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| 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 | 2014 |
| dc.identifier | 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) |
| dc.identifier | 19326203 |
| dc.identifier | http://hdl.handle.net/10854/3341 |
| dc.identifier | https://doi.org/10.1371/journal.pone.0107625 |
| dc.identifier.uri | http://hdl.handle.net/10854/3341 |
| dc.description | 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. |
| dc.format | application/pdf |
| dc.language | eng |
| dc.publisher | Plos One |
| dc.relation | http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0107625 |
| dc.rights | Aquest document està subjecte a aquesta llicència Creative Commons |
| dc.rights | http://creativecommons.org/licenses/by/3.0/es/ |
| dc.rights | info:eu-repo/semantics/openAccess |
| dc.subject | Sida -- Tractament |
| 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/publishedVersion |
Documentos con el texto completo de este documento
| Ficheros | Tamaño | Formato | Vista |
|---|---|---|---|
| artconlli_a2014 ... _models_neurocognitive.pdf | 504.6 KB | application/pdf | Vista/Abrir |