Projects Delivered

Lung conditions like asthma and COPD can be incredibly unpredictable, and it can be very hard to foretell when an attack is likely to occur. Inconsistent use of an inhaler (or other treatment), smoking, obesity, history of respiratory infections, and more, are associated with higher risk of attacks. Despite knowing so many risk factors, identifying who is actually going to have an attack has proven a challenging task.

This research will examine conventional and newer medications used in rheumatology. This will include medicines for inflammatory diseases, like rheumatoid arthritis and lupus. These diseases occur when the body's immune system overreacts, causing swelling and pain. It will also include medicines used in diseases that affect the bones, like osteoporosis and rickets. We will study the benefits and side effects of medications over a longer time period compared to trials and consider groups often excluded from trials, such as those with more medical conditions. We will also examine differences in clinical factors and patient characteristics to see if these can help with treatment decisions. This is important because in many cases we cannot predict which medication will work for a patient before starting the treatment.

In this study we aim to conduct an evaluation on trends and assess whether there are inequalities or differences in risk assessment, treatment and outcomes of individuals without cardiovascular disease by sex, age, ethnicity, severe mental illness, and deprivation status, while also exploring the feasibility of the novel federated data access approach.

Chronic obstructive pulmonary disease (COPD) is a common lung condition, often caused by smoking. Patients living with COPD may suffer from shortness of breath and this can worsen unpredictably in flare-ups known as exacerbations. These often require hospital care. COPD is the commonest cause of emergency attendance to the hospital with breathlessness, and the third commonest cause of death worldwide. We plan to use health data from deidentified people with COPD to find risk factors for these exacerbations and other harmful outcomes including death. This will include using machine learning techniques, where advanced computers look for patterns in records that might otherwise be missed. Our aim is to create a new prediction tool that could be used to target care to patients identified at risk of deterioration. 

This project is a continuation of DL_2022_024 and involves new project partners in NHS Lothian to quickly move developments from this project into patient care. 

About one in two hundred people have myalgic encephalomyelitis (ME), sometimes referred to as chronic fatigue syndrome (CFS). ME is an understudied illness. How symptoms and treatments vary across the lifespan of people with ME is unknown. This disease is associated with very poor health-related quality of life. There is no cure or effective treatment of symptoms and no biomedical understanding of what causes ME. Our project is intended to shine a light on the lifelong experiences of thousands of people with ME in Scotland. We will use electronic health record data within DataLoch to understand factors that pre-dispose them to the illness and the impact the disease has on their life and long-term health. We hope to find different illness patterns to better understand how subgroups of ME patients experience the disease over their lifetime, and to look for markers from clinical tests that could identify people with ME.

As they get older, many people develop multiple long-term conditions that affect their health. People with multiple conditions often take multiple medicines.  Medicines help us manage our conditions but they may also cause harm.  Some medicines that we take for different types of conditions have shared harms and may interact with the conditions that a person has. This may increase the risks and harms of medicines. This project will explore how medicines contribute to risk of falls and kidney damage, and how their risk varies depending on other conditions or prescriptions that a person has.  

The aim is to examine the risk of harms associated with different medicines, and whether the risk of medicines varies in people:
-     With multiple health conditions
-     On combinations of medicines that may cause the same harm
-     Taking multiple medicines even if not known to be associated with a specific harm

Metabolic dysfunction-associated steatotic liver disease is the commonest cause of liver disease, affecting 1 in 4 adults. However, not everyone with a fatty liver goes on to have serious liver problems; only about 20% progress to the more severe form, called non-alcoholic steatohepatitis (NASH), which can lead to liver cirrhosis (where healthy cells are replaced by scar tissue), liver cancer and premature death. Currently, we have no way of telling which people with MASLD might develop NASH or cirrhosis and there are no medicines ‘on the market’ to treat this condition. The crucial question is why does MASLD progress in some people but not in others? The answer to this will lead to new diagnostic tests, and effective treatments. To address this, we aim to build up a large group (n=1000) of MASLD cases from across Scotland, using NHS liver samples that are no longer needed and collecting relevant clinical information from electronic health records. This information will help us to understand about: 1) the sort of liver damage that can develop in MASLD; 2) which genes are present in the liver as MASLD gets more severe; and 3) how this relates to various health problems in people with MASLD. We will keep this information in a secure database and analyse it using bioinformatics (an approach that uses computer science to understand biological data) in order to improve the care of people with MASLD.

In Scotland and other parts of the United Kingdom type 2 diabetes is more common in people who live in more deprived areas.  We do not know whether this can be explained by different patterns of overweight and obesity in more and less deprived areas.  This project will use records from GPs (from which names, addresses and other identifiable information such as full date of birth have been removed) to describe how many people in Lothian were given their first diagnosis of type 2 diabetes in 2022 and 2023 and compare the proportions between people in the most and least deprived fifth of the population.  We will also compare weight patterns in people who received a diagnosis of type 2 diabetes in 2022 and 2023 with those of people of a similar age and the same sex and find out if differences in weight patterns appear to explain any differences linked to deprivation.