Knowledge Graph-Guided Identification of Multiple Sclerosis and Therapeutic Trend Analysis: Real-World Evidence from Two Large Healthcare Systems
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Background
The multiple sclerosis (MS) therapeutic landscape has evolved over time.
Objective
We conducted a knowledge graph-guided analysis of MS-specific disease-modifying therapy (DMT) prescription trends using longitudinal real-world clinical data.
Methods
We utilized registry-linked electronic health records (EHR) data from two large independent healthcare systems encompassing both academic and community practices (2004-2022). We applied a novel and efficient Knowledge-driven Online Multimodal Automated Phenotyping (KOMAP) algorithm to identify patients diagnosed with MS and evaluated algorithm performance against chart-reviewed and registry-recorded diagnosis labels. To assess temporal trends in DMT prescriptions, we combined the two cohorts and constructed time-varying temporal knowledge graphs using the patient-level EHR data segmented by calendar year. For each year, we analyzed co-occurrence patterns between DMTs and MS diagnosis by using Shifted Positive Pointwise Mutual Information transformation and singular value decomposition to generate embeddings. We computed patient-wise cosine similarities and confidence intervals.
Results
The phenotyping algorithm achieved robust performance in predicting MS diagnosis (AUROC: MGB=0.994, UPMC=0.922), identifying 29,169 MS patients in the combined dataset. Among commonly used standard-effectiveness DMTs, prescriptions for interferon-beta (slope=-0.018±0.011, p<0.001) and glatiramer acetate (slope=-0.013±0.012, p=0.026) and fumarates (slope=-0.031±0.010, p<0.001) declined after 2013. Use of S1P receptor modulators (slope=-0.026±0.016, p=0.005) declined after 2015. Among commonly used higher-effectiveness DMTs, B-cell depletion therapies (slope=0.051±0.027, p<0.001), particularly ocrelizumab (slope=0.020±0.016, p<0.001), showed a marked increase since 2017. Natalizumab usage peaked in 2012 (slope pre-2012 =0.063±0.012, p pre-2012 <0.001; slope post-2012 =-0.027±0.008, p post-2012 <0.001). Other DMT classes such as cell proliferation inhibitors, chemotherapy agents, and purine blockers, showed low usage during follow-up.
Conclusion
Real-world evidence from two large EHR-based MS cohorts highlights distinct temporal shifts in the MS therapeutic landscape toward higher-effectiveness DMTs, particularly B-cell depletion therapy.
Key Messages
Accurate identification of patients diagnosed with multiple sclerosis (MS) from real-world clinical data is essential for tracking longitudinal prescription patterns at scale and understanding the evolution of the MS therapeutic landscape.
Leveraging electronic health records (EHR) data, our knowledge graph-guided unsupervised algorithm accurately and efficiently identified MS patients from two large, independent healthcare systems.
Temporal analysis using knowledge graph-guided methods revealed major shifts in MS-related disease-modifying therapy (DMT) prescriptions, including a decline in early injectable use and increased adoption of B-cell depletion therapies.
These findings confirm a growing preference for higher-effectiveness DMTs in MS and provide a scalable framework for evaluating long-term treatment patterns across healthcare systems.
