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Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study
Maito MA, Santamaría-García H, Moguilner S, Possin KL, Godoy ME, Avila-Funes JA, Behrens MI, Brusco IL et al.. Lancet regional health. Americas, 2022-11-03
Topic: Amyloid, tau and blood-test diagnosis
Background Global brain health initiatives call for improving methods for the diagnosis of Alzheimer'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. Funding This work was supported by the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by NIA/NIH (R01AG057234), Alzheimer's Association (SG-20-725707-ReDLat), Rainwater Foundation, Takeda (CW2680521), Global Brain Health Institute; as well as CONICET; FONCYT-PICT (2017-1818, 2017-1820); PIIECC, Facultad de Humanidades, Usach; Sistema General de Regalías de Colombia (BPIN2018000100059), Universidad del Valle (CI 5316); ANID/FONDECYT Regular (1210195, 1210176, 1210176); ANID/FONDAP (15150012); ANID/PIA/ANILLOS ACT210096; and Alzheimer's Association GBHI ALZ UK-22-865742.
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APA 7th
Maito, M. A., Santamaría-García, H., Moguilner, S., Possin, K. L., Godoy, M. E., Avila-Funes, J. A., et al. (2023). Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study. Lancet regional health. Americas. https://doi.org/10.1016/j.lana.2022.100387
Vancouver
Maito MA, Santamaría-García H, Moguilner S, Possin KL, Godoy ME, Avila-Funes JA, et al. Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study. Lancet regional health. Americas. 2023. doi:10.1016/j.lana.2022.100387. PMID: 36583137.
BibTeX
@article{maito2023-36583137,
author = {Maito, M. A. and Santamaría-García, H. and Moguilner, S. and Possin, K. L. and Godoy, M. E. and Avila-Funes, J. A. and Behrens, M. I. and Brusco, I. L. and others},
title = {{Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study}},
journal = {Lancet regional health. Americas},
year = {2023},
doi = {10.1016/j.lana.2022.100387},
pmid = {36583137},
url = {https://doi.org/10.1016/j.lana.2022.100387}
}
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Key findings
- Results demonstrated an accurate classification of patients with AD and FTD and HCs.
- 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.
Why it matters
A machine learning model using demographic, clinical, and cognitive data classified Alzheimer's disease and frontotemporal dementia with an accuracy of 0.91 in underrepresented Latin American samples. This is a diagnostic approach and does not treat, cure, or prevent Alzheimer's disease.
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Study details
- Study designcross-sectional
- Participants1,794
- Evidence strengthModerate
How this study scored 62/100
Published in a peer-reviewed journal, and it is a snapshot of people at one point in time. Scored zero for effect reported and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Lancet regional health. Americas) |
| Journal's citation record | 0 / 4 | The Lancet Regional Health - Americas: journal h-index 61 (OpenAlex, 2026-10-29) |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2023 — within 6 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,794 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 73 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.6 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 4 / 6 | Cited 9.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 10 other studies in this library |
| Still being cited | 3 / 4 | Cited 55 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Agustín M. Ibáñez: h-index 81 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Inserm: institution h-index 1,988 (OpenAlex) |
| Multicenter collaboration | 3 / 3 | OpenAlex lists 27 institution(s) in 9 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36583137) |
| Free to read | 2 / 2 | Free to read |
| Total | 62 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.lana.2022.100387
- PubMed: 36583137
- Original source →
- Free full text: The Lancet Regional Health - Americas →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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