Decision Making in Multiple Sclerosis Care Under Uncertainty

Unity Health Toronto

Status: Unknown

Conditions: Multiple Sclerosis

Treatments: Other: Quantitative risk

Study type: Interventional

Funder types: Other; Industry

Identifiers

NCT04035720

15-340

Details and patient eligibility

About

The main objectives of this study are:

i) To determine patient-level, physician-level and health system factors influencing therapeutic decisions in multiple sclerosis (MS) care by applying conjoint discrete experiments.

ii) To determine the prevalence of therapeutic inertia among participating neurologists.

iii) To compare clinical judgement vs. a qualitative or quantitative approach when assessing for a given case-scenario.

iv) To evaluate the influence of decision fatigue in treatment decisions.

Full description

The landscape of MS care is changing. Currently, there are over 15 disease modifying agents (DMTs) available to treat MS, with varying availability around the world.

Significant heterogeneity exists in the efficacy and risks associated with these therapies.

Neurologists caring for MS patients face important choices in each medical encounter: 1) continue with the same management, 2) initiate or escalate therapy for a more effective or safer agent, or 3) consider a reassessment within months under the uncertainty of the current status of the patient.

Limited information on how physicians weigh in different factors when making therapeutic decisions.

Physicians (cognitive biases affecting decision making) and health system (e.g. access to an infusion center) factors are the most responsible causes of practice gaps in MS care. The physician's component is the least studied.

Therapeutic inertia (TI) is a common phenomenon in MS care defined as lack of treatment initiation or escalation (e.g. switch interferons or glatiramer to fingolimod /alemtuzumab /natalizumab/ocrelizumab/ etc.) when recommended by guidelines or evidence of disease progression. This phenomenon leads to poorer patient's outcomes, greater disability, and diminished quality of life.

Goals of the study: i) to determine what are the most relevant factors influencing therapeutic decisions among neurologists with expertise in MS care; ii) to asses whether physicians rely on medical information provided in a case scenario versus a quantitative or qualitative estimation of disease progression based on hypothetical models.

Enrollment: 450 estimated patients

Sex: All

Ages: 23 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

Inclusion Criteria:

• Actively practicing neurologist
• Expertise in treating patients with multiple sclerosis (at least 12 per year)
• Clinical setting: academic or community institutions, private practice or outpatient clinic
• Certified physicians in their specialty
• Online consent to participate in the study

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

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450 participants in 2 patient groups

Quantitative risk estimation

Experimental group

Description:

Participants will be exposed to case-scenarios. Each case scenario provides a description of the current clinical situation (e.g. patient age, current treatment, number of relapses, current EDSS, MRI findings, etc). In addition, participants will see a squared box indicating the probability of risk progression (20%, 25%, 85%, 90%). This information may or may not be accurate to reflect potential errors of risk prediction tools.

Treatment:

Other: Quantitative risk

Qualitative risk estimation

Active Comparator group

Description:

Participants will be exposed to the same case-scenarios as the intervention arm. Each case scenario provides a description of the current clinical situation (e.g. patient age, current treatment, number of relapses, current EDSS, MRI findings, etc). In addition, participants will see a squared box indicating a qualitative probability of risk progression (low, high). This information may or may not be accurate to reflect potential errors of risk prediction tools.

Treatment:

Other: Quantitative risk