Clinical trial NCT04937959: Amyloid Prediction in Early Stage Alzheimer's Disease Through Speech Phenotyping - PAST Extension
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 - AMYPRED-US PAST Extension Study
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Alzheimer Disease
Alzheimer's Disease (Incl Subtypes)
Preclinical Alzheimer's Disease
Prodromal Alzheimer's Disease
Mild Cognitive Impairment
Normal Cognition
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: 2021-06-24
Enrollment: 40
Sex: ALL
Minimum age (years): 50
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Participants will be identified from participants of the AMYPRED-US study.

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, 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.


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 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.

Primary outcomes:
measure: 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.
timeFrame: Up to 85 years
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 using archival spoken or written language samples as input.
timeFrame: Up to 85 years
measure: 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.
timeFrame: Up to 85 years
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 using archival spoken or written language samples as input.
timeFrame: Up to 85 years
measure: The AUC of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms.
timeFrame: Up to 85 years
measure: The sensitivity of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms.
timeFrame: Up to 85 years
measure: The specificity of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms.
timeFrame: Up to 85 years
measure: The Cohen's kappa of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms.
timeFrame: Up to 85 years
measure: The AUC of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms.
timeFrame: Up to 85 years
measure: The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms.
timeFrame: Up to 85 years
measure: The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms.
timeFrame: Up to 85 years
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: Up to 85 years
Other outcomes:
Locations:
city: Santa Ana
country: United States
facility: Syrentis Clinical Research
state: California
Study officials:
affiliation: Novoic Ltd
name: Emil Fristed, MSc
role: PRINCIPAL_INVESTIGATOR
References: