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MCH-Guard: Multimodal machine learning framework for risk stratification of cerebral microhemorrhage risk in the Alzheimer's Disease Neuroimaging Initiative
Gel A, Phillips E, Hausle I, Thropp P, Tosun D, Alzheimer's Disease Neuroimaging Initiative.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2026-09-01
Topic: Risk, prevention and care in population studies
Introduction Efficient cerebral microhemorrhage (MCH) monitoring is critical for anti-amyloid therapy safety due to amyloid-related imaging abnormalities with hemosiderin deposition (ARIA-H) risk. We developed MCH-Guard, a multimodal machine-learning framework, to stratify MCH risk for Alzheimer's Disease Neuroimaging Initiaitive (ADNI) participants (N = 813). Methods Nested models integrated clinical history, fluid biomarkers, and imaging to predict MCH presence, incidence, and stability. Results The comprehensive model detected baseline MCH with high accuracy (area under the curve [AUC] = 0.86). Notably, the minimal model (M1), utilizing only demographics and clinical history, achieved robust performance (AUC = 0.72). Longitudinal models predicted time-to-incidence (R 2 = 0.67) and stratified four-year risk. Furthermore, we identified a transient vascular instability phenotype-where MCH status fluctuates-which was strongly predicted by hepatic factors. Discussion MCH-Guard offers a flexible clinical decision-support tool for optimizing spontaneous MCH and ARIA-H surveillance. The strong performance of the clinical-only model supports equitable risk assessment in resource-limited settings, while the characterization of vascular instability addresses a critical confounder in safety monitoring.
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APA 7th
Gel, A., Phillips, E., Hausle, I., Thropp, P., Tosun, D., & Alzheimer's Disease Neuroimaging Initiative. (2026). MCH-Guard: Multimodal machine learning framework for risk stratification of cerebral microhemorrhage risk in the Alzheimer's Disease Neuroimaging Initiative. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.71818
Vancouver
Gel A, Phillips E, Hausle I, Thropp P, Tosun D, Alzheimer's Disease Neuroimaging Initiative. MCH-Guard: Multimodal machine learning framework for risk stratification of cerebral microhemorrhage risk in the Alzheimer's Disease Neuroimaging Initiative. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2026. doi:10.1002/alz.71818. PMID: 42712106.
BibTeX
@article{gel2026-42712106,
author = {Gel, A. and Phillips, E. and Hausle, I. and Thropp, P. and Tosun, D. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{MCH-Guard: Multimodal machine learning framework for risk stratification of cerebral microhemorrhage risk in the Alzheimer's Disease Neuroimaging Initiative}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
doi = {10.1002/alz.71818},
pmid = {42712106},
url = {https://doi.org/10.1002/alz.71818}
}
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Key findings
- Results The comprehensive model detected baseline MCH with high accuracy (area under the curve [AUC] = 0.86).
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Study details
- Study designlongitudinal study
- Participants813
- Effect sizeAUC = 0.72
- Evidence strengthModerate
How this study scored 79/100
Published in a leading specialty journal, and it is a longitudinal study following the same people over time. Scored zero for 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 46 of the 58 points we could judge it on, which is 79 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Alzheimer's & dementia : the journal of the Alzheimer's Association) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| 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 | 2 / 4 | 813 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 42712106) |
| Free to read | 2 / 2 | Free to read |
| Points | 46 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 46 of the 58 points we could judge it on is 79 out of 100. |
| Total | 79 / 100 | — |
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