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: | , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Public Library of Science (PLoS)
2014
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/c78acb2518ed45a49206e3cd2f257d08 |
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| Sumario: | <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. |
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