Amyloid Prediction in Early Stage Alzheimer's Disease Through Speech Phenotyping - FUTURE Extension
Terminated
- Conditions
- Alzheimer DiseaseMild Cognitive ImpairmentPreclinical Alzheimer's DiseaseAlzheimer's Disease (Incl Subtypes)Prodromal Alzheimer's Disease
- Registration Number
- NCT04951284
- Lead Sponsor
- 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 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- TERMINATED
- Sex
- All
- Target Recruitment
- 42
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.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method 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). 12 months
- Secondary Outcome Measures
Name Time Method 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. 24 months 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. 24 months 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). 24 months 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). 36 months 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. 24 months 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. 12 months 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. 36 months 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). 36 months 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). 36 months 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. 24 months 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). 12 months 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). 24 months 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). 24 months 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. 12 months 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). 12 months 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. 12 months 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. 36 months 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. 12 months 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. 24 months 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. 36 months 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. 12 months The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months. 24 months The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months. 24 months The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months. 12 months The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months. 24 months 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. 36 months 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. 24 months 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. 36 months The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months. 12 months The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months. 12 months The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months. 36 months The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months. 36 months The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months. 36 months 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. 12 months 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. 36 months
Trial Locations
- Locations (1)
Syrentis Clinical Research
🇺🇸Santa Ana, California, United States