Novel Speech Recognizing App may help Predict HF by recognising fluid buildup in lungs: Study

• The researchers analyzed a total of 1,484 recordings and found that the 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%).
• As a complementary test, they further evaluated 72 untagged admissions and the discharge recordings from 9 patients and demonstrated for all 5 SMs. The system successfully segregated the recordings into 2 distinct unknown sets, which, when unblinded, were shown to correspond to the 2 different clinical statuses (ie, admission/discharge), with the exception of only 1 recording (2.2%).

The current observations provided substantial proof of concept that this novel automated speech processing and analysis approach can reliably identify these differences between 2 states of pulmonary congestion in patients with HF at the time of hospitalization for ADHF and following a full course of inpatient treatment.

The authors concluded, “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.”