학술논문

An Automated Virtual Reality Program Accurately Diagnoses HIV-Associated Neurocognitive Disorders in Older People With HIV
Document Type
article
Source
Open Forum Infectious Diseases. 10(12)
Subject
Medical Microbiology
Biomedical and Clinical Sciences
Clinical Sciences
Infectious Diseases
Neurosciences
HIV/AIDS
Brain Disorders
Acquired Cognitive Impairment
Aging
Basic Behavioral and Social Science
Sexually Transmitted Infections
Clinical Research
Mental Health
Neurodegenerative
Behavioral and Social Science
Mental health
cognition
cognitive aging
cognitive screening
digital health
HIV
virtual reality
Clinical sciences
Medical microbiology
Language
Abstract
BackgroundHIV-associated neurocognitive disorders (HANDs) remain prevalent despite antiretroviral therapy, particularly among older people with HIV (PWH). However, the diagnosis of HAND is labor intensive and requires expertise to administer neuropsychological tests. Our prior pilot work established the feasibility and accuracy of a computerized self-administered virtual reality program (DETECT; Display Enhanced Testing for Cognitive Impairment and Traumatic Brain Injury) to measure cognition in younger PWH. The present study expands this to a larger sample of older PWH.MethodsWe enrolled PWH who were ≥60 years old, were undergoing antiretroviral therapy, had undetectable plasma viral loads, and were without significant neuropsychological confounds. HAND status was determined via Frascati criteria. Regression models that controlled for demographic differences (age, sex, education, race/ethnicity) examined the association between DETECT's cognition module and both HAND status and Global Deficit Score (GDS) derived via traditional neuropsychological tests.ResultsSeventy-nine PWH (mean age, 66 years; 28% women) completed a comprehensive neuropsychological battery and DETECT's cognition module. Twenty-five (32%) had HAND based on the comprehensive battery. A significant correlation was found between the DETECT cognition module and the neuropsychological battery (r = 0.45, P < .001). Furthermore, in two separate regression models, HAND status (b = -0.79, P < .001) and GDS impairment status (b = -0.83, P < .001) significantly predicted DETECT performance. Areas under the curve for DETECT were 0.78 for differentiating participants by HAND status (HAND vs no HAND) and 0.85 for detecting GDS impairment.ConclusionsThe DETECT cognition module provides a novel means to identify cognitive impairment in older PWH. As DETECT is fully immersive and self-administered, this virtual reality tool holds promise as a scalable cognitive screening battery.