Generating Real-World Insights for Rare Disease Research and Clinical Trial Implications | A Case Study in AL Amyloidosis
“Ultra-rare” and “incurable” – keywords that appear all too often when exploring the rare disease landscape. For patients diagnosed with systemic light chain (AL) amyloidosis, these phrases came with what was traditionally an expected survival of mere months. However, the past 40 years have seen incredible progress in the improvement of survival and early mortality rates for these patients, something that could only be achieved through a powerful dedication to research and therapeutic development within the AL amyloidosis community.
Despite such advancements, these two terms remain unfortunately true. Many patients face the understanding of a potentially poor prognosis, ranging from an estimated median of 6 months to 6 years depending on the patient cohort studied, and the risk of sudden cardiac death in what is reported to be almost one-quarter of the population. Indeed, these are risks that persist even in the face of the improved therapeutic options developed over recent years, driving an ongoing need for a better understanding of the disease, its monitoring, and its assessment within the clinical trials that seek to provide new options for improving patient outcomes.
As a disease characterised by the deposition of misfolded protein into tissues, and with the pattern and severity of organ involvement a key factor in survival, there is therefore a critical need for new treatment options that go beyond the current mechanisms of reducing new amyloid production and target the aggregates already deposited in organs. So how do we advance the disease knowledge base and accelerate innovations in the treatment landscape for rare conditions such as AL amyloidosis?
Real-world evidence to inform disease understanding, patient outcomes, and future clinical trials.
Multi-disciplinary approaches continue to form the foundation of a successful research and discovery process. Recently, we collaborated with Professor Ashutosh Wechalekar (Royal Free London NHS Foundation Trust), Dr Jahanzaib Khwaja, and their team to drive new insight into the outcomes and prognostic stratification of patients with AL amyloidosis across two different methodologies for disease staging utilising different thresholds of biomarkers – Mayo 2012 and the European modification of Mayo 2004. While both are validated models, it was hypothesised that the inclusion of patients without a uniform treatment protocol may have had an impact on previous insights derived around survival and the results of 6-minute walking time testing (6MWT).
Many of these patients are normally already known to the system as frequent flyers – patients who need a new way of being managed within the community. Some of these patients also live with long-term conditions and have protocols in place for their care pathway, meaning that ED teams should know who they are and what steps 1-X should be when they present. Again, this can possibly be managed in new ways within the community, putting in place pathways that mean patients only need to present at ED as a last resort where clinically appropriate.
Exploring the recently released trust-level breakdown of these 125,000 12-hour ED waiters, something that caught my eye was one hospital in North East London that had 3,000+ patients attending the ED and waiting for over 12 hours. Now for me, I grew up working with colleagues in the NHS and spending each day working first-hand with the healthcare system. However, when looking at these numbers, and for the first time in my years of working in healthcare, the focus of this reporting seemed to slant more towards outcomes and patient-centric clarity. And with this comes vital considerations for research, evidence generation, personalised medicine, and health inequality. Let’s look at outcomes and health inequality for a second.
From the data, there were clear and significant health inequality challenges for patients – let’s call this ‘access inequality’. The data shows that 31% of patients waited for 12 hours in Barking, Havering and Redbridge University Hospitals Trust, whereas 1% waited for the same time at Guy’s and St Thomas’ Foundation Trust, and 25% at Blackpool. Indeed, our mapping of the percentage of patients waiting more than 12 hours by trust over the deprivation rank for each region highlighted key areas of long waits correlating with higher deprivation.
This 12-month follow-up point proved to be of particular importance, with breakdown by haematological responses showing improvements to the 6MWT from baseline in patients with complete responses (CR) or very good partial responses (VGPR) in Mayo 2012 Staging, and a significant peak in the median change for patients with CR at Stage IIIb of the European Modified Staging. Notably, through multivariable analysis that included modelling of the haematological responses and the distinct staging systems, improvements to 6MWT distances by >44 metres from baseline were found to be a predictor of better patient outcomes, both at 6 and 12 months.
Overall, this work identified worsening 6MWT distances in both staging models across increasing cardiac disease stages, with improvements acting as an independent predictor for better survival in patients with AL amyloidosis. However, the most striking improvements were predominantly found in patients with more advanced disease stages, which in turn varied depending on the staging system analysed. Critically, this has driven new thinking around the applicability of these methods of patient stratification within a uniformly treated cohort and at advanced disease stages. As a result, further investigation is ongoing to explore the need for harmonising cardiac staging systems, as well as how these findings impact the use of key endpoints such as the 6MWT in clinical trials.
Insights in Action | Predicting deterioration and mortality for earlier, preventative care.
From retrospective analytics to prospective live patient monitoring, everything we do is driven by the core focus of improving patient outcomes through more powerful underlying knowledgebases and access to better therapeutic innovations.
To this end, we are also working with Professor Wechalekar and his team to derive additional insight and an actionable system of applying our learnings around prognostic risk factors to care at a patient-level, in order to tackle the current lack of established risk factors and dynamic biomarkers across treatment courses. By bringing in new data elements from which to identify these predictive markers, this will support clinical urgency in identifying patients who may benefit from preventative interventions earlier on. Indeed, with a lingering risk of sudden cardiac death despite the initiation of treatment, there is a critical need to build upon known prognosticators, such as hypotension and poor systolic function, while also factoring in the deep variation that exists between patients.
Enriching current data snapshots beyond limited clinical monitoring, our collaboration sees us integrate real-time remote wearable monitoring of patient vitals alongside more traditional medical records, while also bringing in critical patient-reported outcomes (PROs) currently missing from standard assessment. By combining the lived patient experience together with more objective measurements of sleep, activity, heart rate, ECG, blood oxygen, and healthcare events or utilisation, our goal is to take what is known about the disease and the progression of severe complications to a level that may, in turn, help clinical teams to identify high-risk patients. With more powerful prognostic markers derived from our ecosystem, it is our genuine belief that this work could help to inform future preventative strategies at a level that is currently not possible, by predicting patient outcomes and prompting intervention with the treatments identified to be most effective in tackling these events.
As we continue to see promising players emerge from the innovation pipeline for rare diseases such as AL amyloidosis, we are also actively working to translate our insights and findings to support the clinical trials that hold incredible potential in improving patient lives each day. By establishing what is known at a fundamental disease, population subgroup, treatment, and severity level, our work to date and into the future seeks to drive advancements to the knowledge base for patients, clinicians, scientific researchers, and pharmacological innovators.
With the characterisation of core endpoints, ranges, and feasible targets for specific patient cohorts and disease stages, our ambition is to utilise the data and advanced technology at hand to support therapeutic development through real-world evidence bases. From accelerating regulatory approvals, to ensuring that the clinical support needed to provide these new treatments at the optimal time points is available, Sanius Health continues to support our partners across the rare and chronic disease space. Built from a core focus on driving the possibilities around preventing mortality or further disease deterioration, we welcome anyone who wishes to know more about our ecosystem or how it could support you to contact us at firstname.lastname@example.org.