1. A 2-stage model of heterogenous treatment effects for brain atrophy in multiple sclerosis utilizing the MS PATHS research network.
- Author
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Hersh CM, Sun Z, Conway DS, Sotirchos ES, Fitzgerald KC, Hua LH, Ziemssen T, Naismith RT, Pellegrini F, Grossman C, and Campbell N
- Subjects
- Humans, Female, Male, Adult, Middle Aged, Crotonates administration & dosage, Immunologic Factors administration & dosage, Immunologic Factors pharmacology, Toluidines administration & dosage, Natalizumab administration & dosage, Nitriles, Hydroxybutyrates, Outcome Assessment, Health Care, Ethylene Glycols, Glatiramer Acetate administration & dosage, Glatiramer Acetate therapeutic use, Dimethyl Fumarate administration & dosage, Dimethyl Fumarate pharmacology, Fingolimod Hydrochloride administration & dosage, Fingolimod Hydrochloride pharmacology, Fingolimod Hydrochloride therapeutic use, Atrophy, Brain diagnostic imaging, Brain pathology, Brain drug effects, Magnetic Resonance Imaging, Multiple Sclerosis drug therapy, Multiple Sclerosis diagnostic imaging, Multiple Sclerosis pathology
- Abstract
Background: Two-stage models of heterogenous treatment effects (HTE) may advance personalized medicine in multiple sclerosis (MS). Brain atrophy is a relatively objective outcome measure that has strong relationships to MS prognosis and treatment effects and is enabled by standardized MRI., Objectives: To predict brain atrophy outcomes for patients initiating disease-modifying therapies (DMT) with different efficacies, considering the patients' baseline brain atrophy risk measured via brain parenchymal fraction (BPF)., Methods: Analyses included patients enrolled in the Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) network who started DMT and had complete baseline data and ≥ 6-month brain MRI follow-up. All brain MRIs were acquired using standardized acquisition sequences on Siemens 3T scanners. BPF change risk was derived by linear mixed effects models using baseline covariates. Model performance was assessed by predicted versus actual BPF change R
2 . Propensity score (PS) weighting was used to balance covariates between groups defined by DMT efficacy (high: natalizumab, alemtuzumab, ocrelizumab, and rituximab; moderate: dimethyl fumarate, fingolimod, and cladribine; low: teriflunomide, interferons, and glatiramer acetate). HTE models predicting 1 year change in BPF were built using a weighted linear mixed effects model with low-efficacy DMT as the reference., Results: Analyses included 581 high-, 183 moderate-, and 106 low-efficacy DMT-treated patients. The mean and median number of brain MRI observations per treatment period were 2.9 and 3.0, respectively. Risk model performance R2 =0.55. After PS weighting, covariate standardized mean differences were <10 %, indicating excellent balance across measured variables. Changes in BPF between baseline and follow-up were found to be statistically significant (p < 0.001), suggesting a pathological change. Patients with low brain atrophy risk had a similar outcome regardless of DMT selection. In patients with high brain atrophy risk, high- and moderate-efficacy DMTs performed similarly, while a 2-fold worse BPF change was predicted for patients selecting low-efficacy DMTs (p < 0.001). Similar results were observed in a sensitivity analysis adjusting for pseudoatrophy effects in a sub-population of patients treated with natalizumab., Conclusions: The relative benefit of selecting higher efficacy treatments may vary depending on patients' baseline brain atrophy risk. Poor outcomes are predicted in individuals with high baseline risk who are treated with low-efficacy DMTs., Competing Interests: Declaration of competing interest The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Carrie M. Hersh has received speaker, consulting, and/or advisory board fees from Biogen, Novartis, Bristol-Meyers Squibb, EMD Serono, Genentech, Genzyme, TG Therapeutics, Alexion Pharmaceuticals, and Horizon Therapeutics; she also receives research paid directly to her institution from Biogen, Novartis, Bristol Meyers-Squibb, NIHNINDS 1U01NS111678–01A1 sub-award, and Patient Centered Outcomes Research Institute. Zhaonan Sun is an employee of Biogen and may own stock in Biogen at the time this analysis was conducted. Devon Conway, MD has received research support paid to his institution from Novartis, EMD Serono, Horizon Therapeutics, Biogen, Bristol Meyers Squibb, and the Department of Defense. He has received consulting fees from Novartis, Bristol Myers Squibb, TG Therapeutics, and Arena Pharmaceuticals and speaking fees from Biogen. Elias Sotirchos has consulted for Alexion, Horizon Therapeutics, TG Therapeutics, and Roche/Genentech, and has received speaking honoraria from Alexion and Biogen. Kathryn C. Fitzgerald has no conflicts of interest to disclose. Le H. Hua has served as a consultant to Genentech, Novartis, EMD Serono, TG Therapeutics, Horizon, and Alexion; on scientific advisory boards for Genentech, EMD Serono and Novartis; and has received non-promotional speaker honoraria for Genzyme and TG Therapeutics. She has received research funding paid directly to her institution from Biogen. Tjalf Ziemssen received grants and study funding as well as speaking and consulting fees from Biogen, BMS, Hexal, Merck, Novartis, Roche, Sanofi, TEVA and Viatris. Robert T. Naismith has consulted for Alexion Pharmaceuticals, Biogen, Bristol Myers Squibb, Celltrion, Genentech, Genzyme, EMD Serono, Horizon Therapeutics, Novartis, TG Therapeutics. Fabio Pellegrini was an employee of Biogen and may own stock in Biogen at the time this analysis was conducted. Cynthia Grossman was an employee of Biogen and may own stock in Biogen at the time this analysis was conducted. Nolan Campbell was an employee of Biogen and may own stock in Biogen at the time this analysis was conducted., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)- Published
- 2024
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