DSpace Repository

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

Show simple item record

dc.contributor Universitat de Vic. Càtedra de la Sida i Malalties Relacionades
dc.contributor.author Muñoz-Moreno, José A.
dc.contributor.author Pérez Alvarez, Núria
dc.contributor.author Muñoz-Murillo, A.
dc.contributor.author Prats, A.
dc.contributor.author Garolera, M.
dc.contributor.author Jurado, M.A.
dc.contributor.author Fumaz, C.R.
dc.contributor.author Negredo, Eugenia
dc.contributor.author Ferrer, M.J.
dc.contributor.author Clotet, Bonaventura
dc.date.accessioned 2014-10-06T07:39:02Z
dc.date.available 2014-10-06T07:39:02Z
dc.date.issued 2014
dc.identifier.citation Muñoz-Moreno, J. A., Pérez-Álvarez, N., Muñoz-Murillo, A., Prats, A., Garolera, M., Jurado, M. A., et al. (2014). Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables. Plos One, 9, september(9) ca_ES
dc.identifier.issn 19326203
dc.identifier.uri http://hdl.handle.net/10854/3341
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 btain 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. Results: 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). 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. ca_ES
dc.format application/pdf
dc.format.extent 7 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher Plos One ca_ES
dc.rights Aquest document està subjecte a aquesta llicència Creative Commons ca_ES
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/ ca_ES
dc.subject.other Sida -- Tractament ca_ES
dc.title Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables ca_ES
dc.type info:eu-repo/semantics/article ca_ES
dc.identifier.doi https://doi.org/10.1371/journal.pone.0107625
dc.relation.publisherversion http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0107625
dc.rights.accesRights info:eu-repo/semantics/openAccess ca_ES
dc.type.version info:eu-repo/publishedVersion ca_ES
dc.indexacio Indexat a SCOPUS
dc.indexacio Indexat a WOS/JCR ca_ES

Files in this item

Show simple item record

Aquest document està subjecte a aquesta llicència Creative Commons Except where otherwise noted, this item's license is described as Aquest document està subjecte a aquesta llicència Creative Commons

Search RIUVic


Browse

Statistics