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.

Novoic Limited1 site in 1 country42 target enrollmentJanuary 21, 2021

ConditionsAlzheimer Disease Preclinical Alzheimer's Disease Prodromal Alzheimer's Disease Alzheimer's Disease (Incl Subtypes)Mild Cognitive Impairment

Overview

Phase: Not Applicable
Intervention: Not specified
Conditions: Alzheimer Disease
Sponsor: Novoic Limited
Enrollment: 42
Locations: 1
Primary Endpoint: 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).
Status: Terminated
Last Updated: last year

Overview Investigators Eligibility Criteria Outcomes Sites Similar Trials

Overview

Brief 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.

Registry

clinicaltrials.gov

Start Date

January 21, 2021

End Date

May 28, 2024

Last Updated

last year

Study Type

Observational

Sex

All

Investigators

Sponsor

Novoic Limited

Responsible Party

Sponsor

Eligibility Criteria

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.

Outcomes

Primary Outcomes

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).

Time Frame: 12 months

Secondary Outcomes

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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)