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Trial record 5 of 9 for:    novoic

Amyloid Prediction in Early Stage Alzheimer's Disease Through Speech Phenotyping (AMYPRED-US)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04928976
Recruitment Status : Completed
First Posted : June 16, 2021
Last Update Posted : September 5, 2021
Sponsor:
Information provided by (Responsible Party):
Novoic Limited

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).

Condition or disease
Alzheimer Disease Preclinical Alzheimer's Disease Prodromal Alzheimer's Disease Alzheimer's Disease (Incl Subtypes) Mild Cognitive Impairment

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Study Type : Observational [Patient Registry]
Actual Enrollment : 67 participants
Observational Model: Case-Control
Time Perspective: Cross-Sectional
Target Follow-Up Duration: 2 Weeks
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
Actual Study Start Date : January 22, 2021
Actual Primary Completion Date : July 30, 2021
Actual Study Completion Date : July 30, 2021


Group/Cohort
Arm 1: MCI amyloid positive
  • Meet the National Institute of Aging - Alzheimer's Association (NIA-AA) core clinical criteria (2011) for MCI due to Alzheimer's
  • Positive amyloid PET or amyloid CSF status.
  • MMSE 23-30 (inclusive)
Arm 2: MCI amyloid negative
  • Non-AD Mild Cognitive Impairment (MCI)
  • Negative amyloid PET or amyloid CSF status.
  • MMSE 23-30 (inclusive)
Arm 3: CN amyloid positive
  • Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline
  • Positive amyloid PET or amyloid CSF status.
  • MMSE 26-30 (inclusive)
Arm 4: CN amyloid negative
  • Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline
  • Negative amyloid PET or amyloid CSF status.
  • MMSE 26-30 (inclusive)



Primary Outcome Measures :
  1. 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 Outcome Measures :
  1. The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms. [ Time Frame: baseline ]
  2. The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms. [ Time Frame: baseline ]
  3. The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms. [ Time Frame: baseline ]
  4. The sensitivity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms. [ Time Frame: baseline ]
  5. The specificity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms. [ Time Frame: baseline ]
  6. 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. [ Time Frame: baseline ]
  7. The AUC of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms. [ Time Frame: baseline ]
  8. The sensitivity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms. [ Time Frame: baseline ]
  9. The specificity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms. [ Time Frame: baseline ]
  10. The Cohen's kappa of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms. [ Time Frame: baseline ]
  11. The AUC of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms. [ Time Frame: baseline ]
  12. The sensitivity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms. [ Time Frame: baseline ]
  13. The specificity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms. [ Time Frame: baseline ]
  14. The Cohen's kappa of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms. [ Time Frame: baseline ]
  15. The AUC of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms. [ Time Frame: baseline ]
  16. 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). [ Time Frame: baseline ]
  17. 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. [ Time Frame: baseline ]


Information from the National Library of Medicine

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Ages Eligible for Study:   50 Years to 85 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
Participants will be identified primarily through data-base searches of the Investigator sites.
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.
  • 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
  • Have access to one of following 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; 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 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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04928976


Locations
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United States, California
Syrentis Clinical Research
Santa Ana, California, United States, 92705
Sponsors and Collaborators
Novoic Limited
Investigators
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Principal Investigator: Emil Fristed, MSc Novoic Limited
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Responsible Party: Novoic Limited
ClinicalTrials.gov Identifier: NCT04928976    
Other Study ID Numbers: NOV-0110
First Posted: June 16, 2021    Key Record Dates
Last Update Posted: September 5, 2021
Last Verified: June 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Novoic Limited:
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
Additional relevant MeSH terms:
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Alzheimer Disease
Cognitive Dysfunction
Dementia
Brain Diseases
Central Nervous System Diseases
Nervous System Diseases
Tauopathies
Neurodegenerative Diseases
Neurocognitive Disorders
Mental Disorders
Cognition Disorders