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

Novoic Limited1 site in 1 country67 target enrollmentJanuary 22, 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: 67
Locations: 1
Primary Endpoint: 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 speech recordings as input.
Status: Completed
Last Updated: 4 years ago

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 detect amyloid-specific cognitive impairment in early stage Alzheimer's disease, as measured by the 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. Secondary objectives include (1) evaluating whether similar algorithms can detect amyloid-specific cognitive impairment in the cognitively normal (CN) and MCI Arms respectively, as measured on binary classifier performance; (2) whether they can detect MCI, as measured on binary classifier performance (AUC, sensitivity, specificity, Cohen's kappa), and the agreement between the PACC5 composite and the corresponding regression model predicting it in all Arms pooled (Wilcoxon signed-rank test, CIA); (3) evaluating variables that can impact performance of such algorithms of covariates from the speaker (age, gender, education level) and environment (measures of acoustic quality).

Registry

clinicaltrials.gov

Start Date

January 22, 2021

End Date

July 30, 2021

Last Updated

4 years ago

Study Type

Observational

Sex

All

Investigators

Sponsor

Novoic Limited

Responsible Party

Sponsor

Eligibility Criteria

Inclusion Criteria

•Amyloid status must be known, based on an amyloid PET scan or CSF amyloid test, no older than 30 months at the time of consent for Arm 2 and Arm 4 participants (amyloid negative Arms).
•Amyloid status must be known, based on an amyloid PET scan or CSF amyloid test, no older than 60 months at the time of consent for Arm 1 and Arm 3 (amyloid positive Arms).
•Subjects must be aged 50-85 (inclusive).
•Subjects must have MMSE scores of 23-30 (inclusive) based on a test not older than 1 month at the time of the visit.
•Date of diagnosis (if applicable) maximum of five years prior to consent.
•Subjects' first language must be English.
•Willing to participate in a study investigating speech and dementia.
•Availability of a person ('caregiver') who in the investigator's judgment has frequent and sufficient in-person contact with the participant, and is able to provide accurate information regarding the participant's cognitive and functional abilities. This is most likely met when living with a caregiver.
•Able to provide valid informed consent.
•Able to use, or has a caregiver who is able to use a smartphone device.

Exclusion Criteria

•Clinically significant unstable psychiatric illness in 6 months.
•Diagnosis of General Anxiety Disorder.
•Current, or history within the past 2 years of major depressive disorder diagnosis (according to DSM-5 criteria); or psychiatric symptoms that, in the opinion of the investigator, could interfere with study procedures.
•History or presence of stroke within the past 2 years.
•Documented history of transient ischemic attack or unexplained loss of consciousness within the last 12 months.
•The participant is using drugs to treat symptoms related to AD, and the doses of these drugs were not stable for at least 8 weeks prior to consent.

Outcomes

Primary Outcomes

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 speech recordings as input.

Time Frame: baseline

Secondary Outcomes

The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.(baseline)
The sensitivity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.(baseline)
For each classifier/regressor in outcome 1-16, the correlation between the AUC/CIA and each age group, gender and speech-to-reverberation modulation energy ratio group, as measured by the Kendall rank correlation coefficient.(baseline)
The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.(baseline)
The specificity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.(baseline)
The AUC of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.(baseline)
The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.(baseline)
The specificity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.(baseline)
The sensitivity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.(baseline)
The specificity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.(baseline)
The Cohen's kappa of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.(baseline)
The AUC of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.(baseline)
The Cohen's kappa of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.(baseline)
The AUC of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.(baseline)
The sensitivity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.(baseline)
The Cohen's kappa of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.(baseline)
The agreement between the PACC5 composite and the corresponding regression model predicting it in all four Arms, as measured by the coefficient of individual agreement (CIA).(baseline)