Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables.

<h4>Objective</h4>We used demographic and clinical data to design practical classification models for prediction of neurocognitive impairment (NCI) in people with HIV infection.<h4>Methods</h4>The study population comprised 331 HIV-infected patients with available demographic...

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Autores principales:	Jose A Muñoz-Moreno, Núria Pérez-Álvarez, Amalia Muñoz-Murillo, Anna Prats, Maite Garolera, M Àngels Jurado, Carmina R Fumaz, Eugènia Negredo, Maria J Ferrer, Bonaventura Clotet
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c78acb2518ed45a49206e3cd2f257d08
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spelling	oai:doaj.org-article:c78acb2518ed45a49206e3cd2f257d082021-11-25T06:00:04ZClassification models for neurocognitive impairment in HIV infection based on demographic and clinical variables.1932-620310.1371/journal.pone.0107625https://doaj.org/article/c78acb2518ed45a49206e3cd2f257d082014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0107625https://doaj.org/toc/1932-6203<h4>Objective</h4>We used demographic and clinical data to design practical classification models for prediction of neurocognitive impairment (NCI) in people with HIV infection.<h4>Methods</h4>The study population comprised 331 HIV-infected patients with available demographic, clinical, and neurocognitive data collected using a comprehensive battery of neuropsychological tests. Classification and regression trees (CART) were developed to obtain detailed and reliable models to predict NCI. Following a practical clinical approach, NCI was considered the main variable for study outcomes, and analyses were performed separately in treatment-naïve and treatment-experienced patients.<h4>Results</h4>The study sample comprised 52 treatment-naïve and 279 experienced patients. In the first group, the variables identified as better predictors of NCI were CD4 cell count and age (correct classification [CC]: 79.6%, 3 final nodes). In treatment-experienced patients, the variables most closely related to NCI were years of education, nadir CD4 cell count, central nervous system penetration-effectiveness score, age, employment status, and confounding comorbidities (CC: 82.1%, 7 final nodes). In patients with an undetectable viral load and no comorbidities, we obtained a fairly accurate model in which the main variables were nadir CD4 cell count, current CD4 cell count, time on current treatment, and past highest viral load (CC: 88%, 6 final nodes).<h4>Conclusion</h4>Practical classification models to predict NCI in HIV infection can be obtained using demographic and clinical variables. An approach based on CART analyses may facilitate screening for HIV-associated neurocognitive disorders and complement clinical information about risk and protective factors for NCI in HIV-infected patients.Jose A Muñoz-MorenoNúria Pérez-ÁlvarezAmalia Muñoz-MurilloAnna PratsMaite GaroleraM Àngels JuradoCarmina R FumazEugènia NegredoMaria J FerrerBonaventura ClotetPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e107625 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jose A Muñoz-Moreno Núria Pérez-Álvarez Amalia Muñoz-Murillo Anna Prats Maite Garolera M Àngels Jurado Carmina R Fumaz Eugènia Negredo Maria J Ferrer Bonaventura Clotet Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables.
description	<h4>Objective</h4>We used demographic and clinical data to design practical classification models for prediction of neurocognitive impairment (NCI) in people with HIV infection.<h4>Methods</h4>The study population comprised 331 HIV-infected patients with available demographic, clinical, and neurocognitive data collected using a comprehensive battery of neuropsychological tests. Classification and regression trees (CART) were developed to obtain detailed and reliable models to predict NCI. Following a practical clinical approach, NCI was considered the main variable for study outcomes, and analyses were performed separately in treatment-naïve and treatment-experienced patients.<h4>Results</h4>The study sample comprised 52 treatment-naïve and 279 experienced patients. In the first group, the variables identified as better predictors of NCI were CD4 cell count and age (correct classification [CC]: 79.6%, 3 final nodes). In treatment-experienced patients, the variables most closely related to NCI were years of education, nadir CD4 cell count, central nervous system penetration-effectiveness score, age, employment status, and confounding comorbidities (CC: 82.1%, 7 final nodes). In patients with an undetectable viral load and no comorbidities, we obtained a fairly accurate model in which the main variables were nadir CD4 cell count, current CD4 cell count, time on current treatment, and past highest viral load (CC: 88%, 6 final nodes).<h4>Conclusion</h4>Practical classification models to predict NCI in HIV infection can be obtained using demographic and clinical variables. An approach based on CART analyses may facilitate screening for HIV-associated neurocognitive disorders and complement clinical information about risk and protective factors for NCI in HIV-infected patients.
format	article
author	Jose A Muñoz-Moreno Núria Pérez-Álvarez Amalia Muñoz-Murillo Anna Prats Maite Garolera M Àngels Jurado Carmina R Fumaz Eugènia Negredo Maria J Ferrer Bonaventura Clotet
author_facet	Jose A Muñoz-Moreno Núria Pérez-Álvarez Amalia Muñoz-Murillo Anna Prats Maite Garolera M Àngels Jurado Carmina R Fumaz Eugènia Negredo Maria J Ferrer Bonaventura Clotet
author_sort	Jose A Muñoz-Moreno
title	Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables.
title_short	Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables.
title_full	Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables.
title_fullStr	Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables.
title_full_unstemmed	Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables.
title_sort	classification models for neurocognitive impairment in hiv infection based on demographic and clinical variables.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/c78acb2518ed45a49206e3cd2f257d08
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Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables.

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