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

NCT04937959 — Unknown, 40 participants, Novoic Limited

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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 PAST Extension Study

Registry	NCT04937959, acronym PAST-US
Status	Unknown
Study type	Observational
Enrollment	40 (Estimated)
Sponsor	Novoic Limited (Industry)
Conditions	Alzheimer Disease, Alzheimer's Disease (Incl Subtypes), Preclinical Alzheimer's Disease, Prodromal Alzheimer's Disease, Mild Cognitive Impairment, Normal Cognition
Dates	start 2021-01-22, primary completion 2022-08-30 (estimated), completion 2022-08-30 (estimated), first posted 2021-06-24, last updated 2022-04-07
Locations	1 site in United States

-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, based on archival spoken or written language samples, as measured by the area under the curve (AUC) of the receiver operating characteristic curve of the binary classifier distinguishing between amyloid positive and amyloid negative arms. Secondary objectives include (1) evaluating how many years before diagnosis of Mild Cognitive Impairment (MCI) such algorithms work, as measured on binary classifier performance of the classifiers trained to classify MCI vs cognitively normal (CN) arms using archival material from the following time bins before MCI diagnosis: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years; (2) evaluating at what age such algorithms can detect later amyloid positivity, as measured on binary classifier performance of the classifiers trained to classify amyloid positive vs amyloid negative arms using archival material from the following age bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old.

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+Arms and interventions

Arm	Type	Interventions	Description
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)

+Outcome measures

Primary outcomes

The 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. [Up to 85 years]: Using archival spoken or written language samples as input.

Secondary outcomes

The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms using archival spoken or written language samples as input. [Up to 85 years]
The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms using archival spoken or written language samples as input. [Up to 85 years]
The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms using archival spoken or written language samples as input. [Up to 85 years]
The AUC of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms. [Up to 85 years]: Using archival spoken or written language samples as input in the following bins: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years before MCI diagnosis.
The sensitivity of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms. [Up to 85 years]: Using archival spoken or written language samples as input in the following bins: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years before MCI diagnosis.
The specificity of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms. [Up to 85 years]: Using archival spoken or written language samples as input in the following bins: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years before MCI diagnosis.
The Cohen's kappa of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms. [Up to 85 years]: Using archival spoken or written language samples as input in the following bins: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years before MCI diagnosis.
The AUC of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. [Up to 85 years]: Using archival spoken or written language samples as input in the following bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old.
The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. [Up to 85 years]: Using archival spoken or written language samples as input in the following bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old.
The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. [Up to 85 years]: Using archival spoken or written language samples as input in the following bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old.
The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms. [Up to 85 years]: Using archival spoken or written language samples as input in the following bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old.

+Eligibility

Sex: All, ages: 50 Years to 85 Years, accepts healthy volunteers

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 has access to audio or written recordings created by them that are available for collection.
* Subject consents to take part in PAST extension study.

Exclusion Criteria:

* Subject hasn't completed the full visit day in the AMYPRED-US study.

+Locations (1)

Facility	City	Country	Status
Syrentis Clinical Research	Santa Ana, California	United States	Recruiting

+Investigators

Emil Fristed, MSc, Principal investigator, Novoic Ltd