Clinical Application and Potential of Cognitive Amyloid Risk Measure: James Galvin, MD, MPH

WATCH TIME: 5 minutes

"CARM (Cognivue Amyloid Risk Measure) doesn’t replace clinical exams or confirmatory biomarkers—but it helps clinicians decide the next best step for their patient’s diagnostic journey."

With the emergence of novel therapeutics to treat patients with Alzheimer disease (AD), the need for clinical detection of individuals who have amyloid in their brain becomes of the utmost importance. Cognivue Clarity, the first FDA-cleared computerized cognitive test, can detect cognitive impairment, distinguish biomarker-confirmed clinical diagnoses, and identify people with preclinical AD. The Cognivue Amyloid Risk Measure (CARM) was developed using machine learning to provide a risk of amyloid presence and, when combined with Cognivue Clarity, global scores can characterize True Controls, preclinical AD, cognitive impairment due to AD, and cognitive impairment due to a non-AD process.

A recently published study comprised 887 individuals who completed Cognivue Clarity, amyloid PET scan, and blood-based AD biomarkers. In the analysis, Cognivue Clarity effectively distinguished cognitively normal from impaired individuals (P < 0.001, Cohen’s d = 0.732), while the CARM differentiated amyloid-positive from amyloid-negative individuals using both PET imaging (P < 0.001, Cohen’s d = 0.618) and blood-based biomarkers (P’s < 0.001). Notably, Dichotomizing CARM thresholds into low (CARM1/CARM2) and high (CARM3/CARM4) likelihood provided excellent discrimination for amyloid PET positivity (OR, 3.67; 95% CI 2.76–4.89).

Study author James E. Galvin, MD, MPH, sat down to discuss how the CARM was developed, the machine learning technology behind it, and the 3 key subtests it was derived from. Galvin, who serves as a professor of neurology at the Miami Miller School of Medicine, talks about the stratification of patients through CARM and how it serves as an adjunct to cognitive screening with Cognivue Clarity. While not a replacement for PET or cerebrospinal fluid-based biomarkers, Galvin noted that CARM may offer a scalable and efficient method for determining whether a patient’s impairment is likely due to AD, guiding next steps in diagnosis or treatment planning.

REFERENCE
1. Galvin JE, Kleiman MJ, Harris HM, Estes PW. The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity. Neurology & Therapy. 2025;14:865-880. doi:10.1007/s40120-025-00741-x