Clinical trial NCT04928690: Prediction of Amyloid and Mild Cognitive Impairment in Early Stage Alzheimer's Disease From Remote Speech Phenotyping
Official title: Prediction of Amyloid and Mild Cognitive Impairment in Early Stage Alzheimer's Disease From Remote Speech Phenotyping (EARS)
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Alzheimer Disease
Alzheimer's Disease (Incl Subtypes)
Mild Cognitive Impairment
Keywords: 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
Lead sponsor: Novoic Limited
Sponsor class: INDUSTRY
First posted: 2021-06-16
Enrollment: 140
Sex: ALL
Minimum age (years): 50
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: US and UK-based study population.

Brief summary: The S22 study investigates, in a cross-sectional study, the ability of algorithms that analyse acoustic and linguistic patterns of spoken language to predict the presence of amyloid positivity in early stage Alzheimer's disease, specifically in Mild Cognitive Impairment (MCI) and cognitively normal (CN) cohorts; and whether similar algorithms can predict cognitive functioning, in classifying MCI vs CN.


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 cognitive impairment.
* Able to provide valid informed consent.
* Able to use, or has a caregiver who is able to use a smartphone device.
* Has access to a smartphone device running an operation system of Android 6 or above; or iOS 10 or above.

If taking part in the study through virtual visits, the following inclusion criteria also applies:

* Able to use, or has a caregiver who is able to use a personal computer, notebook or tablet.
* Has access to a personal computing device of that is running an operating system of macOS X with macOS 10.9 or later, or Windows 7 or above, or Ubuntu 12.04 or higher; OR has access internet browser software Internet Explorer version 11 or above; or Microsoft Edge version 12 or above, or Firefox version 27 or above, or Google Chrome version 30 or above, or Safari version 7 or above; AND capable of audio and video recording; AND able to connect to the internet.

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 criteria83); 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.
* Participant is, or previously has been enrolled in the Sponsor's NOV-0100 or NOV-0110 studies.

Primary outcomes:
measure: 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.
timeFrame: baseline
Secondary outcomes:
measure: The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.
timeFrame: baseline
measure: The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.
timeFrame: baseline
measure: The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.
timeFrame: baseline
measure: The sensitivity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
timeFrame: baseline
measure: The specificity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
timeFrame: baseline
measure: 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.
timeFrame: baseline
measure: The AUC of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.
timeFrame: baseline
measure: The sensitivity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
timeFrame: baseline
measure: The specificity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
timeFrame: baseline
measure: The Cohen's kappa of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
timeFrame: baseline
measure: The AUC of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.
timeFrame: baseline
measure: The sensitivity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
timeFrame: baseline
measure: The specificity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
timeFrame: baseline
measure: The Cohen's kappa of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
timeFrame: baseline
measure: The AUC of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.
timeFrame: baseline
measure: 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.
timeFrame: baseline
Other outcomes:
Locations:
Study officials:
affiliation: Novoic Ltd
name: Emil Fristed, MSc
role: PRINCIPAL_INVESTIGATOR
References: