M A Piena1, O Schoeman2, J Palace3, M Duddy4, G T Harty5, S L Wong5. 1. Modelling and Meta-Analysis Center of Excellence, Pharmerit International, Rotterdam, The Netherlands. 2. Modelling and Meta-Analysis Center of Excellence, Pharmerit International, Berlin, Germany. 3. Department of Neurology, John Radcliffe Hospital, Oxford, UK. 4. Department of Neurology, Newcastle upon Tyne Hospitals, Newcastle upon Tyne, UK. 5. Global Evidence and Value Development, Global Research and Development, EMD Serono Inc., Billerica, MA, USA.
Abstract
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.
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.
Authors: Marjanne A Piena; Sonja Kroep; Claire Simons; Elisabeth Fenwick; Gerard T Harty; Schiffon L Wong; Ben A van Hout Journal: Adv Ther Date: 2021-11-18 Impact factor: 3.845