Rare disease research poses specific challenges.
Collecting sufficient data to make reliable predictions about rare disease patients, diagnosis, treatment and progression offers an ongoing challenge. Because rare diseases often present differently among patients, reliable long-term data about their progression can be even more difficult to come by.
Those involved in the diagnosis, treatment and research of rare diseases can take steps to expand their access to long-term data to make educated assessments in the face of incomplete information and to expand the availability of information for future use.
The State of Data Availability for Rare and Ultra-Rare Diseases
“A fundamental challenge in researching rare disease is gaining access to enough patient data; even in this age of interconnectivity and social media,” writes Natalie Douglas, founder of healthcare business accelerator Lucidity.
Some of the top challenges Douglas identifies include:
- Difficulty finding patients and gathering information from them.
- Siloed information, including data that isn’t interoperable.
- Data ownership and control questions.
- Limitations imposed by informed consent agreements.
- Commercial value assigned to data, its ownership, consent, and other related issues by various participants, including health systems and payers.
Collecting rare disease data is only the first hurdle, warns Douglas. Rare disease community participants must also be able to put that data to use.
Alexandre Bétourné and fellow researchers identify a host of additional factors that hinder the ability to gather and use long-term data on rare diseases. These include:
- High rates of patients (approximately 90 percent) have “no approved treatment or therapy” and thus no connection to data-gatherers.
- Populations are small and often geographically diverse, making it difficult to find patients, engage them in studies and follow up with them long-term.
While shortcomings in clinical trial design are often blamed for these failures, Bétourné et al. note that clinical trials cannot be responsible for all of the deficiencies in long-term rare disease data.
Even when data does exist, not all of it is useful for teams seeking insight into long-term topics. Recent advances in genome sequencing, for example, offer individual patients the opportunity to learn about their own genetic situation. This information does not provide a clear view of that individual patient’s long-term prognosis, however. Nor does it shed light on other long-term facts related to the patient population as a whole.
In some cases, genetic information doesn’t even provide a particular patient with meaningful data. A gene report might, for instance, reveal that a patient has a mutated gene — but that mutation may be unconnected to the rare disease, says Cat Lutz, senior director of in vivo pharmacology services at The Jackson Laboratory.
Every clinical trial team must choose which data to collect, Jemima E. Mellerio writes in a 2022 article in the British Journal of Dermatology. Decisions are driven by a number of factors, including the goals of the research, regulatory requirements, and collaboration with other rare disease researchers and treatment providers. Each factor weighs on the eventual methods by which data is chosen, collected, stored, analyzed and shared. As a result, even existing long-term data sets for rare disease outcomes are not always accessible to future providers and researchers.
Benefits and Risks of Sharing Data
Patients, caregivers and healthcare professionals vary in how they weigh the risks and benefits of sharing rare disease-related healthcare data. In a 2022 article in the International Journal of Environmental Research and Public Health, Mariana Amorim and fellow researchers interviewed 159 rare disease patients, 478 caregivers and 63 healthcare professionals about the perceived risks and benefits of sharing genomic data.
Amorim et al. found that while patients and caregivers thought a cure would be the top benefit of sharing genomic data, healthcare professionals were more likely to name the development of new treatments as the most important benefit. Meanwhile, patients and caregivers were primarily concerned about data security and lack of data control, while healthcare professionals worried more about how collected data might be used to discriminate against patients and their families.
The financial cost of sharing data can also be high. The price tag associated with collecting, storing, protecting, converting, analyzing and sharing data is one of the factors driving the high cost of rare disease research, writes Douglas. Yet “data sets need to be shared – including the results of failed trials.”
Douglas also notes that rare disease diagnosis, treatment and research often focuses on the data at the expense of the patient. As a result, many patients feel more like test subjects than human beings.
“Developing communication strategies and consent approaches tailored to participants’ expectations and needs can benefit the inclusiveness of genomics research that is key for patient-centered care,” write Amorim et al.
New Frontiers in Data Collection and Analysis
In recent years, technological developments have advanced the diagnosis, treatment and research of many rare diseases. For instance, a 2022 editorial in Nature Genetics notes that “innovations in sequencing technology and machine learning approaches have positively affected diagnostic success.”
Work remains in the collection of accurate, complete, interoperable long-term data sets.
Patient advocacy organizations and professional associations are also joining in the quest to build better long-term data sets. In 2022, the Global Genes and the Rare Disease Diversity Coalition (RDCC) announced their joint Open Data Initiative to deepen and diversify the data available on rare diseases, especially in historically marginalized populations.
“The Open Data Initiative will create and establish a large-scale information and data gathering effort to help identify and elucidate the unique experiences, challenges, and outcomes of rare disease patients from communities of color in diagnosis, care, and treatment, generating insights, setting metrics and tracking improvements and trends over time,” says Craig Martin, CEO of Global Genes.
Remaking rare disease registries may also offer a way to gather and share better long-term data. In a 2022 study in Frontiers in Pharmacology, Marina Mordenti and fellow researchers describe their efforts to remodel the existing Registry of Multiple Osteochondromas (REM) “into a tool consistent with EMA observations and recommendations.” The study found that success required a focus on three separate topics:
- Semantic interoperability, or the ability to present data in a uniform way and have it read by various programs or algorithms.
- Data quality, which focuses on the reliability and completeness of the data itself.
- Governance, including professional expectations, computer protocols and regulatory considerations.
“Our remodeled REM registry fits with most of the scientific community’s needs and indications, as well as the best techniques for providing real-world evidence for regulatory aspects,” write Mordenti et al.
A great need remains for more complete, accurate and interoperable data on the long-term status of various rare diseases. Current datasets are often incomplete or nonexistent, and sharing data continues to pose challenges. By leveraging new technologies, those who diagnose, treat and research rare diseases can improve access to high-quality, long-term data that reflects the lived experiences of patient populations.
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