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

Alzheimer Disease Clinical Trial

— FUTURE-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 FUTURE Extension Study.

Clinical Trial Details — Status: Terminated

Administrative data

• NCT number: NCT04951284
• Other study ID #: NOV-0110-1
• Status: Terminated
• Start date: January 21, 2021
• Est. completion date: May 28, 2024

Study information

• Verified date: June 2024
• Source: Novoic Limited
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

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 predict change in Preclinical Alzheimer's Clinical Composite with semantic processing (PACC5) between baseline and +12 month follow up across all four Arms, as measured by the coefficient of individual agreement (CIA) between the change in PACC5 and the corresponding regression model, trained on baseline speech data to predict it. Secondary objectives include (1) evaluating whether similar algorithms can predict change in PACC5 between baseline and +12 month follow up in the cognitively normal (CN) and MCI populations separately; (2) evaluating whether similar algorithms trained to regress against PACC5 scores at baseline, still regress significantly against PACC5 scores at +12 month follow-up, as measured by the coefficient of individual agreement (CIA) between the PACC5 composite at +12 months and the regression model, trained on baseline speech data to predict PACC5 scores at baseline; (3) evaluating whether similar algorithms can classify converters vs non-converters in the cognitively normal Arms (Arm 3 + 4), and fast vs slow decliners in the MCI Arms (Arm 1 + 2), as measured by the Area Under the Curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity and Cohen's kappa of the corresponding binary classifiers. Secondary objectives include the objectives above, but using time points of +24 months and +36 months; and finally to evaluate whether the model performance for the objectives and outcomes above improved if the model has access to speech data at 1 week, 1 month, and 3 month timepoints.

Recruitment information / eligibility

• Status: Terminated
• Enrollment: 42
• Est. completion date: May 28, 2024
• Est. primary completion date: November 21, 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 consents to take part in FUTURE 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
• 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 agreement between the change in the PACC5 composite between baseline and +12 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).		12 months
Secondary	The agreement between the change in the PACC5 composite between baseline and +24 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).		24 months
Secondary	The agreement between the change in the PACC5 composite between baseline and +36 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).		36 months
Secondary	The agreement between the change in the PACC5 composite between baseline and +12 months and the corresponding regression model, trained on baseline speech data, to predict it in the CN Arms (Arms 3 and 4), as measured by the CIA.		12 months
Secondary	The agreement between the change in the PACC5 composite between baseline and +24 months and the corresponding regression model, trained on baseline speech data, to predict it in the CN Arms (Arms 3 and 4), as measured by the CIA.		24 months
Secondary	The agreement between the change in the PACC5 composite between baseline and +36 months and the corresponding regression model, trained on baseline speech data, predicting it in the MCI Arms (Arms 1 and 2), as measured by the CIA.		36 months
Secondary	The agreement between the PACC5 composite at +12 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms based on +12 month speech data, as measured by the coefficient of individual agreement (CIA).		12 months
Secondary	The agreement between the PACC5 composite at +24 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms based on +12 month speech data, as measured by the coefficient of individual agreement (CIA).		24 months
Secondary	The agreement between the PACC5 composite at +36 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms based on +12 month speech data, as measured by the coefficient of individual agreement (CIA).		36 months
Secondary	The agreement between the PACC5 composite and the corresponding regression model, trained on baseline speech data and +12 month speech data, as measured by the coefficient of individual agreement (CIA).		12 months
Secondary	The agreement between the PACC5 composite and the corresponding regression model, trained on baseline speech data and +24 month speech data, as measured by the coefficient of individual agreement (CIA).		24 months
Secondary	The agreement between the PACC5 composite and the corresponding regression model, trained on baseline speech data and +36 month speech data, as measured by the coefficient of individual agreement (CIA).		36 months
Secondary	The AUC of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.		12 months
Secondary	The AUC of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.		24 months
Secondary	The AUC of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.		36 months
Secondary	The sensitivity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.		12 months
Secondary	The sensitivity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.		24 months
Secondary	The sensitivity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.		36 months
Secondary	The specificity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.		12 months
Secondary	The specificity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.		24 months
Secondary	The specificity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.		36 months
Secondary	The Cohen's kappa of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.		12 months
Secondary	The Cohen's kappa of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.		24 months
Secondary	The Cohen's kappa of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.		36 months
Secondary	The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.		12 months
Secondary	The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.		24 months
Secondary	The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.		36 months
Secondary	The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.		12 months
Secondary	The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.		24 months
Secondary	The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.		36 months
Secondary	The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.		12 months
Secondary	The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.		24 months
Secondary	The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.		36 months
Secondary	The Cohen's kappa of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.		12 months
Secondary	The Cohen's kappa of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.		24 months
Secondary	The Cohen's kappa of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.		36 months