ABSTRACT
BACKGROUND AND AIMS: Transition to secondary progressive multiple sclerosis (SPMS) from relapsing-remitting MS (RRMS) is an expected part of the disease trajectory for most patients. However, the transition is challenging to identify due to the gradual nature of progression, and the complications of superimposed relapses, comorbidities, and natural variability in symptoms. This healthcare professional (HCP) survey sought to characterize the transition to and management of SPMS in UK clinical practice. METHODS: Telephone interviews with 20 neurologists and MS specialist nurses from England and Scotland gathered quantitative and qualitative responses. Numerical analyses and theoretical thematic methods were used to identify key emerging themes. RESULTS: The burden SPMS imposes on patients and caregivers was a major theme; discharge from specialist services is common, leading to a sense of abandonment. Respondents acknowledged substantial hesitancy toward identifying SPMS, predominantly due to restricted options of licensed and reimbursed disease-modifying therapies (DMTs) for SPMS compared with RRMS. Currently, HCPs continue DMTs under a label of RRMS, even after recognition of progression. This survey identified MS to be unusual in comparison with other disease areas in that reimbursement guidelines have a direct impact on clinicians' decisions around disease staging. Respondents suggested reimbursed DMTs proven to slow disability progression in SPMS will create a step-change in identifying SPMS, providing rationale to acknowledge progression earlier while removing key obstacles to identification. To aid this change, respondents identified a need for SPMS-specific diagnostic guidance, despite substantial divergence in implementation of current guidance. CONCLUSIONS: In contrast to the current heterogeneity, a more structured and standardized approach to the identification of SPMS, along with guidelines on treatment, will ensure patients can maximally benefit as treatment options for SPMS evolve.
ABSTRACT
OBJECTIVES: Specific economic model types often become de facto standard for health technology appraisal over time. Markov and discrete event simulation (DES) models were compared to investigate the impact of innovative modeling on the cost-effectiveness of disease-modifying therapies (DMTs) in relapsing-remitting multiple sclerosis (RRMS). Fingolimod was compared to dimethyl fumarate (DMF; in highly active [HA] RRMS), alemtuzumab (in HA RRMS) and natalizumab (in rapidly evolving severe RRMS). Comparator DMTs were chosen to reflect different dosing regimens. MATERIALS AND METHODS: Markov and DES models used have been published previously. Inputs were aligned in all relevant respects, with differences in the modeling of event-triggered attributes, such as relapse-related retreatment, which is inherently difficult with a memoryless Markov approach. Outcomes were compared, with and without different attributes. RESULTS: All results used list prices. For fingolimod and DMF, incremental cost-effectiveness ratios (ICERs) were comparable (Markov: £4206/quality-adjusted life year [QALY] gained versus DES: £3910/QALY gained). Deviations were observed when long-term adverse events (AEs) were incorporated in the DES (Markov: £25,412 saved/QALY lost, versus DES: £34,209 saved/QALY lost, fingolimod versus natalizumab; higher ICERs indicate greater cost-effectiveness). For fingolimod versus alemtuzumab, when relapse-triggered retreatment was included in the DES, large cost differences were observed (difference between incremental cost is £35,410 and QALY is 0.10). LIMITATIONS: UK payer perspective, therefore societal approach was not considered. Resource utilization and utilities for both models were not derived from the subpopulations; as the focus is on model type, input limitations that apply to both models are less relevant. CONCLUSIONS: Whilst no model can fully represent a disease, a DES allows an opportunity to include features excluded in a Markov structure. A DES may be more suitable for modeling in RRMS for health technology assessment purposes given the complexity of some DMTs. This analysis highlights the capabilities of different model structures to model event-triggered attributes.
