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

Alzheimer Disease Clinical Trial

— PAST-US  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04937959
• Other study ID #: NOV-0110-2
• Status: Recruiting
• Start date: January 22, 2021
• Est. completion date: August 30, 2022

Study information

• Verified date: June 2021
• Source: Novoic Limited
• Contact: Head of Clinical Operations
• Phone: 07849522891
• Email: s2[@]novoic.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 40
• Est. completion date: August 30, 2022
• Est. primary completion date: August 30, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 50 Years to 85 Years

Eligibility

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.

Related Conditions & MeSH terms

• Alzheimer Disease
• Alzheimer's Disease (Incl Subtypes)
• Cognitive Dysfunction
• Mild Cognitive Impairment
• Normal Cognition
• Preclinical Alzheimer's Disease
• Prodromal Alzheimer's Disease

Locations

Country	Name	City	State
United States	Syrentis Clinical Research	Santa Ana	California

Sponsors (1)

Lead Sponsor	Collaborator
Novoic Limited

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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