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MCH-Guard: Multimodal machine learning framework for risk stratification of cerebral microhemorrhage risk in the
Alper Gel1, Eliana Phillips1, Isabella Hausle1
1Northern California Institute for Research and Education, San Francisco, California, USA.
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 (R2 = 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.