Amyloid Prediction in Early Stage Alzheimer's Disease Through Speech Phenotyping - PAST Extension

Study Overview
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Brief Summary
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The primary objective of the study is to evaluate whether a set of algorithms analysing acoustic and linguistic patterns of speech can detect amyloid-specific cognitive impairment in early stage Alzheimer's disease, based on archival spoken or written language samples, as measured by the area under the curve (AUC) of the receiver operating characteristic curve of the binary classifier distinguishing between amyloid positive and amyloid negative arms. Secondary objectives include (1) evaluating how many years before diagnosis of Mild Cognitive Impairment (MCI) such algorithms work, as measured on binary classifier performance of the classifiers trained to classify MCI vs cognitively normal (CN) arms using archival material from the following time bins before MCI diagnosis: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years; (2) evaluating at what age such algorithms can detect later amyloid positivity, as measured on binary classifier performance of the classifiers trained to classify amyloid positive vs amyloid negative arms using archival material from the following age bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old.

Official Title
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A Study to Evaluate the Ability of Speech- and Language-based Digital Biomarkers to Detect and Characterise Prodromal and Preclinical Alzheimer's Disease in a Clinical Setting - AMYPRED-US PAST Extension Study

Conditions
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Alzheimer Disease, Alzheimer's Disease (Incl Subtypes), Preclinical Alzheimer's Disease, Prodromal Alzheimer's Disease, Mild Cognitive Impairment, Normal Cognition

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Subjects are fully eligible for and have completed the AMYPRED-US (Amyloid Prediction in early stage Alzheimer's disease from acoustic and linguistic patterns of speech) study. (See https://clinicaltrials.gov/ct2/show/NCT04928976) Subject has access to audio or written recordings created by them that are available for collection. Subject consents to take part in PAST extension study. Exclusion Criteria: Subject hasn't completed the full visit day in the AMYPRED-US study.

Ages Eligible for Study
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Minimum Age: 50 Years
Maximum Age: 85 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Arm 1: MCI amyloid positive<br>Meet the National Institute of Aging - Alzheimer's Association (NIA-AA) core clinical criteria (2011) for MCI due to Alzheimer's Positive amyloid PET or amyloid CSF status. MMSE 23-30 (inclusive) | |
| Arm 2: MCI amyloid negative<br>Non-AD Mild Cognitive Impairment (MCI) Negative amyloid PET or amyloid CSF status. MMSE 23-30 (inclusive) | |
| Arm 3: CN amyloid positive<br>Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline Positive amyloid PET or amyloid CSF status. MMSE 26-30 (inclusive) | |
| Arm 4: CN amyloid negative<br>Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline Negative amyloid PET or amyloid CSF status. MMSE 26-30 (inclusive) | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. | Using archival spoken or written language samples as input. | Up to 85 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms using archival spoken or written language samples as input. |  | Up to 85 years | 
| The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms using archival spoken or written language samples as input. |  | Up to 85 years | 
| The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms using archival spoken or written language samples as input. |  | Up to 85 years | 
| The AUC of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms. | Using archival spoken or written language samples as input in the following bins: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years before MCI diagnosis. | Up to 85 years | 
| The sensitivity of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms. | Using archival spoken or written language samples as input in the following bins: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years before MCI diagnosis. | Up to 85 years | 
| The specificity of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms. | Using archival spoken or written language samples as input in the following bins: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years before MCI diagnosis. | Up to 85 years | 
| The Cohen's kappa of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms. | Using archival spoken or written language samples as input in the following bins: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years before MCI diagnosis. | Up to 85 years | 
| The AUC of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. | Using archival spoken or written language samples as input in the following bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old. | Up to 85 years | 
| The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. | Using archival spoken or written language samples as input in the following bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old. | Up to 85 years | 
| The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. | Using archival spoken or written language samples as input in the following bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old. | Up to 85 years | 
| The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. | Using archival spoken or written language samples as input in the following bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old. | Up to 85 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Alzheimer's disease, Preclinical Alzheimer's disease, Prodromal Alzheimer's disease, Mild Cognitive Impairment, Normal Cognition, Amyloid, Speech, Acoustic, Language, Linguistic, Machine Learning, Artificial Intelligence