Decision tree-based classification as a support to diagnosis in the Alzheimer's disease continuum using cerebrospinal fluid biomarkers: insights from automated analysis
Braz. J. Psychiatry (São Paulo, 1999, Impr.)
; 44(4): 370-377, July-Aug. 2022. tab, graf
Artículo
en Inglés
|
LILACS-Express
| LILACS
| ID: biblio-1394066
Biblioteca responsable:
BR1.1
ABSTRACT
Objective: Cerebrospinal fluid (CSF) biomarkers add accuracy to the diagnostic workup of cognitive impairment by illustrating Alzheimer's disease (AD) pathology. However, there are no universally accepted cutoff values for the interpretation of AD biomarkers. The aim of this study is to determine the viability of a decision-tree method to analyse CSF biomarkers of AD as a support for clinical diagnosis. Methods: A decision-tree method (automated classification analysis) was applied to concentrations of AD biomarkers in CSF as a support for clinical diagnosis in older adults with or without cognitive impairment in a Brazilian cohort. In brief, 272 older adults (68 with AD, 122 with mild cognitive impairment [MCI], and 82 healthy controls) were assessed for CSF concentrations of Aβ1-42, total-tau, and phosphorylated-tau using multiplexed Luminex assays; biomarker values were used to generate decision-tree algorithms (classification and regression tree) in the R statistical software environment. Results: The best decision tree model had an accuracy of 74.65% to differentiate the three groups. Cluster analysis supported the combination of CSF biomarkers to differentiate AD and MCI vs. controls, suggesting the best cutoff values for each clinical condition. Conclusion: Automated analyses of AD biomarkers provide valuable information to support the clinical diagnosis of MCI and AD in research settings.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Contexto en salud:
Agenda de Salud Sostenible para las Américas
Problema de salud:
Objetivo 4: Financiamiento de la salud
Base de datos:
LILACS
Tipo de estudio:
Estudio diagnóstico
/
Evaluación económica en salud
/
Estudio pronóstico
Idioma:
Inglés
Revista:
Braz. J. Psychiatry (São Paulo, 1999, Impr.)
Asunto de la revista:
Psiquiatria
Año:
2022
Tipo del documento:
Artículo
/
Documento de proyecto
País de afiliación:
Brasil
Institución/País de afiliación:
Universidade de São Paulo/BR