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Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data
Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Bürger C, Münker S, Mühlmann L et al.. JAMA psychiatry, 2016-06-01
Topic: Risk, prevention and care in population studies
Both groups were compared with a sample of healthy control participants (n = 21). Results One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years). Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score. Furthermore, the analysis of treatment effects revealed a increase in hippocampal volume in the ECT sample (MNI coordinates x = -28, y = -9, z = -18; Z score, 7.81; P < .001) that was missing in the medication-only sample. Conclusions and relevance A relatively small degree of structural impairment in the subgenual cingulate cortex before therapy seems to be associated with successful treatment with ECT.
Key findings
- Both groups were compared with a sample of healthy control participants (n = 21).
- Results One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years).
- Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score.
- Furthermore, the analysis of treatment effects revealed a increase in hippocampal volume in the ECT sample (MNI coordinates x = -28, y = -9, z = -18; Z score, 7.81; P < .001) that was missing in the medication-only sample.
- Conclusions and relevance A relatively small degree of structural impairment in the subgenual cingulate cortex before therapy seems to be associated with successful treatment with ECT.
Study details
- Participants24
- Effect size78.3%
- Evidence strengthLow
How this study scored 50/100
Published in a leading specialty 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 and multi-centre collaboration, so those were left out: it earned 29 of the 58 points we could judge it on, which is 50 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (JAMA psychiatry) |
| 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 | 2016 — within 20 year(s) of the newest study here |
| Sample size | 1 / 4 | 24 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 27145449) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 29 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 29 of the 58 points we could judge it on is 50 out of 100. |
| Total | 50 / 100 | — |
What each of these 20 components measures, and what this score is not →
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APA 7th
Redlich, R., Opel, N., Grotegerd, D., Dohm, K., Zaremba, D., Bürger, C., et al. (2016). Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data. JAMA psychiatry. https://doi.org/10.1001/jamapsychiatry.2016.0316
Vancouver
Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Bürger C, et al. Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data. JAMA psychiatry. 2016. doi:10.1001/jamapsychiatry.2016.0316. PMID: 27145449.
BibTeX
@article{redlich2016-27145449,
author = {Redlich, R. and Opel, N. and Grotegerd, D. and Dohm, K. and Zaremba, D. and Bürger, C. and Münker, S. and Mühlmann, L. and others},
title = {{Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data}},
journal = {JAMA psychiatry},
year = {2016},
doi = {10.1001/jamapsychiatry.2016.0316},
pmid = {27145449},
url = {https://doi.org/10.1001/jamapsychiatry.2016.0316}
}
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