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Published on: June 30, 2014
Modified Delphi study of decision-making around treatment sequencing in relapsing-remitting multiple sclerosis
M A Piena1, O Schoeman2, J Palace3
1Modelling and Meta-Analysis Center of Excellence, Pharmerit International, Rotterdam, The Netherlands.
This study sought consensus on modeling assumptions for disease-modifying drug (DMD) sequencing in relapsing-remitting multiple sclerosis (RRMS). Experts agreed on factors for switching DMDs, including relapse rates and adverse events, to improve treatment models.
Area of Science:
- Neurology
- Pharmacology
- Health Economics
Background:
- Current models for disease-modifying drugs (DMDs) in relapsing-remitting multiple sclerosis (RRMS) focus on single treatments.
- Patients with RRMS frequently transition between multiple DMDs throughout their lifetime.
- Understanding treatment switching decisions is crucial for developing realistic RRMS management models.
Purpose of the Study:
- To achieve consensus on modeling assumptions for DMD treatment sequencing in RRMS.
- To inform the development of clinically relevant models for DMD treatment strategies.
- To address the need for evaluating sequential DMD use in RRMS.
Main Methods:
- A modified Delphi technique involving three rounds of expert discussion.
- An international panel of 10 physicians specializing in RRMS management participated.
- Consensus-building on assumptions for modeling DMD treatment sequences.
Main Results:
- Agreement was reached that time to Expanded Disability Status Scale 6.0 is a suitable proxy for disease severity.
- Key factors for modeling DMD switching decisions include time between relapses, MRI outcomes, and adverse event risk.
- Adverse event risk tolerance for DMDs is disease severity-dependent.
- Consensus on some aspects of DMD effectiveness in sequences was achieved, but significant knowledge gaps and uncertainty remain.
Conclusions:
- Valuable insights into clinical decision-making for RRMS treatment sequencing were gained.
- The study's findings validate core modeling concepts for DMD treatment strategies.
- The acquired knowledge facilitates the generation of clinically meaningful results for RRMS management.
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