NCT04828122

Amyloid Prediction in Early Stage Alzheimer's Disease From Acoustic and Linguistic Patterns of Speech

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 141 (actual)
• Sponsor: Novoic Limited · Industry
• Sex: All
• Age: 50 Years – 85 Years
• Healthy volunteers: Accepted

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, as measured by the 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. Secondary objectives include (1) evaluating whether similar algorithms can detect amyloid-specific cognitive impairment in the cognitively normal (CN) and MCI Arms respectively, as measured on binary classifier performance; (2) whether they can detect MCI, as measured on binary classifier performance (AUC, sensitivity, specificity, Cohen's kappa), and the agreement between the PACC5 composite and the corresponding regression model predicting it in all Arms pooled (Wilcoxon signed-rank test, CIA); (3) evaluating variables that can impact performance of such algorithms of covariates from the speaker (age, gender, education level) and environment (measures of acoustic quality).

Conditions

• Alzheimer Disease
• Preclinical Alzheimer's Disease
• Prodromal Alzheimer's Disease
• Alzheimer's Disease (Incl Subtypes)
• Mild Cognitive Impairment

Timeline

• Start date: 2020-11-19
• Primary completion: 2021-08-06
• Completion: 2021-08-06
• First posted: 2021-04-01
• Last updated: 2021-09-05

Locations

4 sites across 1 country: United Kingdom