Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study

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art_MAITO_Classification_of_Alzheimers_disease_and_frontotemporal_dementia_using_2023.PDF (1012.18 KB)
Citações na Scopus
24
Tipo de produção
article
Data de publicação
2023
DOI
Scopus
Web of Science
PubMed
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Título do Volume
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ELSEVIER
Autores
MAITO, Marcelo Adrian
SANTAMARIA-GARCIA, Hernando
MOGUILNER, Sebastian
POSSIN, Katherine L.
GODOY, Maria E.
AVILA-FUNES, Jose Alberto
I, Maria Behrens
BRUSCO, Ignacio L.
BRUNO, Martin A.
CARDONA, Juan F.
Citação
LANCET REGIONAL HEALTH-AMERICAS, v.17, article ID 100387, 14p, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Background Global brain health initiatives call for improving methods for the diagnosis of Alzheimer & rsquo;s disease (AD) and frontotemporal dementia (FTD) in underrepresented populations. However, diagnostic procedures in upper -middle-income countries (UMICs) and lower-middle income countries (LMICs), such as Latin American countries (LAC), face multiple challenges. These include the heterogeneity in diagnostic methods, lack of clinical harmonisation, and limited access to biomarkers. Methods This cross-sectional observational study aimed to identify the best combination of predictors to discriminate between AD and FTD using demographic, clinical and cognitive data among 1794 participants [904 diagnosed with AD, 282 diagnosed with FTD, and 606 healthy controls (HCs)] collected in 11 clinical centres across five LAC (ReDLat cohort). Findings A fully automated computational approach included classical statistical methods, support vector machine procedures, and machine learning techniques (random forest and sequential feature selection procedures). Results demonstrated an accurate classification of patients with AD and FTD and HCs. A machine learning model produced the best values to differentiate AD from FTD patients with an accuracy = 0.91. The top features included social cognition, neuropsychiatric symptoms, executive functioning performance, and cognitive screening; with secondary contributions from age, educational attainment, and sex. Interpretation Results demonstrate that data-driven techniques applied in archival clinical datasets could enhance diagnostic procedures in regions with limited resources. These results also suggest specific fine-grained cognitive and behavioural measures may aid in the diagnosis of AD and FTD in LAC. Moreover, our results highlight an opportunity for harmonisation of clinical tools for dementia diagnosis in the region.
Palavras-chave
Alzheimer?s Disease, Frontotemporal dementia, Underrepresented samples, Machine learning
URI
https://observatorio.fm.usp.br/handle/OPI/53170
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Coleções
Artigos e Materiais de Revistas Científicas - HC/ICHC
Artigos e Materiais de Revistas Científicas - LIM/45