Home › Research › Long-term cohort studies
Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review and Meta-Analysis
Chen Y, Qian X, Zhang Y, Su W, Huang Y, Wang X, Chen X, Zhao E et al.. Frontiers in aging neuroscience, 2022-04-07
Topic: Amyloid, tau and blood-test diagnosis
Background and purpose Alzheimer's disease (AD) is a devastating neurodegenerative disorder with no cure, and available treatments are only able to postpone the progression of the disease. Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD. Therefore, prediction models for conversion from MCI to AD are desperately required.
Key findings
- Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD.
- Therefore, prediction models for conversion from MCI to AD are desperately required.
- These will allow early treatment of patients with MCI before they develop AD.
- This study performed a systematic review and meta-analysis to summarize the reported risk prediction models and identify the most prevalent factors for conversion from MCI to AD.
Why it matters
This systematic review and meta-analysis summarized existing prediction models for conversion from mild cognitive impairment to Alzheimer's disease and identified the most common factors used in those models. The authors state that such models are needed to allow early treatment of patients with MCI before they develop Alzheimer's disease.
Study details
- Study designsystematic review
- Evidence strengthHigh
How this study scored 63/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 60 of the 96 points we could judge it on, which is 63 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in aging neuroscience) |
| Journal's citation record | 2 / 4 | Frontiers in Aging Neuroscience: journal h-index 174 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A systematic review of all the studies on the question |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 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 | A review of other studies — randomisation is not credited here |
| Times cited | 5 / 7 | Cited 105 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 17.5 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 3 / 6 | Cited 4.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 5 other studies in this library |
| Still being cited | 3 / 4 | Cited 89 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Yuxia Ma holds only 8 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | Lanzhou University: institution h-index 524 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35493941) |
| Free to read | 2 / 2 | Free to read |
| Points | 60 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 60 of the 96 points we could judge it on is 63 out of 100. |
| Total | 63 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fnagi.2022.840386
- PubMed: 35493941
- Original source →
- Free full text: Frontiers in Aging Neuroscience →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Chen, Y., Qian, X., Zhang, Y., Su, W., Huang, Y., Wang, X., et al. (2022). Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review and Meta-Analysis. Frontiers in aging neuroscience. https://doi.org/10.3389/fnagi.2022.840386
Vancouver
Chen Y, Qian X, Zhang Y, Su W, Huang Y, Wang X, et al. Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review and Meta-Analysis. Frontiers in aging neuroscience. 2022. doi:10.3389/fnagi.2022.840386. PMID: 35493941.
BibTeX
@article{chen2022-35493941,
author = {Chen, Y. and Qian, X. and Zhang, Y. and Su, W. and Huang, Y. and Wang, X. and Chen, X. and Zhao, E. and others},
title = {{Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review and Meta-Analysis}},
journal = {Frontiers in aging neuroscience},
year = {2022},
doi = {10.3389/fnagi.2022.840386},
pmid = {35493941},
url = {https://doi.org/10.3389/fnagi.2022.840386}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 21.
Later studies that cite this one
5 studies in the alzhub library list this one in their references.