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Modified Delphi study of decision‐making around treatment sequencing in relapsing–remitting multiple sclerosis

医学 德尔菲法 疾病 多发性硬化 复发-缓解 不利影响 代理(统计) 内科学 人工智能 精神科 机器学习 计算机科学
作者
Marjanne A Piena,O. Schoeman,Jacqueline Palace,Martin Duddy,Gerard Harty,Schiffon L. Wong
出处
期刊:European Journal of Neurology [Wiley]
卷期号:27 (8): 1530-1536 被引量:4
标识
DOI:10.1111/ene.14267
摘要

Background and purpose Existing effectiveness models of disease‐modifying drugs (DMDs) for relapsing–remitting multiple sclerosis (RRMS) evaluate a single line of treatment; however, RRMS patients often receive more than one lifetime DMD. To develop treatment sequencing models grounded in clinical reality, a detailed understanding of the decision‐making process regarding DMD switching is required. Using a modified Delphi approach, this study attempted to reach consensus on modelling assumptions. Methods A modified Delphi technique was conducted based on three rounds of discussion amongst an international group of 10 physicians with expertise in RRMS. Results The panel agreed that the expected time from disease onset to Expanded Disability Status Scale 6.0 is a proxy for disease severity as well as suitable for classifying severity into three groups. A modelled clinical decision rule regarding the timing of switching should contain at least the time between relapses, magnetic resonance imaging outcomes and the occurrence/risk of adverse events. The experts agreed that the assessment of adverse event risk for a DMD is dependent on disease severity, with more risks accepted when the patient’s disease is more severe. The effectiveness of DMDs conditional on their position in a sequence and/or disease duration was discussed: there was consensus on some statements regarding this topic but these were accompanied by a high degree of uncertainty due to considerable knowledge gaps. Conclusion Useful insights into the medical decision‐making process regarding treatment sequencing in RRMS were obtained. The knowledge gained has been used to validate the main modelling concepts and to further generate clinically meaningful results.
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