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.
Jaskanwal Deep Singh Sara "I would say that this voice-analysis technology is not a standalone diagnostic tool," Jaskanwal Deep Singh Sara, MBChB, Mayo Clinic, Rochester, Minnesota, told theheart.org | Medscape Cardiology. "Once we filter out people who are unlikely to have disease, maybe we could use this as a screening tool to identify those with a higher pretest probability, and then start working them up with conventional methods. |
