Clinical trial NCT04951284: Amyloid Prediction in Early Stage Alzheimer's Disease Through Speech Phenotyping - FUTURE 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 FUTURE Extension Study.
Study type: OBSERVATIONAL
Overall status: TERMINATED
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: 2021-07-06
Enrollment: 42
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: PROSPECTIVE
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 predict change in Preclinical Alzheimer's Clinical Composite with semantic processing (PACC5) between baseline and +12 month follow up across all four Arms, as measured by the coefficient of individual agreement (CIA) between the change in PACC5 and the corresponding regression model, trained on baseline speech data to predict it. Secondary objectives include (1) evaluating whether similar algorithms can predict change in PACC5 between baseline and +12 month follow up in the cognitively normal (CN) and MCI populations separately; (2) evaluating whether similar algorithms trained to regress against PACC5 scores at baseline, still regress significantly against PACC5 scores at +12 month follow-up, as measured by the coefficient of individual agreement (CIA) between the PACC5 composite at +12 months and the regression model, trained on baseline speech data to predict PACC5 scores at baseline; (3) evaluating whether similar algorithms can classify converters vs non-converters in the cognitively normal Arms (Arm 3 + 4), and fast vs slow decliners in the MCI Arms (Arm 1 + 2), as measured by the Area Under the Curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity and Cohen's kappa of the corresponding binary classifiers. Secondary objectives include the objectives above, but using time points of +24 months and +36 months; and finally to evaluate whether the model performance for the objectives and outcomes above improved if the model has access to speech data at 1 week, 1 month, and 3 month timepoints.


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 consents to take part in FUTURE extension study.

Exclusion Criteria:

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

Primary outcomes:
measure: The agreement between the change in the PACC5 composite between baseline and +12 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).
timeFrame: 12 months
Secondary outcomes:
measure: The agreement between the change in the PACC5 composite between baseline and +24 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).
timeFrame: 24 months
measure: The agreement between the change in the PACC5 composite between baseline and +36 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).
timeFrame: 36 months
measure: The agreement between the change in the PACC5 composite between baseline and +12 months and the corresponding regression model, trained on baseline speech data, to predict it in the CN Arms (Arms 3 and 4), as measured by the CIA.
timeFrame: 12 months
measure: The agreement between the change in the PACC5 composite between baseline and +24 months and the corresponding regression model, trained on baseline speech data, to predict it in the CN Arms (Arms 3 and 4), as measured by the CIA.
timeFrame: 24 months
measure: The agreement between the change in the PACC5 composite between baseline and +36 months and the corresponding regression model, trained on baseline speech data, predicting it in the MCI Arms (Arms 1 and 2), as measured by the CIA.
timeFrame: 36 months
measure: The agreement between the PACC5 composite at +12 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms based on +12 month speech data, as measured by the coefficient of individual agreement (CIA).
timeFrame: 12 months
measure: The agreement between the PACC5 composite at +24 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms based on +12 month speech data, as measured by the coefficient of individual agreement (CIA).
timeFrame: 24 months
measure: The agreement between the PACC5 composite at +36 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms based on +12 month speech data, as measured by the coefficient of individual agreement (CIA).
timeFrame: 36 months
measure: The agreement between the PACC5 composite and the corresponding regression model, trained on baseline speech data and +12 month speech data, as measured by the coefficient of individual agreement (CIA).
timeFrame: 12 months
measure: The agreement between the PACC5 composite and the corresponding regression model, trained on baseline speech data and +24 month speech data, as measured by the coefficient of individual agreement (CIA).
timeFrame: 24 months
measure: The agreement between the PACC5 composite and the corresponding regression model, trained on baseline speech data and +36 month speech data, as measured by the coefficient of individual agreement (CIA).
timeFrame: 36 months
measure: The AUC of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.
timeFrame: 12 months
measure: The AUC of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.
timeFrame: 24 months
measure: The AUC of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.
timeFrame: 36 months
measure: The sensitivity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.
timeFrame: 12 months
measure: The sensitivity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.
timeFrame: 24 months
measure: The sensitivity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.
timeFrame: 36 months
measure: The specificity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.
timeFrame: 12 months
measure: The specificity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.
timeFrame: 24 months
measure: The specificity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.
timeFrame: 36 months
measure: The Cohen's kappa of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.
timeFrame: 12 months
measure: The Cohen's kappa of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.
timeFrame: 24 months
measure: The Cohen's kappa of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.
timeFrame: 36 months
measure: The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.
timeFrame: 12 months
measure: The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.
timeFrame: 24 months
measure: The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.
timeFrame: 36 months
measure: The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.
timeFrame: 12 months
measure: The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.
timeFrame: 24 months
measure: The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.
timeFrame: 36 months
measure: The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.
timeFrame: 12 months
measure: The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.
timeFrame: 24 months
measure: The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.
timeFrame: 36 months
measure: The Cohen's kappa of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.
timeFrame: 12 months
measure: The Cohen's kappa of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.
timeFrame: 24 months
measure: The Cohen's kappa of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.
timeFrame: 36 months
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: