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

摘要 精神错乱影响高达50%的住院老年患者(75+)，并与增加12倍的风险有关 阿尔茨海默病(AD)和AD相关痴呆(AD/ADRD)的后续发展以及 那些既有认知障碍的人认知能力下降的速度更快。然而，超过75%的精神错乱病例 在医院的环境中仍然没有得到诊断。也不知道谁会出现持续性的认知衰退。 入院后及入院后谁将恢复基线认知。该项目旨在开发数字语音 用于精神错乱诊断和预测(探索性)医院后认知功能下降的生物标记物， 最终目标是检测对持续性认知障碍和AD/ADRD的脆弱性。“骚乱发生在 语言“是精神错乱的诊断标准之一，但语言在很大程度上还没有作为一种诊断或 由于难以实现标准化和可靠的评估，这是一种预测工具。自动语音 分析是一种准确的、非侵入性的、有效的声学和文本语音量化方法 功能。由这种分析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但尚未被检测的标志物 诊断精神错乱或预测住院后认知功能下降。在我们的初步研究中，我们发现计算 在医院环境中收集语音特征是可行的，并允许进行更准确的分类 在老年人中精神错乱的状况比人口统计和疾病严重程度更重要。进一步的验证是 需要的。我们提案的目标是使用自动语音分析和机器学习(ML)来 开发数字生物标记物用于：(目标1)轻度认知障碍和非轻度认知障碍老年人的诊断 目的2)探讨院后认知功能减退的预测。学习 设计：我们将招募210名住院老年人(75+)，其中至少40%有既往认知能力 损害(MCI和AD/ADRD)。将对参与者进行评估，以确定是否存在预先存在的 认知障碍和精神错乱，并在两个时间点提供录音语音样本 在完成一系列语言任务的同时住院。语音样本将逐字记录 转录和自动计算机化处理，以量化与节奏、韵律、 组织、词汇特点和不流利。我们将评估3个月的住院后认知 结果。预期结果：如果成功，我们将显著提高 住院期间精神错乱的诊断，并确定AD/ADRD和持续性的易感性患者 认知能力下降。未来的纵向研究将评估精神错乱和易感性之间的关系。 通过自动语音分析和AD/ADRD的后续开发检测到。

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.