Identifying diagnostic biomarkers for Delirium and predicting cognitive Outcomes in hospitalized older adults using automated Speech Analysis (IDOSA)

使用自动语音分析 (IDOSA) 识别谵妄的诊断生物标志物并预测住院老年人的认知结果

• 批准号: 10806491
• 负责人: Liron Danay Sinvani
• 金额: $ 43.92万
• 依托单位: FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10806491
• 关键词: Acceleration; Acoustics; Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; Area Under Curve; Biological Markers; Characteristics; Classification; Clinical; Cognition; Cognitive; Delirium; Dementia; Development; Diagnosis; Digital biomarker; Elderly; Examinations and Diagnoses; Future; Genetic Transcription; Goals; Hospitalization; Hospitals; Impaired cognition; Intervention; Language; Longitudinal Studies; Machine Learning; Medicine; Methods; Modeling; Neuropsychology; Outcome; Participant; Patients; Pilot Projects; Prevalence; Psychiatry; Psychoses; Qualifying; Reading; Research Design; Risk; Sampling; Screening procedure; Series; Severities; Severity of illness; Speech; Standardization; Sum; Testing; Training; Validation; Work; accurate diagnosis; biomarker development; clinically significant; cognitive change; cognitive performance; comorbidity; computerized; confusion assessment method; demographics; diagnostic biomarker; diagnostic criteria; diagnostic tool; digital; improved; innovation; lexical; machine learning method; machine learning model; mild cognitive impairment; multidisciplinary; older patient; predictive tools; primary outcome; prognostic tool; recruit; secondary outcome

Abstract Delirium affects up to 50% of hospitalized older patients (75+) and is associated with a 12-fold increased risk for subsequent development of Alzheimer’s Disease (AD) and AD-related dementias (AD/ADRD) as well as accelerated cognitive decline in those with pre-existing cognitive impairment. Yet, over 75% of delirium cases in the hospital setting remain undiagnosed. It is also unknown who will develop persistent cognitive decline post-hospitalization and who will return to baseline cognition. This project seeks to develop digital speech biomarkers for delirium diagnosis and prediction (exploratory) of post-hospital cognitive decline, with the ultimate goal of detecting vulnerability toward persistent cognitive impairment and AD/ADRD. “Disturbance in language” is among the diagnostic criteria for delirium, but speech is largely unexplored as a diagnostic or prognostic tool due to the difficulty of achieving standardized and reliable assessments. Automated speech analysis is an accurate, non-invasive, and efficient method for quantifying acoustic and textual speech features. The computational speech features generated from such analyses are objective, non-invasive markers that have been used to accurately diagnose and predict dementia but have not been examined for diagnosing delirium or predicting post-hospital cognitive decline. In our pilot study, we found that computational speech features could be feasibly collected in the hospital setting and allowed for more accurate classification of delirium status among older adults than demographics and illness severity alone. Further validation is needed. The objectives of our proposal are to use automated speech analysis and machine learning (ML) to develop digital biomarkers for: (Aim 1) diagnosis of delirium in older adults with and without Mild Cognitive Impairment (MCI) and AD/ADRD; and (Aim 2) explore the prediction of post-hospital cognitive decline. Study Design: We will recruit 210 hospitalized older adults (75+), including at least 40% with pre-existing cognitive impairment (MCI and AD/ADRD). Participants will be assessed for the presence and severity of pre-existing cognitive impairment as well as delirium, and provide audio-recorded speech samples at 2 timepoints during hospitalization while completing a series of language tasks. Speech samples will undergo verbatim transcription and automated computerized processing to quantify speech features related to tempo, prosody, organization, lexical characteristics, and dysfluencies. We will assess 3-month post-hospital cognitive outcomes. Expected Outcomes: If successful, we will significantly improve the timeliness and accuracy of delirium diagnosis during hospitalization, and identify patients with vulnerability for AD/ADRD and lasting cognitive decline. Future longitudinal studies will evaluate the relationship between delirium and vulnerability detected through automated speech analysis and subsequent development of AD/ADRD.

摘要 谵妄影响高达50%的住院老年患者（75岁以上），并与12倍的风险增加有关 用于随后发展为阿尔茨海默病（AD）和AD相关痴呆（AD/ADRD）以及 加速认知功能减退的患者。然而，超过75%的谵妄病例 在医院里的病人还没有被确诊也不知道谁会出现持续的认知能力下降 住院后，谁将恢复到基线认知。该项目旨在开发数字语音 谵妄诊断和预测（探索性）院后认知功能下降的生物标志物， 最终目标是检测对持续性认知障碍和AD/ADRD的脆弱性。“骚乱， “语言”是谵妄的诊断标准之一，但言语在很大程度上未被用作诊断或 由于难以实现标准化和可靠的评估，自动语音 分析是一种精确、非侵入性和有效的量化声学和文本语音的方法 功能.从这种分析中产生的计算语音特征是客观的，非侵入性的 已被用于准确诊断和预测痴呆症的标记物，但尚未被检查 诊断谵妄或预测出院后认知能力下降。在我们的初步研究中，我们发现计算 语音特征可以在医院环境中可行地收集，并允许更准确的分类 老年人中谵妄状态比人口统计学和疾病严重程度单独。进一步确认 needed.我们提案的目标是使用自动语音分析和机器学习（ML）来 开发数字生物标志物用于：（目的1）诊断伴有和不伴有轻度认知障碍的老年人的谵妄 目的2：探讨住院后认知功能下降的预测。研究 设计：我们将招募210名住院的老年人（75岁以上），其中至少40%的患者先前存在认知障碍。 （MCI和AD/ADRD）。将评估受试者是否存在预先存在的 认知障碍以及谵妄，并在治疗期间的2个时间点提供录音语音样本。 住院期间完成一系列语言任务。语音样本将逐字 转录和自动计算机化处理来量化与克里思，韵律， 组织、词汇特征和不流利。我们将评估住院后3个月的认知能力， 结果。预期成果：如果成功，我们将大大提高 在住院期间进行谵妄诊断，并确定易患AD/ADRD的患者， 认知能力下降未来的纵向研究将评估谵妄和脆弱性之间的关系 通过自动语音分析和AD/ADRD的后续发展来检测。