Conference Series : World's leading Event Organizer

Back

Helena Binetskaya

Helena Binetskaya

Healthy Networks, Belarus

Title: Possibilities of early detection of COPD exacerbations

Biography

Biography: Helena Binetskaya

Abstract

Statement of the Problem: COPD exacerbations contribute significantly to the total COPD burden on the healthcare systems due to considerable morbidity and mortality associated with COPD. At the same time, it is now recognized that many exacerbations are delayed to be reported or are not reported at all to healthcare professionals. This results in significant impact on exacerbation outcomes, hospitalization, and health status. Early detection and prompt treatment of COPD exacerbation can reduce their impact on health status and health care utilization.
Methodology & Theoretical Orientation: LungPass—an innovative device that can be used for early detection and monitoring of lung conditions, including COPD exacerbations. It works on the basis of lung sound detection using a digital stethoscope with a subsequent evaluation of auscultation data and questionnaires (including symptoms, peak expiratory flow rate (PEFR), adherence to therapy, etc.) through a developed mobile application. The first step of our study after the algorithm development was evaluation of its accuracy in lung sounds detection compared with “gold standard” and individual practitioners. The second step will be assessment of the possibility of using the device for early detection COPD exacerbations and managing them in cooperation with healthcare providers.
Findings: 300 audio records from patients with bronchitis, pneumonia, asthma and COPD were classified by the developed diagnostic algorithm and the overall classification accuracy was 90.8% (for normal breathing sensitivity (Sn): 82%, specificity (Sp): 99%, for wheezes+rhonchi Sn: 93%, Sp: 99%, for coarse crackles+fine crackles Sn: 98%, Sp: 90%). To date, a study protocol for second step of our research has been developed.
Conclusion & Significance: The developed device and app demonstrated high lung sound classification accuracy and together with analyzing change of symptoms, daily activity, using of short-acting bronchodilators, PEFR, etc. may be used for monitoring and early detection of COPD exacerbations.