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Decision tree-based classification as a support to diagnosis in the Alzheimer's disease continuum using cerebrospinal fluid biomarkers: insights from automated analysis
Costa, Alana; Pais, Marcos; Loureiro, Júlia; Stella, Florindo; Radanovic, Márcia; Gattaz, Wagner; Forlenza, Orestes; Talib, Leda.
  • Costa, Alana; Universidade de São Paulo. Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina. Laboratório de Neurociências (LIM-27), Departamento de Psiquiatria. São Paulo. BR
  • Pais, Marcos; Universidade de São Paulo. Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina. Laboratório de Neurociências (LIM-27), Departamento de Psiquiatria. São Paulo. BR
  • Loureiro, Júlia; Universidade de São Paulo. Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina. Laboratório de Neurociências (LIM-27), Departamento de Psiquiatria. São Paulo. BR
  • Stella, Florindo; Universidade de São Paulo. Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina. Laboratório de Neurociências (LIM-27), Departamento de Psiquiatria. São Paulo. BR
  • Radanovic, Márcia; Universidade de São Paulo. Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina. Laboratório de Neurociências (LIM-27), Departamento de Psiquiatria. São Paulo. BR
  • Gattaz, Wagner; Universidade de São Paulo. Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina. Laboratório de Neurociências (LIM-27), Departamento de Psiquiatria. São Paulo. BR
  • Forlenza, Orestes; Universidade de São Paulo. Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina. Laboratório de Neurociências (LIM-27), Departamento de Psiquiatria. São Paulo. BR
  • Talib, Leda; Universidade de São Paulo. Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina. Laboratório de Neurociências (LIM-27), Departamento de Psiquiatria. São Paulo. BR
Braz. J. Psychiatry (São Paulo, 1999, Impr.) ; 44(4): 370-377, July-Aug. 2022. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1394066
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.


Full text: Available Index: LILACS (Americas) Type of study: Diagnostic study / Health economic evaluation / Prognostic study Language: English Journal: Braz. J. Psychiatry (São Paulo, 1999, Impr.) Journal subject: Psychiatry Year: 2022 Type: Article / Project document Affiliation country: Brazil Institution/Affiliation country: Universidade de São Paulo/BR

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Full text: Available Index: LILACS (Americas) Type of study: Diagnostic study / Health economic evaluation / Prognostic study Language: English Journal: Braz. J. Psychiatry (São Paulo, 1999, Impr.) Journal subject: Psychiatry Year: 2022 Type: Article / Project document Affiliation country: Brazil Institution/Affiliation country: Universidade de São Paulo/BR