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inTrigue

Improving the Rare Disease Diagnostic Journey with using AI. Using Volv's methodology inTrigue and Scailyte's single-cell data analysis platform ScaiVision

Improving the Rare Disease Diagnostic Journey with Advanced AI

The journey from first symptoms to diagnosis is a long one for most patients with rare diseases. According to a survey from EURODIS, 25 per cent of patients with among the most common rare diseases waited between 5 and 30 years for a diagnosis and 40 per cent were misdiagnosed during that time.

There are many reasons why diagnosis is so challenging. One is that most physicians have limited knowledge about rare or ultra-rare diseases. Another is that as many as 60% of rare diseases present with significant heterogeneity of symptoms, making it extremely difficult to diagnose patients early enough in their disease progression.

A third barrier to diagnosis can often be attributed to the complexity of the diagnostic test currently available. For some rare conditions, deep muscle biopsies are still used for diagnosis and for some rare heart conditions, stents are often the only commercial diagnostic tool available to accurately identify a condition.

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Determining patient prevalence with rare and ultra-rare diseases will help to build your gene therapy value story

Building Your Gene Therapy Value Story From Day One

Cell and gene therapies, also known as advanced therapy medicinal products (ATMPs), are potentially life changing. For patients with rare diseases, they extend the hope of a longer, healthier life and even of a cure. But these therapies are exceptionally expensive with up-front costs ranging from $500,000 to $2 million. Additionally, some incur heavy ongoing costs throughout the life of the patient.

For health technology assessment (HTA) organisations – which must balance clinical effectiveness, safety and efficacy with cost effectiveness, social outcomes and ethical considerations – the decision to support market access for ATMPs is a complex one. Budget constraints mean HTAs and insurers often must make tough decisions balancing the ATMP reimbursement with a reduction in spending elsewhere in the healthcare system. Consequently, therapies that are not viewed as compelling, face rejection. Moreover, the decision-making process can vary from region to region: vastly different decision criteria, for example, are adopted in the UK, the USA and China.

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Why can’t we find the 50% of people with rare diseases who remain undiagnosed?

Why can’t we find the 50% of people with rare diseases?

It might be said that picking out patterns to identify patients with rare diseases is a bit like distinguishing thousands of constellations of stars. Neither is within the scope of the human eye and both require extremely advanced technologies to even begin to decipher and separate patterns. Yet finding the 50 percent of undiagnosed patients with one of the approximately 7,000 rare diseases is a medical and clinical imperative.

Typically, the way clinicians diagnose patients is by taking what the broader healthcare industry knows about a disease – generally as described by key opinion leaders (KOL) – and correlating a patient’s symptoms to those definitions. The problem with this approach when it comes to rare and ultra-rare diseases is that it is subject to experiential bias. If the KOL has not observed a pattern of symptoms or the order in which those symptoms emerge differs significantly, the patient will likely remain undiagnosed.

There is so much we don’t know about rare disease, but what we do know is that there is enormous heterogeneity of symptoms – so much so that as many as 60% of rare diseases present with significant heterogeneity, according to genomics experts. Understanding this 60% variation in symptoms with rare diseases is undoubtedly the greatest challenge facing both healthcare professionals as well as the companies seeking to find and develop new treatment options. Even for those rare diseases where there are already treatments, the difficulty can be diagnosing patients early enough to limit the worst effects of the disease. For example, some symptoms may not be flagged as significant from a clinical perspective, despite the challenges they present to the patient on their journey to diagnosis, and by the time the patient’s symptoms escalate to correlate with recognised patterns, it’s often much later in the disease’s progression, on average, six years from the onset of symptoms.

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This case study derives from a pharmaceutical company that approached Volv to develop a prediction model to find patients suffering from a rare disease

Finding Patients for Rare Diseases: A Case Study

The Case Study Problem

This case study derives from an ongoing Volv engagement, which started in July 2017. A pharmaceutical company approached Volv Global to develop a prediction model to identify additional patients suffering from the rare disease treatable by its specialist medicine.

This pharmaceutical company faced four difficulties: the disease prevalence was one in a million of population; specialist clinicians were able to diagnose the disease with no more than 76% accuracy; only one in four patients were ever identified; and those that were identified, were done so generally after six years of misdiagnoses. To compound these four difficulties, the pharmaceutical company was unable to provide medical records for any already diagnosed patients.

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