Discrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis. (Registro nro. 16933)
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| Campo de control de longitud fija | nab a22 7a 4500 |
| 003 - IDENTIFICADOR DEL NÚMERO DE CONTROL | |
| Campo de control | PC16933 |
| 005 - FECHA Y HORA DE LA ÚLTIMA TRANSACCIÓN | |
| Campo de control | 20220707130206.0 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA | |
| Campo de control de longitud fija | 220707b xxu||||| |||| 00| 0 eng d |
| 040 ## - FUENTE DE LA CATALOGACIÓN | |
| Centro transcriptor | H12O |
| 041 ## - CÓDIGO DE LENGUA | |
| Código de lengua del texto/banda sonora o título independiente | eng |
| 100 ## - PUNTO DE ACCESO PRINCIPAL - NOMBRE DE PERSONA | |
| 9 (RLIN) | 2441 |
| Nombre de persona | Muñoz Madrigal, José Luis |
| Término indicativo de función | Instituto de Investigación i+12 |
| 100 ## - PUNTO DE ACCESO PRINCIPAL - NOMBRE DE PERSONA | |
| 9 (RLIN) | 1995 |
| Nombre de persona | Leza, Juan Carlos |
| Término indicativo de función | Instituto de Investigación i+12 |
| 245 00 - MENCIÓN DE TÍTULO | |
| Título | Discrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis. |
| Tipo de material | [artículo] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Nombre del editor distribuidor etc. | Frontiers in aging neuroscience, |
| Fecha de publicación distribución etc. | 2015 |
| 300 ## - DESCRIPCIÓN FÍSICA | |
| Extensión | 7:231. |
| 500 ## - NOTA GENERAL | |
| Nota general | Formato Vancouver: Besga A, González I, Echeburua E, Savio A, Ayerdi B, Chyzhyk D et al. Discrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis. Front Aging Neurosci. 2015 Dec 14;7:231. |
| 501 ## - NOTA DE “CON” | |
| Nota de "Con" | PMID: 26696883 PMC4677464 |
| 504 ## - NOTA DE BIBLIOGRAFÍA; ETC. | |
| Nota de bibliografía etc. | Contiene 72 referencias |
| 520 ## - NOTA DE SUMARIO; ETC. | |
| Sumario etc. | Background: Late onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment. Objective: The aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables. Materials: A sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time. Methods: We applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch's t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance. Results: Welch's t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%. Conclusion: It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance. |
| 710 ## - PUNTO DE ACCESO ADICIONAL - NOMBRE DE ENTIDAD | |
| 9 (RLIN) | 625 |
| Nombre de entidad o nombre de jurisdicción como elemento inicial | Instituto de Investigación imas12 |
| 856 ## - LOCALIZACIÓN Y ACCESO ELECTRÓNICOS | |
| Identificador Uniforme del Recurso (URI) | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4677464/ |
| Acceso | Acceso libre |
| 942 ## - ENTRADA PARA ELEMENTOS AGREGADOS (KOHA) | |
| Fuente de clasificación o esquema de ordenación en estanterías | |
| Koha [por defecto] tipo de item | Artículo |
| Suprimido en OPAC | Público |
| Suprimido | Estado de pérdida | Fuente de clasificación o esquema de ordenación en estanterías | Estropeado | No para préstamo | Localización permanente | Localización actual | Fecha de adquisición | Signatura completa | Fecha última consulta | Fecha del precio de reemplazo | Tipo de item de Koha |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Hospital Universitario 12 de Octubre | Hospital Universitario 12 de Octubre | 2022-07-07 | PC16933 | 2022-07-07 | 2022-07-07 | Artículo |
