Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control
Martin Huecker1, Craig Schutzman1, Joshua French1
1Department of Emergency Medicine, University of Louisville, Louisville, KY, United States.
A novel mobile phone app uses sound recordings to accurately measure heart failure (HF) parameters like ejection fraction (EF) and stroke volume (SV). This technology can bring vital HF screening and monitoring to remote and underserved areas globally.
Area of Science:
- Cardiology
- Biomedical Engineering
- Digital Health
Background:
- Heart failure (HF) significantly impacts global morbidity, mortality, and healthcare costs.
- Accurate HF parameter measurement is limited in ambulatory, rural, and underserved settings, hindering telehealth applications.
- Current diagnostic limitations in remote settings necessitate innovative, accessible solutions for HF management.
Purpose of the Study:
- To describe a novel diagnostic technology for heart failure (HF) utilizing audio recordings from standard mobile phones.
- To develop and validate physics-based predictive algorithms for estimating key HF parameters.
- To assess the feasibility of using mobile phone acoustics for HF diagnosis and monitoring in diverse settings.
Main Methods:
- Prospective study involving acoustic microphone recordings from patients at two US clinical sites.
- Physics-based models were used to create predictive algorithms correlating acoustic data with echocardiogram-derived ejection fraction (EF) and stroke volume (SV).
- Recordings were obtained at the aortic site with patients in an upright position, analyzing acoustic frequencies to determine HF indicators.
Main Results:
- The study analyzed recordings from 113 participants, with no exclusions due to background noise.
- The physics-based model achieved high accuracy for EF (AUROC 0.955) and SV (AUROC 0.922) estimation.
- Acoustic frequencies related to SV were higher and more prone to distortion than those for EF, indicating distinct physiological signatures.
Conclusions:
- Mobile phone auscultation recordings can accurately estimate EF and SV, demonstrating a novel HF diagnostic approach.
- This technology holds potential for a mobile app to facilitate HF screening and monitoring in home, telehealth, rural, and underserved settings.
- The developed method offers accessible, high-quality diagnostic capabilities using readily available mobile phone equipment, expanding HF care globally.
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