Bloom Patricia P, Fisher Caitlyn J, Tedesco Nicholas, Kamdar Neil, Garrido-Trevino Luis, Robin Jessica, Asrani Sumeet K, Lok Anna S
Department of Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Stanford University, Stanford, California, USA.
Hepatology. 2025 Jun 1;81(6):1740-1752. doi: 10.1097/HEP.0000000000001086. Epub 2024 Sep 12.
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.
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).
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.
肝性脑病（HE）是导致肝硬化患者生活质量低下的主要原因。目前缺乏一种简单的诊断测试来识别轻微肝性脑病（MHE）并预测未来显性肝性脑病（OHE）。我们旨在评估使用现代语音平台分析语音模式是否（1）与经过验证的肝性脑病测试相关，（2）与轻微肝性脑病相关，以及（3）预测未来显性肝性脑病。
在一项针对200例肝硬化门诊患者和50例对照的两中心前瞻性队列研究中，患者接受了基线语音记录，并通过心理测量肝性脑病评分进行了经过验证的肝性脑病诊断测试。对患者进行了6个月的随访，以确定显性肝性脑病发作情况。使用自动语音分析平台提取了752个语音变量，反映了语音的声学、词汇和语义方面。肝硬化患者的年龄中位数为63岁（四分位间距54,68），49.5%（99例）为女性。超过100个语音变量与心理测量肝性脑病评分显著相关（经错误发现率调整后p<0.05）。一个三变量语音模型（2个声学变量，1个语速变量）在预测轻微肝性脑病方面与动物命名测试相似（曲线下面积0.76对0.69；p =0.11）。加入年龄和终末期肝病模型钠评分（MELD-Na）提高了语音模型的准确性（曲线下面积：0.82）。一个联合临床-语音模型（“HEAR-MHE模型”）预测显性肝性脑病发生时间的一致性为0.74（p =0.06）。
自动语音分析与经过验证的肝性脑病测试高度相关，与轻微肝性脑病相关，并且可能预测未来显性肝性脑病。需要进一步的研究来验证该工具，并了解其在临床实践中的应用方式。
