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Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
Qiang Liu, Nemanja Vaci, Ivan Koychev, Andrey Kormilitzin, Zhenpeng Li, Andrea Cipriani, Alejo Nevado-Holgado. BMC medicine, 2022-02-01
Topic: Drug trials: efficacy and safety
Overall, 1343 patients with MMSE scores were identified for internal validation and 285 [21.22%] took the drug recommended. After 2 years, the reduction of mean [standard deviation] MMSE score in this group was significantly smaller than the remaining 1058 [78.78%] patients (0.60 [0.26] vs 2.80 [0.28]; P = 0.02). In the external validation cohort (N = 1772), 222 [12.53%] patients took the drug recommended and reported a smaller MMSE reduction compared to the 1550 [87.47%] patients who did not (1.01 [0.49] vs 4.23 [0.60]; P = 0.01). A similar performance gap was seen when testing the model on patients prescribed with AChEIs only. It was possible to identify the most effective drug for the real-world treatment of cognitive impairment in dementia at an individual patient level. Routine care patients whose prescribed medications were the best fit according to the model had better cognitive performance after 2 years.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Liu, Q., Vaci, N., Koychev, I., Kormilitzin, A., Li, Z., Cipriani, A., et al. (2022). Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model. BMC medicine. https://doi.org/10.1186/s12916-022-02250-2
Vancouver
Liu Q, Vaci N, Koychev I, Kormilitzin A, Li Z, Cipriani A, et al. Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model. BMC medicine. 2022. doi:10.1186/s12916-022-02250-2. PMID: 35101059.
BibTeX
@article{liu2022-35101059,
author = {Liu, Qiang and Vaci, Nemanja and Koychev, Ivan and Kormilitzin, Andrey and Li, Zhenpeng and Cipriani, Andrea and Nevado-Holgado, Alejo},
title = {{Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model}},
journal = {BMC medicine},
year = {2022},
doi = {10.1186/s12916-022-02250-2},
pmid = {35101059},
url = {https://doi.org/10.1186/s12916-022-02250-2}
}
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
- Overall, 1343 patients with MMSE scores were identified for internal validation and 285 [21.22%] took the drug recommended.
- After 2 years, the reduction of mean [standard deviation] MMSE score in this group was significantly smaller than the remaining 1058 [78.78%] patients (0.60 [0.26] vs 2.80 [0.28]; P = 0.02).
- In the external validation cohort (N = 1772), 222 [12.53%] patients took the drug recommended and reported a smaller MMSE reduction compared to the 1550 [87.47%] patients who did not (1.01 [0.49] vs 4.23 [0.60]; P = 0.01).
- A similar performance gap was seen when testing the model on patients prescribed with AChEIs only.
- It was possible to identify the most effective drug for the real-world treatment of cognitive impairment in dementia at an individual patient level.
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Study details
- Participants1,772
- Effect size21.22%; 78.78%; 12.53%
- Evidence strengthModerate
How this study scored 59/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 33 of the 56 points we could judge it on, which is 59 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (BMC medicine) |
| 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 | 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 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,772 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35101059) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 33 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 33 of the 56 points we could judge it on is 59 out of 100. |
| Total | 59 / 100 | — |
What each of these 20 components measures, and what this score is not →
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