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Qdata MS (Multiple Sclerosis) Provides Curated Real-World Data to Help the Search for Treatments and a Cure

Author: Heather E. Moss, MD, PhD
November 2022
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Multiple sclerosis (MS) impacts a person’s brain, spinal cord and optic nerves, which comprise the central nervous system (CNS). This unpredictable disease causes a person’s immune system to attack the CNS, disrupting signals to and from the rest of the body.

Nearly 1 million people are living with MS in the U.S., the National Multiple Sclerosis Society estimates. Symptoms of MS include numbness, tingling, pain, fatigue, coordination and balance problems, mood changes, memory problems, and, in severe cases, blindness and/or paralysis. MS is nearly three times more common among women than men, and is commonly diagnosed in women in their 30s and 40s. 

We don’t yet understand the cause of MS or why it is far more prevalent in women. Nor is there a known cause or cure. That’s why it’s so important that researchers have access to curated real-world data (RWD) they need to help search for effective treatments for different patient profiles and a possible cure for this debilitating autoimmune disease.

That’s why I’m excited about the development of Qdata™ MS, Verana Health’s second Neurology Qdata module for clinical researchers. Qdata MS is being derived from the American Academy of Neurology’s Axon Registry®. The Axon Registry, managed by Verana Health’s VeraQ™ population health data engine, is one of the largest–and growing–real-world clinical data registries for neurology. 

Qdata MS helps provide exclusive insights into the MS patient population through de-identified electronic health record (EHR) data including detailed diagnostic classification beyond international classification of diseases (ICDs), such as MS phenotype or clinical characteristics. MS Qdata also offers a more granular understanding of disease activity by applying artificial intelligence (AI) to unstructured clinician notes to extract meaningful variables including Expanded Disability Status Score (EDSS) and clinical relapse. 

Having access to de-identified, curated RWD from patients with MS can help provide researchers with insights to gain a more detailed understanding of how patients with different types of the disease may react to certain treatments, as noted by clinical measures such as ambulatory status, gait, and EDSS over time, for example. 

As the Qdata MS module evolves through development, it is estimated to include curated RWD on about 30,000 patients diagnosed with MS, one of the largest longitudinal multiple sclerosis datasets for research use. By participating in the Axon Registry, physicians and their patients have the opportunity to lend their anonymized experiences to MS research. I also believe this Qdata module will help the broader neurology community to learn more about MS.

Qdata MS could help give clinical researchers real-world insights into the MS patient population through Axon Registry data and even help them identify opted-in practices that may have patients who transition to a progressive disease course. This curated RWD could also help optimize study design for promising MS treatments or even supporting a label expansion or modification for a therapy on the market. 

Life sciences health economics and outcomes research (HEOR) and Medical Affairs teams can utilize Qdata MS to help better understand and track the journey of treated and untreated MS patients. By studying curated RWD, HEOR can make evidence-based decisions about access to specific therapies, while Medical Affairs can have better access to higher-quality research data to share with the medical community.

We know that MS can be difficult to diagnose, because symptoms may be nonspecific and fleeting. We also know that damage can begin occurring in a person’s CNS even before symptoms are detectable. Qdata MS can help to provide MS researchers with access to longitudinal, curated RWD from a wide cross-section of patients with the disease. By combining Axon Registry data with Verana Health’s artificial intelligence to understand clinical notes–such as natural language process (NLP) and machine learning (ML)– as well as clinical and data science expertise, we’re working together to help enable a better understanding of and more effective treatments for MS.