Clinical trial NCT04851496: Amyloid Prediction in Early Stage Alzheimer's Disease From Acoustic and Linguistic Patterns of Speech - 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 - PAST Extension Study.
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Alzheimer Disease
Preclinical Alzheimer's Disease
Prodromal Alzheimer's 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: 2021-04-20
Enrollment: 80
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 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 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 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 (Amyloid Prediction in early stage Alzheimer's disease from acoustic and linguistic patterns of speech) study.

(See https://clinicaltrials.gov/ct2/show/NCT04828122)

* 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 study.

Primary outcomes:
measure: The primary outcome measure is 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, specificity and 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, sensitivity, specificity and Cohen's kappa of the binary classifiers distinguishing between MCI and cognitively normal (CN) arms.
timeFrame: Up to 85 years
measure: The AUC, sensitivity, specificity and 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: Birmingham
country: United Kingdom
facility: Re:Cognition Health
city: Guildford
country: United Kingdom
facility: Re:Cognition Health
city: London
country: United Kingdom
facility: Re:Cognition Health
city: Plymouth
country: United Kingdom
facility: Re:Cognition Health
Study officials:
affiliation: Novoic Limited
name: Emil Fristed, MSc
role: PRINCIPAL_INVESTIGATOR
References: