Hypertrophic cardiomyopathy, defined by unexplained heart muscle thickening, is the most common form of inherited heart disease, and has a notorious reputation due to the fact it causes sudden death in young, previously well individuals. the condition can be complicated by heart failure, atrial fibrillation and stroke. however, in contemporary practice most patients will live long, healthy lives.

Management involves estimating the risk of complications, intervening when indicated (for example by implanting a defibrillator to prevent sudden death) and screening first degree family members. For a service to do this effectively, the identity, disease severity and genotype of every diagnosed individual under its care needs to be known. If you were to ask for this information in most UK hospital outpatient departments, it would be difficult to readily obtain. This is because routine hospital statistics focus on inpatient episodes. So despite being in the setting best suited to deliver precision medicine – and to prevent admissions – capabilities to do so may be limited by lack of information.

At University Hospital Birmingham NHS Foundation Trust, we have created a disease management platform to try to address this. It allows relevant data for every diagnosed patient in a hospital to be brought together and areas of need highlighted. Its key components are a clinico-genomic registry, dashboard and an automated means of extracting data from unstructured clinical text. This work involved the creation of a new interdisciplinary team of clinical, IT, clinical informatics and bioinformatic specialists. As many members were doing this alongside their usual commitments, in its early stages the project relied on their enthusiasm alongside support from senior staff and stakeholders. More recently, it has benefitted from being incorporated into the Midlands Health Data Research UK programme.

We have piloted this work in our Heart Muscle Disease Service. Detailed data has now been collected during routine care for almost 1000 patients over 3 years. This includes results for genetic tests, over half of which were performed by a cardiologist, without requiring referral to the genetics department. This mainstream approach has attracted 2 years of funding for a specialist nurse from the BHF Miles Frost Fund. Patients can view this information securely via a web portal.

The dashboard gives clinicians access to the data needed to change care in a single, easily absorbed picture. For the first time, the population can be visualized and segmented in real time. This has allowed care to be targeted at those with increased risk of sudden death, heart failure and stroke (6%, 20% and 23% of the cohort respectively), and led to the detection of 1 in 10 patients who had not been gene tested. As we develop a clearer picture of our entire population, we are in a much better position to plan services.

The data has also been exploited to drive recruitment for the 100,000 Genomes Project – the aim of which is to bring the predicted benefits of genomics to the NHS – and the National Institutes of Health funded HCMR study.

Whilst demonstrating the value of this approach, we are also simplifying data collection. The amount of work involved is significant; data has been extracted from the outset by hand from seven different sources within the hospital IT infrastructure, and several externally. We have recently automated integration of blood results, medications and clinic codes into the dashboard and are preparing to do the same for as many other elements in the data set as is possible.

To identify and characterise diagnosed patients from clinical documents, academic bioinformaticians from the University of Birmingham, working alongside a clinician, have created and validated a natural language processing algorithm. This algorithm has identified a sizeable group of patients across the electronic patient record who were not present in either the registry or hospital coding records.

We are planning to extend the platform through a clinical network in the West Midlands. It will then become increasingly useful for clinicians screening family members as they by integrating smartphone generated data and video clinics will know in advance whether to offer into the platform, as well as risk prediction algorithms which will be developed from the curated data we are collecting.

We envisage a very different service emerging over the next 5 years; one in which low risk patients are followed remotely, with focus placed on new patients and those at increased risk.
Whilst risk prediction will never be perfect, improving current practice will be a step forward for a condition which remains defined by its potentially fatal nature, cardiac or genetic tests, and in the case of genetic testing, precisely what genetic test to offer. This will be particularly useful in the West Midlands as the region is home to one of the largest non transient populations in Europe. The registry will also reach a suitable size for research, a benefit highlighted in the UK’s Life Sciences Strategy.

As improved management results in a growing population of patients, the standard practice of seeing patients in person every year will become untenable. To address this, we are taking advantage of new technologies by integrating smartphone generated data and video clinics into the platform, as well as risk prediction algorithms which will be developed from the curated data we are collecting.

We envisage a very different service emerging over the next 5 years; one in which low risk patients are followed remotely, with focus placed on new patients and those at increased risk. Whilst risk prediction will never be perfect, improving current practice will be a step forward for a condition which remains defined by its potentially fatal nature.

Author

DR WILLIAM BRADLOW is a cardiology consultant in cardiac imaging and heart muscle disease at the Queen Elizabeth Hospital Birmingham. He undertook echocardiography in Auckland and cardiovascular magnetic resonance at the Royal Brompton Hospital, London before training in Oxford. He manages a large cohort of patients with hypertrophic cardiomyopathy and has a specific interest in how data science and technology can be used to better manage chronic disease. @wbradlow1