Early Prediction of Parkinson's Disease Progression by Integrating Research Cohort and Real-World Data Using Knowledge-Anchored Graph Learning.

Parkinson's disease (PD) progression is highly heterogeneous. Deeply phenotyped longitudinal research cohorts have enabled characterization of PD progression trajectories. Early prediction of these progression patterns can help us better understand patient disease conditions and manage appropriately. However, the sample sizes of these cohorts are typically too small to build robust early predictors, and usually it is challenging to translate them to real-world patients because of the differences in the population as well as the information availability. In this paper we present MedStitcher, a graph-based machine learning framework that stitches individuals' multimodal data across research cohorts and real-world data (RWD) using a biomedical knowledge graph-anchored architecture. This design enables predictive modeling under modality missingness and cross-dataset population heterogeneity. On the research cohort data combining PPMI and PDBP, MedStitcher achieved an AUROC of 0.819 ± 0.040 on predicting rapid PD progressors, outperforming existing machine learning approaches. Graph-based model interpretation revealed clinical and molecular drivers involving cognitive vulnerability, α-synuclein biology, vesicle trafficking and neuroinflammation. Importantly, MedStitcher-predicted rapid progressors in RWD cohort demonstrated elevated risks of dementia, falls, mild cognitive impairment, and gait impairment, which also enabled identification of early indicators of rapid PD progression in real world patient populations.