HEAR-MHE study: Automated speech analysis identifies minimal hepatic encephalopathy and may predict future overt hepatic encephalopathy

Background and Aims: HE is a major cause of poor quality of life in patients with cirrhosis. A simple diagnostic test to identify minimal hepatic encephalopathy (MHE) and predict future overt HE (OHE) is lacking. We aimed to evaluate if analysis of speech patterns using a modern speech platform (1) correlates with validated HE tests, (2) correlates with MHE, and (3) predicts future OHE. Approach and Results: In a two-center prospective cohort study of 200 outpatients with cirrhosis and 50 controls, patients underwent baseline speech recording and validated HE diagnostic testing with psychometric HE score. Patients were followed for 6 months to identify episodes of OHE. Seven hundred fifty-two speech variables were extracted using an automated speech analysis platform, reflecting the acoustic, lexical, and semantic aspects of speech. Patients with cirrhosis were median 63 years old (IQR 54, 68), 49.5% (99) were female. Over 100 speech variables were significantly associated with psychometric HE score ( p <0.05 with false discovery rate adjustment). A three-variable speech model (2 acoustic, 1 speech tempo variable) was similar to animal naming test in predicting MHE (AUC 0.76 vs. 0.69; p =0.11). Adding age and MELD-Na improved the accuracy of the speech model (AUC: 0.82). A combined clinical-speech model (“HEAR-MHE model”) predicted time to OHE with a concordance of 0.74 ( p =0.06). Conclusions: Automated speech analysis is highly correlated with validated HE tests, associated with MHE, and may predict future OHE. Future research is needed to validate this tool and to understand how it can be implemented in clinical practice.