Utility of a Short Neuropsychological Protocol for Detecting HIV-Associated Neurocognitive Disorders in Patients with Asymptomatic HIV-1 Infection

Human Immunodeficiency Virus type 1 (HIV-1) infection is a chronic disease that affects ~40 million people worldwide. HIV-associated neurocognitive disorders (HAND) are common in individuals with HIV-1 Infection, and represent a recent public health problem. Here we evaluate the performance of a rec...

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Autores:
Martinez-Banf, Martha
Vélez, Jorge I.
Mebarak Chams, Moisés R.
Arcos-Holzinger, Mauricio
Acosta-López, Johan E.
García, Ricardo
Perea, María Victoria
Arcos-Burgos, Mauricio
Ladera, Valentina
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/8110
Acceso en línea:
https://hdl.handle.net/20.500.12442/8110
https://doi.org/10.3390/brainsci11081037
https://www.mdpi.com/2076-3425/11/8/1037
Palabra clave:
HIV
AIDS
HAND
Neurocognitive disorder
Predictive models
Neuropsychological screening
Machine learning
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Description
Summary:Human Immunodeficiency Virus type 1 (HIV-1) infection is a chronic disease that affects ~40 million people worldwide. HIV-associated neurocognitive disorders (HAND) are common in individuals with HIV-1 Infection, and represent a recent public health problem. Here we evaluate the performance of a recently proposed short protocol for detecting HAND by studying 60 individuals with HIV-1-Infection and 60 seronegative controls from a Caribbean community in Barranquilla, Colombia. The short evaluation protocol used significant neuropsychological tests from a previous study of asymptomatic HIV-1 infected patients and a group of seronegative controls. Brief screening instruments, i.e., the Mini-mental State Examination (MMSE) and the International HIV Dementia Scale (IHDS), were also applied. Using machine-learning techniques, we derived predictive models of HAND status, and evaluated their performance with the ROC curves. The proposed short protocol performs exceptionally well yielding sensitivity, specificity, and overall prediction values >90%, and better predictive capacity than that of the MMSE and IHDS. Community-specific cut-off values for HAND diagnosis, based on the MMSE and IHDS, make this protocol suitable for HAND screening in individuals from this Caribbean community. This study shows the effectivity of a recently proposed short protocol to detect HAND in individuals with asymptomatic HIV-1-Infection. The application of community-specific cut-off values for HAND diagnosis in the clinical setting may improve HAND screening accuracy and facilitate patients’ treatment and follow-up. Further studies are needed to assess the performance of this protocol in other Latin American populations.