HEALTH ECONOMIC IMPACT OF SOFTWARE-ASSISTED BRAIN MRI ON TREATMENT DECISIONS AND OUTCOMES IN RELAPSING-REMITTING MULTIPLE SCLEROSIS: A MICROSIMULATION STUDY
Author(s)
Jayson Brian Habib, MPH1, Margaret H. Ainslie-Garcia, MSc1, Ricardo De La Fuente, MBA, PharmD, PhD2, Diana M. Sima, PhD3, Eric Hawrylyshyn, MSc1.
1EVERSANA Life Science Services, Victoria, BC, Canada, 2GE HealthCare, Chicago, IL, USA, 3icometrix, Leuven, Belgium.
1EVERSANA Life Science Services, Victoria, BC, Canada, 2GE HealthCare, Chicago, IL, USA, 3icometrix, Leuven, Belgium.
OBJECTIVES: A microsimulation cost-effectiveness model was developed to assess the value of software-assisted magnetic resonance imaging (MRI) for detecting disease activity and informing treatment decisions in patients with relapsing-remitting multiple sclerosis (RRMS) (DOI:10.3390/brainsci11121570). This study sought to update results using the most recent utility, cost, and efficacy data.
METHODS: The model compared decision-making strategies in a simulated RRMS cohort assessed using 1) clinical evaluation alone (relapse and disability progression), 2) clinical evaluation plus visual MRI assessment, and 3) clinical evaluation plus MRI scans assessed using software-assisted lesion detection and brain volume loss estimation (icobrain ms). The model simulated clinical (EDSS transitions, relapses) and subclinical disease activity and progression (new lesions, brain volume loss), incorporating low- and high-efficacy disease-modifying therapies (DMT), DMT switch, and DMT escalation upon detected activity. Potential error factors that may occur during decision making (incomplete detection of new lesions or inexact computation of brain volume loss) were also modeled. Outcomes included time on ineffective therapy, quality-adjusted life years (QALYs), and annual health state costs from a US payer perspective over 10-15 years.
RESULTS: The average time with undetected disease progression while on low-efficacy treatment was 2.9±2.6 years with clinical assessment alone. Clinical assessment accompanied by visual read of an MRI scan reduced time with undetected disease activity (2.0±1.6 years), with further reductions provided by software-assisted MRI (1.0±0.2 years). Software-assisted detection led to faster escalation to a high-efficacy DMT, resulting in improved health outcomes through progression offsets. Software-assisted MRI was dominant, generating incremental gains of 0.18-0.23 QALYs over 10 and 15 years versus clinical evaluation alone, translating to lower annual health state costs, with savings of approximately $1,917-$1,994 USD per patient.
CONCLUSIONS: Software-assisted MRI assessment supports timely treatment optimization through enhanced detection of disease activity in patients with RRMS, and should be considered a value-enhancing strategy for management of MS.
METHODS: The model compared decision-making strategies in a simulated RRMS cohort assessed using 1) clinical evaluation alone (relapse and disability progression), 2) clinical evaluation plus visual MRI assessment, and 3) clinical evaluation plus MRI scans assessed using software-assisted lesion detection and brain volume loss estimation (icobrain ms). The model simulated clinical (EDSS transitions, relapses) and subclinical disease activity and progression (new lesions, brain volume loss), incorporating low- and high-efficacy disease-modifying therapies (DMT), DMT switch, and DMT escalation upon detected activity. Potential error factors that may occur during decision making (incomplete detection of new lesions or inexact computation of brain volume loss) were also modeled. Outcomes included time on ineffective therapy, quality-adjusted life years (QALYs), and annual health state costs from a US payer perspective over 10-15 years.
RESULTS: The average time with undetected disease progression while on low-efficacy treatment was 2.9±2.6 years with clinical assessment alone. Clinical assessment accompanied by visual read of an MRI scan reduced time with undetected disease activity (2.0±1.6 years), with further reductions provided by software-assisted MRI (1.0±0.2 years). Software-assisted detection led to faster escalation to a high-efficacy DMT, resulting in improved health outcomes through progression offsets. Software-assisted MRI was dominant, generating incremental gains of 0.18-0.23 QALYs over 10 and 15 years versus clinical evaluation alone, translating to lower annual health state costs, with savings of approximately $1,917-$1,994 USD per patient.
CONCLUSIONS: Software-assisted MRI assessment supports timely treatment optimization through enhanced detection of disease activity in patients with RRMS, and should be considered a value-enhancing strategy for management of MS.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
EE663
Topic
Economic Evaluation, Medical Technologies
Disease
Neurological Disorders, No Additional Disease & Conditions/Specialized Treatment Areas