Real-World Evidence Expertise in Dravet Syndrome

Identifying patients with Dravet syndrome in real-world data (RWD) is difficult due to the small patient population, but worth it – real-world evidence is vital for improving patient care, lowering healthcare costs, and addressing unmet need for patients.

We have developed an algorithmic approach to identifying patients with Dravet syndrome in RWD, and our analysis of hospital and patient data gives unparalleled insight into the effectiveness and safety outcomes of treatments.

If you have an asset for Dravet syndrome (DS), you’re likely facing significant evidence gaps that make it harder to demonstrate value to payers, health technology assessment (HTA) bodies, and market access stakeholders.

Common challenges include:

• • Small, heterogeneous patient populations

• • Difficulty identifying DS patients due to the variable presentation of symptoms over time and the absence of standardized diagnosis codes

• • Lack of validated or comparable outcome measures

• • Limited follow-up in trial data often insufficient to measure outcomes (e.g., cognitive impairment, motor and gait disturbances, evolution of seizures, chronic comorbidities)

• • Sparse information on costs and healthcare resource utilization

When traditional trial data is limited, real-world evidence (RWE) can help bridge these gaps by leveraging data from actual clinical practice to generate meaningful insights into Dravet syndrome patients.

Typical RWE applications include:

• • Demonstrating burden of illness and unmet need

• • Validating surrogate endpoints

• • Creating external control arms for comparative effectiveness

• • Supporting clinical and healthcare resource use inputs for cost-effectiveness modeling

Email us at contact@lumanity.com to discuss your research needs.