Evaluating Personalized Care for Rare Disease: A Decision-Analytic Framework for Patient-Centered Solutions in Multiple System Atrophy
Author(s)
Beate Jahn, PhD1, Igor Kuchin, MD MSc1, Gaby Sroczynski, MPH DrPH1, Marjan Arvandi, MStat PhD1, Julia Santamaria, MA1, Sibylle Puntscher, PhD1, Daniela Schmid, PhD2, Florian Krismer, MD PhD3, Anette Schrag, MD PhD4, Petra Schwingenschuh, MD PhD5, Alessandra Fanciulli, MD PhD3, Uwe Siebert, MPH MSc ScD MD6.
1Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL – University for Health Sciences and Technology, Hall in Tirol, Austria, 2Faculty of Life Sciences, Albstadt-Sigmaringen University, Sigmaringen, Germany, 3Department of Neurology, Medical University Innsbruck, Innsbruck, Austria, 4Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom, 5Department of Neurology, Medical University of Graz, Graz, Austria, 6Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL – University for Health Sciences and Technology; Harvard Chan School of Public Health; Harvard Medical School, Hall in Tirol, Austria.
1Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL – University for Health Sciences and Technology, Hall in Tirol, Austria, 2Faculty of Life Sciences, Albstadt-Sigmaringen University, Sigmaringen, Germany, 3Department of Neurology, Medical University Innsbruck, Innsbruck, Austria, 4Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom, 5Department of Neurology, Medical University of Graz, Graz, Austria, 6Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL – University for Health Sciences and Technology; Harvard Chan School of Public Health; Harvard Medical School, Hall in Tirol, Austria.
OBJECTIVES: Multiple system atrophy (MSA) is a rare neurodegenerative movement disorder with no effective treatment. Personalized care protocols tailored to individual needs can mitigate the decline in quality of life (QoL). We aim to develop a decision-analytic modelling framework for future implementation to assess long-term effectiveness, cost-effectiveness, and cost-utility of personalized best medical care (PBMC) with or without telemedicine (TM) compared to usual care in patients with MSA, to inform decision makers.
METHODS: We developed a decision-analytic framework to guide model design and data implementation, informed by literature and input from an interdisciplinary expert panel including clinicians, QoL and end-of-life care specialists, epidemiologists, ethicists, HTA experts, and decision scientists. The framework defines research questions, population, comparators, time horizon, health states, model type, and analytical methods. We followed ISPOR-SMDM and causal modeling guidelines.
RESULTS: The framework includes three interventions: 1) PBMC, 2) PBMC with TM, and 3) usual care (based on the European MSA cohort and MeDeMSA trial, NCT06072105). Health states were defined using the Unified Multiple System Atrophy Rating Scale Part IV, ranging from 1 (independent) to 5 (bedridden), incorporating background and MSA-specific mortality. Given the manageable number of states, a Markov state-transition cohort model was chosen. The model simulates a cohort starting at age 61, with 6-month cycles and a lifelong horizon. Outcomes include life expectancy, quality-adjusted life expectancy (QALE), harms, costs, and incremental cost-effectiveness ratios from both societal and healthcare perspectives. Natural history and transitions are informed by the European MSA cohort. Utility values, based on EQ-5D scores, inform QALE estimation. Resource use data are drawn from the trial, with additional inputs from literature and public datasets.
CONCLUSIONS: We present an evidence-based decision-analytic framework to evaluate long-term outcomes of best medical care for MSA. Future analyses will support clinical and policy decisions to improve care for patients with MSA.
METHODS: We developed a decision-analytic framework to guide model design and data implementation, informed by literature and input from an interdisciplinary expert panel including clinicians, QoL and end-of-life care specialists, epidemiologists, ethicists, HTA experts, and decision scientists. The framework defines research questions, population, comparators, time horizon, health states, model type, and analytical methods. We followed ISPOR-SMDM and causal modeling guidelines.
RESULTS: The framework includes three interventions: 1) PBMC, 2) PBMC with TM, and 3) usual care (based on the European MSA cohort and MeDeMSA trial, NCT06072105). Health states were defined using the Unified Multiple System Atrophy Rating Scale Part IV, ranging from 1 (independent) to 5 (bedridden), incorporating background and MSA-specific mortality. Given the manageable number of states, a Markov state-transition cohort model was chosen. The model simulates a cohort starting at age 61, with 6-month cycles and a lifelong horizon. Outcomes include life expectancy, quality-adjusted life expectancy (QALE), harms, costs, and incremental cost-effectiveness ratios from both societal and healthcare perspectives. Natural history and transitions are informed by the European MSA cohort. Utility values, based on EQ-5D scores, inform QALE estimation. Resource use data are drawn from the trial, with additional inputs from literature and public datasets.
CONCLUSIONS: We present an evidence-based decision-analytic framework to evaluate long-term outcomes of best medical care for MSA. Future analyses will support clinical and policy decisions to improve care for patients with MSA.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
P50
Topic
Health Service Delivery & Process of Care, Methodological & Statistical Research, Study Approaches
Disease
Neurological Disorders, No Additional Disease & Conditions/Specialized Treatment Areas, Personalized & Precision Medicine, Rare & Orphan Diseases