Tailoring diagnosis and treatment pathways for complex lung disease

Dr Anjali Crawshaw shares insights into her work on idiopathic pulmonary fibrosis and how AI may be able to extend the survival rates of people living with complex lung disease.

University Hospitals Birmingham NHS Foundation Trust has recently launched what it understands to be a world-first project aiming to improve the survival rates of people living with fibrotic lung disease, including idiopathic pulmonary fibrosis (IPF).

Lung disease clinicians and researchers will use sophisticated algorithms developed by the Cambridge medical data company Qureight to read patient lung scans. The goal is to help improve understanding of fibrotic lung diseases and make more accurate and earlier diagnoses, facilitating earlier treatment.

In addition, the project will analyse significant volumes of data from ethnic minority groups to address health inequalities in the system and allow for a tailored approach to treatment for these individuals.

Dr Anjali Crawshaw is consultant respiratory physician lead, Birmingham Interstitial Lung Disease Unit, University Hospitals Birmingham NHS Foundation Trust. Here, she explains why complex inflammatory and fibrotic lung diseases – her area of specialism – can be challenging to manage in clinic and how the research will help unlock valuable insights from existing patient data.

What is idiopathic pulmonary fibrosis?

Idiopathic pulmonary fibrosis (IPF) is the most common type of fibrotic lung disease that affects roughly 50 in every 100,000 people. It causes the lungs to become scarred, leading to cough, severe breathlessness and progressive respiratory failure. It currently has a survival time worse than most cancers.

Why is inflammatory fibrotic lung disease so difficult to diagnose?

It can be difficult to classify the disease due to the complex and varied patterns seen. In addition, deciding if this is responding to treatment, is stable or getting worse can be challenging. It is currently necessary for specialist radiology doctors to analyse CT scan images of lungs as part of the diagnosis and monitoring process, but the process can be open to interpretation bias. One of the widely accepted and published difficulties in this field, is that if you have multiple doctors looking at the same scan, you won’t always get the same answer. One of the advantages of having good quality computer standardised algorithms is that you will.

In addition to a lung doctor specialising in such lung conditions, our multidisciplinary teams involve radiologists, pathologists, specialist nurses and pharmacists who currently make a diagnosis based on the patient history, blood tests and CT imaging. In more complex cases, invasive investigations such as a telescope test into the lungs may be required, which is not without risk. This allows a biopsy to be taken, although sometimes a more invasive biopsy is still required to make a clear diagnosis. Improved imaging techniques have reduced the number of biopsies required.

There’s a shortage of specialists, which can make this process slow and difficult.

What are the limitations with the current healthcare dataset?

One of the problems in healthcare in general is that a lot of our data comes from white people of European descent. There’s partly an assumption that this is the data set we’ve got, and everybody’s healthcare can be extrapolated from this.

That’s not quite right, but we don’t know how that’s not quite right. For example, the lung function of a person of Indian origin born in the US may be better than a relative the same age and build born in India. We don’t really know why that is. There are lots of sociological and environmental factors that are at play here, and we don’t understand what those are.

Idiopathic pulmonary fibrosis – just one of a huge number of fibrotic lung diseases – is another example where unconscious bias may come into play. The ‘typical’ IPF patient is a 70-year-old white man, so a patient from an ethnic minority background presenting with the same symptoms may be at risk of delayed diagnosis.

I look after a lot of people with sarcoidosis who can also develop fibrotic lung disease. They are often much younger and of working age. There’s a greater prevalence in people who are black, and their disease is often more severe, but we just don’t fully understand why that is – the data is not there.

How will the AI tool work for diagnosing lung disease?

All the patients who come through our service get CT scans as part of their diagnostic process. The study algorithm will combine the data from patient scans – for example, their lung and airway volume – with lung function data from tests, blood results and demographic records.

This information will be securely and anonymously processed to deliver insights into the presentation, development and progression of IPF. We will look specifically at the similarities and differences for ethnic minority patients.

Why is Birmingham so uniquely placed to collect this patient data?

We’re a young, super-diverse city. We’re home to people from 187 different nationalities, and more than half the population is from an ethnic minority, so we are perfectly placed to be leading on this work.

Part of the reason we’re missing this data is because you need a certain amount of money and funding to conduct studies. If research is happening in rich countries that have good access to CT imaging that will, by virtue, skew the population of patients in the database as you’re using data from the patients in front of you. Places in other parts of the world have the expertise and drive to do the research, but they don’t have the funding or access to good CT imaging so it doesn’t get done. 

This partnership with Qureight marks a very significant moment for our team. Patient data that truly reflects the unique diversity of Birmingham’s population will be invaluable to the planning and delivery of more equitable patient care – not just in Birmingham and the UK but internationally.