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http://hdl.handle.net/2445/113703
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DC Field | Value | Language |
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dc.contributor.author | Muñoz Moreno, José A. | - |
dc.contributor.author | Pérez Álvarez, Núria | - |
dc.contributor.author | Muñoz Murillo, Amalia | - |
dc.contributor.author | Prats, Anna | - |
dc.contributor.author | Garolera i Freixa, Maite | - |
dc.contributor.author | Jurado, Ma. Ángeles (María Ángeles) | - |
dc.contributor.author | Fumaz, Carmina | - |
dc.contributor.author | Negredo, Eugènia | - |
dc.contributor.author | Ferrer, María Jesus | - |
dc.contributor.author | Clotet, Bonaventura, 1953- | - |
dc.date.accessioned | 2017-07-12T09:40:07Z | - |
dc.date.available | 2017-07-12T09:40:07Z | - |
dc.date.issued | 2014-09 | - |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | http://hdl.handle.net/2445/113703 | - |
dc.description.abstract | Objective: We used demographic and clinical data to design practical classification models for prediction of neurocognitive impairment (NCI) in people with HIV infection. Methods: 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-naı¨ve and treatment-experienced patients. Results: The study sample comprised 52 treatment-naı¨ve 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). Conclusion: 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. | - |
dc.format.extent | 7 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Public Library of Science (PLoS) | - |
dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.1371/journal.pone.0107625 | - |
dc.relation.ispartof | PLoS One, 2014, vol. 9, num. 9, p. e107625 | - |
dc.relation.uri | https://doi.org/10.1371/journal.pone.0107625 | - |
dc.rights | cc-by (c) Muñoz Moreno, José et al., 2014 | - |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es | - |
dc.source | Articles publicats en revistes (Psicologia Clínica i Psicobiologia) | - |
dc.subject.classification | Infeccions per VIH | - |
dc.subject.classification | Antiretrovirals | - |
dc.subject.classification | Tests neuropsicològics | - |
dc.subject.other | HIV infections | - |
dc.subject.other | Antiretroviral agents | - |
dc.subject.other | Neuropsychological tests | - |
dc.title | Classification Models for Neurocognitive Impairment in HIV Infection Based on Demographic and Clinical Variables | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.idgrec | 642958 | - |
dc.date.updated | 2017-07-12T09:40:07Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
dc.identifier.pmid | 25237895 | - |
Appears in Collections: | Articles publicats en revistes (Psicologia Clínica i Psicobiologia) |
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