Remote Speech Analysis in the Evaluation of Hospitalized Patients With Acute Decompensated Heart Failure
- PMID: 34969496
- DOI: 10.1016/j.jchf.2021.08.008
Remote Speech Analysis in the Evaluation of Hospitalized Patients With Acute Decompensated Heart Failure
Abstract
Objectives: This study assessed the performance of an automated speech analysis technology in detecting pulmonary fluid overload in patients with acute decompensated heart failure (ADHF).
Background: Pulmonary edema is the main cause of heart failure (HF)-related hospitalizations and a key predictor of poor postdischarge prognosis. Frequent monitoring is often recommended, but signs of decompensation are often missed. Voice and sound analysis technologies have been shown to successfully identify clinical conditions that affect vocal cord vibration mechanics.
Methods: Adult patients with ADHF (n = 40) recorded 5 sentences, in 1 of 3 languages, using HearO, a proprietary speech processing and analysis application, upon admission (wet) to and discharge (dry) from the hospital. Recordings were analyzed for 5 distinct speech measures (SMs), each a distinct time, frequency resolution, and linear versus perceptual (ear) model; mean change from baseline SMs was calculated.
Results: In total, 1,484 recordings were analyzed. Discharge recordings were successfully tagged as distinctly different from baseline (wet) in 94% of cases, with distinct differences shown for all 5 SMs in 87.5% of cases. The largest change from baseline was documented for SM2 (218%). Unsupervised, blinded clustering of untagged admission and discharge recordings of 9 patients was further demonstrated for all 5 SMs.
Conclusions: Automated speech analysis technology can identify voice alterations reflective of HF status. This platform is expected to provide a valuable contribution to in-person and remote follow-up of patients with HF, by alerting to imminent deterioration, thereby reducing hospitalization rates. (Clinical Evaluation of Cordio App in Adult Patients With CHF; NCT03266029).
Keywords: acute decompensated heart failure (ADHF); remote speech analysis; speech measure (SM).
Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.
Conflict of interest statement
Funding Support and Author Disclosures The study was supported by Cordio Medical Ltd. Dr Amir has been a paid consultant to Cordio Medical Ltd. Dr Abraham has received consulting fees from Abbott, Boehringer Ingelheim, CVRx, Edwards Lifesciences, and Respicardia; received salary support from V-Wave Medical; and research support from the U.S. National Institutes of Health/National Heart, Lung, and Blood Institute. Dr Anker has received grant support from Abbott and Vifor Pharma; and fees from Abbott, Bayer, Boehringer Ingelheim, Cardiac Dimension, Impulse Dynamics, Novartis, Servier, and Vifor Pharma. Dr Pinney has received consulting fees from Abbott, CareDx, Medtronic, NuPulse, and Procyrion. Dr Shallom is the Chief Technology Officer of Cordio Medical. Dr Lotan has been a board member of Cordio Medical Ltd; and has received lectures fee from Boehringer Ingelheim. Dr Edelman has been supported in part by a grant from the National Institutes of Health (NIH R01 49039); and a paid consultant to Cordio Medical Ltd. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Comment in
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Extracting Vocal Biomarkers for Pulmonary Congestion With a Smartphone App.JACC Heart Fail. 2022 Jan;10(1):50-51. doi: 10.1016/j.jchf.2021.10.007. Epub 2021 Dec 8. JACC Heart Fail. 2022. PMID: 34969497 Free PMC article. No abstract available.
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