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