Automated speech analysis using the HearO app successfully differentiated discharge (dry) from admission (wet) recordings in 94% of patients with acute decompensated heart failure.
Observational (n=40)
Does automated speech analysis detect changes in fluid status in adult patients hospitalized with acute decompensated heart failure?
Automated speech analysis can successfully identify voice alterations reflecting fluid status changes in patients hospitalized with acute decompensated heart failure, offering a potential tool for remote monitoring.
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).
Amir et al. (Wed,) conducted a observational in Acute decompensated heart failure (ADHF) (n=40). HearO automated speech analysis application vs. Baseline (admission/wet status) was evaluated on Successful tagging of discharge recordings as distinctly different from baseline (wet). Automated speech analysis using the HearO app successfully differentiated discharge (dry) from admission (wet) recordings in 94% of patients with acute decompensated heart failure.