Vocal Biomarker Is Associated With Hospitalization and Mortality Among Heart Failure Patients

Abstract

Background The purpose of this article is to evaluate the association of voice signal analysis with adverse outcome among patients with congestive heart failure (CHF). Methods and Results The study cohort included 10 583 patients who were registered to a call center of patients who had chronic conditions including CHF in Israel between 2013 and 2018. A total of 223 acoustic features were extracted from 20 s of speech for each patient. A biomarker was developed based on a training cohort of non-CHF patients (N=8316). The biomarker was tested on a mutually exclusive CHF study cohort (N=2267) and was evaluated as a continuous and ordinal (4 quartiles) variable. Median age of the CHF study population was 77 (interquartile range 68-83) and 63% were men. During a median follow-up of 20 months (interquartile range 9-34), 824 (36%) patients died. Kaplan-Meier survival analysis showed higher cumulative probability of death with increasing quartiles (23%, 29%, 38%, and 54%; P<0.001). Survival analysis with adjustment to known predictors of poor survival demonstrated that each SD increase in the biomarker was associated with a significant 32% increased risk of death during follow-up (95% CI, 1.24-1.41, P<0.001) and that compared with the lowest quartile, patients in the highest quartile were 96% more likely to die (95% CI, 1.59-2.42, P<0.001). The model consistently demonstrated an independent association of the biomarker with hospitalizations during follow-up (P<0.001). Conclusions Noninvasive vocal biomarker is associated with adverse outcome among CHF patients, suggesting a possible role for voice analysis in telemedicine and CHF patient care.

Keywords: congestive heart failure; telemedicine; vocal biomarkers; voice.

Figure 1

Matrix representation of the acoustic signal. The image demonstrates a 2‐s snapshot of such matrix representation of the acoustic signal. The horizontal is the “time” axis with 100 points per s resolution, and the vertical axis is categorical, where each line represents a specific time‐series of low‐level features from the abovementioned feature sets.

Figure 2

Kaplan–Meier survival analysis. Kaplan–Meier curve showing overall cumulative survival probability for the 4 biomarker quartile groups.

Figure 3

Kaplan–Meier hospitalization probability. Kaplan–Meier curve showing overall cumulative hospitalization probability for the 4 biomarker quartile groups.

Figure 4

Time‐dependent receiver operator curve for 30‐, 90‐, 180‐, and 365‐day hospitalization. Receiver‐operating curves for selected univariate predictors of 30‐, 90‐, 180‐, and 365‐day hospitalization along with the vocal biomarker. All receiver‐operating curves and all calculated areas under the curve are for the outcome of hospitalization, and are calculated at 30 days (top left), 90 days (top right), 180 days (bottom left), and 365 days (bottom right). Please see text for categorical NRI analysis.