Knowledge graph-guided multiple sclerosis identification and therapeutic trend analysis: Real-world evidence from two large healthcare systems
TL;DR
An unsupervised phenotyping algorithm is developed that leverages a publicly available knowledge graph and informative EHR features to identify patients with MS and achieves robust performance in predicting MS diagnosis, highlighting distinct temporal shifts in the complex MS therapeutic landscape toward higher-effectiveness DMTs, particularly B-cell depletion therapy.
Abstract
The multiple sclerosis (MS) therapeutic landscape has evolved over time. We conducted a knowledge graph-guided analysis of MS-specific disease-modifying therapy (DMT) prescription trends using longitudinal real-world clinical data. We analyzed registry-linked electronic health record (EHR) data from two large independent healthcare systems between 2004 and 2022, including both academic and community practices. We developed an unsupervised phenotyping algorithm that leverages a publicly available knowledge graph and informative EHR features to identify patients with MS. After identifying MS cases, we combined the two cohorts and constructed annual time-varying knowledge graphs that capture co-occurrence patterns between DMTs and MS diagnosis. For each year, we analyzed co-occurrence patterns between DMTs and MS diagnosis using Shifted Positive Pointwise Mutual Information transformation and singular value decomposition to generate embeddings. We computed patient-wise cosine similarities and confidence intervals to quantify MS-specific DMT usage patterns. 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, MS-specific prescriptions declined after 2011 for interferon-beta (DMT-MS cosine similarity slope = −0.019 ± 0.011, p = 0.002) and glatiramer acetate (slope = −0.013 ± 0.012, p = 0.026), from 2013–2020 for fumarates (slope = −0.028 ± 0.015, p = 0.004), and after 2014 for S1P receptor modulators (slope = −0.026 ± 0.016, p = 0.005). 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 =0.032), showed a marked increase since 2018. Natalizumab usage peaked in 2011 (slopepre-2011 = 0.063 ± 0.013, ppre-2011 < 0.001; slopepost-2011= −0.027 ± 0.008, ppost-2011 < 0.001). Other DMT classes such as cell proliferation inhibitors and chemotherapy agents, showed low usage during follow-up. These findings provide real-world evidence from two large EHR-based MS cohorts, highlighting distinct temporal shifts in the complex MS therapeutic landscape toward higher-effectiveness DMTs, particularly B-cell depletion therapy.