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Modeling Alzheimer's Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR-SOB Scores From ADNI
Delor I, Charoin JE, Gieschke R, Retout S, Jacqmin P.. CPT: pharmacometrics & systems pharmacology, 2013-10-02
Topic: Amyloid, tau and blood-test diagnosis
Disease-onset time (DOT) and disease trajectory concepts were applied to derive an Alzheimer's disease (AD) progression population model using the clinical dementia rating scale-sum of boxes (CDR-SOB) from the AD neuroimaging initiative (ADNI) database. The model enabled the estimation of a DOT and a disease trajectory for each patient. The model also allowed distinguishing fast and slow-progressing subpopulations according to the functional assessment questionnaire, normalized hippocampal volume, and CDR-SOB score at study entry.
Key findings
- The model enabled the estimation of a DOT and a disease trajectory for each patient.
- The model also allowed distinguishing fast and slow-progressing subpopulations according to the functional assessment questionnaire, normalized hippocampal volume, and CDR-SOB score at study entry.
- On the basis of these prognostic factors, 81% of the mild cognitive impairment (MCI) subjects could correctly be assigned to slow or fast progressers, and 77% of MCI to AD conversions could be predicted whereas the model described correctly 84% of the conversions.
- Finally, synchronization of the biomarker-time profiles on estimated individual DOT virtually expanded the population observation period from 3 to 8 years.
Why it matters
This study applied disease-onset time and disease trajectory concepts to CDR-SOB scores to model Alzheimer's disease progression. The model could distinguish fast and slow-progressing subgroups and predicted many conversions from mild cognitive impairment to Alzheimer's disease.
Study details
- Evidence strengthLow
How this study scored 36/100
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. We have no data on journal's citation record and senior author's track record, so those were left out: it earned 33 of the 92 points we could judge it on, which is 36 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (CPT: pharmacometrics & systems pharmacology) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2013 — within 20 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 | 4 / 7 | Cited 58 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.9 citations a year over 15 year(s) since 2013 |
| Impact for its field and year | 2 / 6 | Cited 1.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 1 / 4 | Cited 11 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 0 / 3 | Roche (Switzerland): institution h-index 638 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 24088949) |
| Free to read | 2 / 2 | Free to read |
| Points | 33 / 92 | Scored on 18 of 20 parts. We have no data for the rest, so they are left out: 33 of the 92 points we could judge it on is 36 out of 100. |
| Total | 36 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/psp.2013.54
- PubMed: 24088949
- Original source →
- Free full text: CPT Pharmacometrics & Systems Pharmacology →
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. Check the citation against the original before you use it.
APA 7th
Delor, I., Charoin, J. E., Gieschke, R., Retout, S., & Jacqmin, P. (2013). Modeling Alzheimer's Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR-SOB Scores From ADNI. CPT: pharmacometrics & systems pharmacology. https://doi.org/10.1038/psp.2013.54
Vancouver
Delor I, Charoin JE, Gieschke R, Retout S, Jacqmin P. Modeling Alzheimer's Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR-SOB Scores From ADNI. CPT: pharmacometrics & systems pharmacology. 2013. doi:10.1038/psp.2013.54. PMID: 24088949.
BibTeX
@article{delor2013-24088949,
author = {Delor, I. and Charoin, J. E. and Gieschke, R. and Retout, S. and Jacqmin, P.},
title = {{Modeling Alzheimer's Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR-SOB Scores From ADNI}},
journal = {CPT: pharmacometrics \& systems pharmacology},
year = {2013},
doi = {10.1038/psp.2013.54},
pmid = {24088949},
url = {https://doi.org/10.1038/psp.2013.54}
}
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