'Voice Biomarker' Shows Progress in CAD Risk Assessment
Vocal characteristics that can't actually be heard, discernible only by computer, might help identify individuals with confirmed or suspected heart disease who are at increased risk for a cardiovascular (CV) event over the next several years, a prospective study suggests. The research is only the latest to suggest a potential role for "voice biomarkers" — acoustic features discernible with machine-learning algorithms — for CV risk assessment, with implications for screening, noninvasive risk stratification, and telemedicine, investigators say. Voice recordings of the study's 108 patients were processed and assigned scores based on how much they expressed the inaudible biomarker. Patients with assigned scores in the top third, compared with scores in the lower two-thirds, showed 2.6 times the risk of developing acute coronary syndrome (ACS) or presenting to the hospital with chest pain over about 24 months. They showed triple the risk for a positive stress test or coronary artery disease (CAD) at angiography.
@trina Earlier research demonstrated significant associations between the same or similar voice biomarkers, or the separate constituent voice signal features, and baseline CAD, pulmonary hypertension, and, in patients with heart failure, mortality and risk for hospitalization. But the current study is the first to use the voice-analysis techniques to prospectively forecast CAD events, Sara observed. The voice biomarker it tested was derived — using proprietary artificial intelligence (AI) methods (Vocalis Health) — from voice signals from more than 10,000 patients with chronic diseases, he noted. The resulting algorithms were developed to analyze 80 voice-signal features, such as frequency, amplitude, pitch, and cadence. Patients in the current study, who had been referred for coronary angiography for various indications — including angina-like chest pain, a positive stress test, hospitalization with ACS, or preoperative evaluation — each provided three 30-second voice recordings that were processed for the distinct voice signal features.