Author
Listed:
- Ziming Gan
- Wen Zhu
- Weijing Tang
- Sara Morini Sweet
- Michele Morris
- Yunqing Han
- Chenyi Chen
- Junwei Lu
- Emily Song
- Mohammed Moro
- Shyam Visweswaran
- Tianrun Cai
- Tanuja Chitnis
- Tianxi Cai
- Zongqi Xia
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
Suggested Citation
Ziming Gan & Wen Zhu & Weijing Tang & Sara Morini Sweet & Michele Morris & Yunqing Han & Chenyi Chen & Junwei Lu & Emily Song & Mohammed Moro & Shyam Visweswaran & Tianrun Cai & Tanuja Chitnis & Tianx, 2026.
"Knowledge graph-guided multiple sclerosis identification and therapeutic trend analysis: Real-world evidence from two large healthcare systems,"
PLOS Digital Health, Public Library of Science, vol. 5(8), pages 1-15, August.
Handle:
RePEc:plo:pdig00:0001554
DOI: 10.1371/journal.pdig.0001554
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