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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
LAOPIIII完成签到,获得积分10
1秒前
1秒前
1秒前
1秒前
华仔应助A神采纳,获得10
1秒前
1秒前
2秒前
2秒前
qyj发布了新的文献求助10
2秒前
LAOPIIII发布了新的文献求助10
5秒前
月蚀六花发布了新的文献求助10
5秒前
烟花应助纯情的凡双采纳,获得10
5秒前
健忘冬灵发布了新的文献求助10
6秒前
PAPER完成签到 ,获得积分10
6秒前
LHD201520完成签到,获得积分10
6秒前
热情的戾发布了新的文献求助10
7秒前
Asteroid发布了新的文献求助10
8秒前
8秒前
爱学习的孩纸完成签到 ,获得积分10
8秒前
提前退休发布了新的文献求助10
9秒前
西门晴完成签到,获得积分10
9秒前
10秒前
两天浇一次水完成签到,获得积分10
10秒前
深情安青应助Z170采纳,获得10
11秒前
一只橙子完成签到,获得积分10
12秒前
tinna发布了新的文献求助10
13秒前
orixero应助丝丝采纳,获得10
13秒前
13秒前
月蚀六花发布了新的文献求助10
14秒前
LXN发布了新的文献求助10
14秒前
14秒前
xiaobo完成签到,获得积分10
15秒前
健忘冬灵完成签到,获得积分20
15秒前
喜悦寒凝完成签到,获得积分10
15秒前
15秒前
peng发布了新的文献求助10
16秒前
16秒前
16秒前
17秒前
Star完成签到,获得积分10
17秒前
高分求助中
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Propeller Design 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Handbook of pharmaceutical excipients, Ninth edition 1500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6009869
求助须知:如何正确求助?哪些是违规求助? 7552460
关于积分的说明 16132052
捐赠科研通 5156526
什么是DOI,文献DOI怎么找? 2761950
邀请新用户注册赠送积分活动 1740404
关于科研通互助平台的介绍 1633283