Volv Global Blog

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.

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.

At Volv we provide the insights on our approaches and methodology 

Quantifying Predictive Power of Features in Electronic Health Record Models – the Volv inTrigue way

When we work on complex prediction models with our inTrigue methodology, we are often asked to help clinicians and others to interpret these models by listing the patient features (attributes) which are used by the model to form its predictions. And indeed, generating a list of predictors ranked by their ‘importance’ in a model can translate to improved interpretability and clinical impact. However, there is some work that needs to be carefully considered in order to produce tooling that is derived from complex models that can provide real benefit in a clinical situation. So Rich Colbaugh and I decided to discuss this for you, our audience.

as a challenge for customers and is discussed at many conferences. As it is important to deliver real world results, we discuss here what Volv does to create interpretable models of true utility. The journey from complex modelling to interpretable models is however not necessarily simple when dealing with real-world solutions involving highly dimensional messy data.

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.