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Predicting rates of cognitive and functional decline in Alzheimer’s disease and mild cognitive impairment
Antigone Fogel, Chloe Walsh, Nan Fletcher‐Lloyd, Paresh Malhotra, Mina Ryten, Ramin Nilforooshan, Payam Barnaghi. Communications Medicine, 2026-02-26
Topic: Risk, prevention and care in population studies
The MMSE model achieves a mean absolute error (MAE) of 1.84 (95% CI: 1.64-2.04) internally and 2.19 in external validation. The BADL model achieves an MAE of 3.88 (95% CI: 3.46-4.30). Baseline scores on ideational praxis, orientation, and word recall are among the strongest predictors of cognitive decline, while independence in food preparation, finances, and dressing are among the top predictors of functional decline. Our models use only routinely collected and easily accessible data, offering high translational potential. If implemented, our scalable, data-driven prognostic support tool could streamline clinical workflows, support personalised care planning, and provide PLWD and their families with greater clarity and reassurance.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Fogel, A., Walsh, C., Fletcher‐Lloyd, N., Malhotra, P., Ryten, M., Nilforooshan, R., et al. (2026). Predicting rates of cognitive and functional decline in Alzheimer’s disease and mild cognitive impairment. Communications Medicine. https://doi.org/10.1038/s43856-026-01432-w
Vancouver
Fogel A, Walsh C, Fletcher‐Lloyd N, Malhotra P, Ryten M, Nilforooshan R, et al. Predicting rates of cognitive and functional decline in Alzheimer’s disease and mild cognitive impairment. Communications Medicine. 2026. doi:10.1038/s43856-026-01432-w. PMID: 41748878.
BibTeX
@article{fogel2026-41748878,
author = {Fogel, Antigone and Walsh, Chloe and Fletcher‐Lloyd, Nan and Malhotra, Paresh and Ryten, Mina and Nilforooshan, Ramin and Barnaghi, Payam},
title = {{Predicting rates of cognitive and functional decline in Alzheimer’s disease and mild cognitive impairment}},
journal = {Communications Medicine},
year = {2026},
doi = {10.1038/s43856-026-01432-w},
pmid = {41748878},
url = {https://doi.org/10.1038/s43856-026-01432-w}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- The MMSE model achieves a mean absolute error (MAE) of 1.84 (95% CI: 1.64-2.04) internally and 2.19 in external validation.
- The BADL model achieves an MAE of 3.88 (95% CI: 3.46-4.30).
- Baseline scores on ideational praxis, orientation, and word recall are among the strongest predictors of cognitive decline, while independence in food preparation, finances, and dressing are among the top predictors of functional decline.
- Our models use only routinely collected and easily accessible data, offering high translational potential.
- If implemented, our scalable, data-driven prognostic support tool could streamline clinical workflows, support personalised care planning, and provide PLWD and their families with greater clarity and reassurance.
Why it matters
Models using routinely collected data predicted rates of cognitive and functional decline in Alzheimer's disease and mild cognitive impairment, with the MMSE model achieving a mean absolute error of 1.84 internally and 2.19 in external validation. The authors say such a tool could support personalised care planning and give people and families greater clarity, but it does not treat or prevent the disease.
Study details
- Evidence strengthModerate
How this study scored 39/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Communications Medicine) |
| Journal's citation record | 0 / 4 | Communications Medicine: journal h-index 55 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| 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 | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 3 / 6 | Cited 4.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Payam Barnaghi: h-index 42 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Cambridge: institution h-index 1,973 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41748878) |
| Free to read | 2 / 2 | Free to read |
| Total | 39 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s43856-026-01432-w
- PubMed: 41748878
- Original source →
- Free full text: Communications Medicine →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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