Great Ormond Street Hospital, a specialist children’s centre, has established a digital research unit, including NHS clinicians, UCL academics and industry partners. Our aim is to improve child health by optimising the use of clinical data for research. This project will demonstrate how routinely collected, non-identifiable patient data can be used and tracked through the complete research cycle. We will take advantage of cloud-platforms and new technology (‘Fast Healthcare Interoperability Resources’ ;FHIR) for the collection and safe handling of healthcare data. This approach will support the development of various healthcare ‘apps’, for patients, families and healthcare professionals, similar to existing phone apps used regularly by millions. Throughout this project, data is collected, accessed, and used in a safe and secure manner. The development of SMART apps does not require access to personal data while FHIR infrastructure can make health data available, irrespective of the IT system used by the organisation. The availability of health data will support research across NHS organisations that will directly benefit patients and clinical staff by improving knowledge, communication and healthcare management, and will support future developments such as artificial intelligence, without personal data being shared with technology companies that can provide these services.

Multiple sclerosis (MS) is a devastating condition, which is rare in children. It is caused by the body's immune system destroying parts of the myelin sheath, a fatty protective covering of nerves in the brain and spine, which is essential for transmission of messages from the brain to the rest of the body. Currently there is no cure, and over time individuals accumulate progressive disability. In children and young people this can affect movement, vision and particularly their thinking. There has been an explosion in recent years in the treatments available, most of which have not been trialled in children. There are currently 14 different treatments available which aim to modify the body's inflammatory response. Choosing the right treatment for the individual child is difficult, as there are no tools to help us predict which medication is likely to work best for them. Much of the time paediatricians need to rely on adult data. MS in children though is different from that in adults; they have more relapses, more brain lesions and develop more learning problems than adults. The only paediatric trial so far completed, identified benefits and risks of treatment which are different from those seen in adults. This makes it crucial to gather information in children, rather than relying on information acquired in adults. I am a Paediatric Neurologist who specialises in MS. In this project, I will partner with Prof Ciccarelli, who in 2018 was awarded a NIHR Research Professorship, to develop a tool to predict the best medicine to use for the individual with adult MS, by using special mathematical models that learn from the individual MS profile (demographic and diet, lifestyle, clinical findings, specific blood tests, genetics and MRI images) and make prediction about the future. I will extend this goal to children, and together we will develop a tool to help guide treatment choice for any patient with MS, independently of their age. I will take this unique opportunity to focus on cognitive impairment in children with MS. MS is a highly specialised and complex condition in childhood, and NHS England has recently agreed to fund 5 Highly Specialist Services (HSS) across England, to ensure excellence in delivering care. I lead one of these services and will collaborate with the other centres. In this project, I will look at two groups: the first group is the existing cohort of 100 children with MS across England, the second group includes 80 children with newly diagnosed MS. Children who do not wish to start a medication will still have their data recorded and will be used as a control group. We will use tablet computers in clinic to record information about diet, lifestyle, exposure to sunlight and nicotine, amongst other parameters, as well as quality of life. We will also document their clinical examination and their educational performance and academic ability. We will take blood to look for markers of inflammation which might provide important clues. We will also record their relapses, to document how well controlled their MS disease activity is. All this information, together with repeat MRI scans acquired routinely as part of the NHS HSS, will be analysed by computer both separately and together with the adult data. We will use a tiered approach to identify factors which are likely to predict which medicine will work best for any one person with MS. All these data will be stored in a national registry, thereby providing valuable information on the long-term outcome for these young people in England. All the medications and any serious side effects will also be recorded on the database. This will allow us over time to identify early any unexpected safety concerns with the medications. The ultimate goal of the project is to learn about the individual treatment response and side effects in the clinical setting and to help the child and their parents choose the best medication for them as an individual.

Magnetic Resonance Imaging (MRI) scans play a vital role in helping many ill children, by finding out what the problem is and helping plan their treatment. MRI is safe because it does not use radiation. MRI scans produce good-quality pictures or images of many parts of the body, including the brain, heart, spine, joints and other organs. The main problem is they take a long time - often over an hour. During the scan, the child has to keep very still and may even need to hold their breath many times. This is especially hard for children and unwell patients. Hence, younger children under 8 years old need a general anaesthetic, to put them to sleep during the scan. In many childhood diseases, for example in cancer, children may need many MRI scans to follow up disease progression and treatment. Being put to sleep for all of these scans is not pleasant for the child and may occasionally cause problems. It also puts a lot of pressure on hospitals who need to find the doctors, beds, equipment and funds for this. One way of overcoming these problems would be to speed up the MRI scans so the children do not have to keep still or hold their breath. The simplest way of doing this is to collect less data for each image, but this causes so much distortion in the images that they cannot be used. There are some ways of converting these into useful images, but these are complicated and take too long to use in a hospital. Machine Learning is an upcoming way of teaching computers to find complicated patterns in large amounts of information. Recent advances mean that computers are now so powerful that they can learn effectively. Machine Learning has been successfully used for analysing many types of images, for example to perform de-noising, interpolation, image classification and border identification. Despite its popularity, only a few recent studies have shown its potential for reconstruction of MRI images. This is partly due to the greater complexity of the problem and importantly, the large amounts of data required to 'learn' the solution. At Great Ormond Street Hospital, we have MRI images from over 100,000 children and scan an additional 10,000 children each year, all of which we could use to help train and test Machine Learning technologies. I have already shown that basic Machine Learning techniques can remove distortions from MRI scans of the heart, so I am well placed to develop Machine Learning techniques to reconstruct MRI images from other children's diseases, as well as developing more advanced Machine Learning techniques. I showed Machine Learning to be faster than existing reconstruction methods and the images were of better quality than more conventional state-of-the-art techniques. However, much more work is needed to get Machine Learning working reliably in children's scans and to make the most of the possible benefits. If we can use fast scanning with Machine Learning we could shorten scan times from 1 hour to about 10 minutes for children having MRI scans. They would not have to keep completely still for the scan and would not have to hold their breath, therefore reducing the need to put patients to sleep. This would make MRI scanning far less difficult and daunting for children, and would eliminate the cost and side effects from the anaesthetic. Quicker scans would help reduce waiting lists and costs for the NHS. It would also mean that MRI scanning would be used far more often, so it could help many more children. Additionally, these techniques could enable MRI scans to become affordable in some countries for the first time.

a. aim(s) of the research: It seems that some children and young people (CYP) remain ill for a long time after infection with COVID virus. They are said to have ‘long COVID’. Something similar can follow a common childhood infection called glandular fever. Doctors don’t know how to diagnose long COVID, how common it is or how long it goes on for. There is no simple test for long COVID. We need to know more about it if we want to treat it. b. background to the research: Little is known about long COVID in adults or CYP. Risk factors for worse COVID in CYP include obesity, pre-existing diseases, learning disabilities, diseases of the brain, mental health problems and coming from an ethnic minority. The CYP likely to be most at risk of long COVID are teenagers who are more at risk of persistent fatigue and mental health problems after other viral infections. c. design and methods used: We will approach 30,000 CYP, half of whom we know had COVID. We expect 6,000 to agree to help us and we will ask them whether they still have physical or mental problems at 3, 6,12 and 24 months afterwards. We can compare the 3,000 responders who had a positive COVID test with the 3,000 responders who didn’t test positive. We can then agree on what is a medical diagnosis of long COVID and how we might treat it. d. patient and public involvement: (PPI): We will have a paid PPI lead who will ensure co-production with carers and CYP. We will also use some funds to encourage busy carers and CYP to give their valuable time to complete the survey s.e. Complete transparency: We will share all our results ASAP for free with anyone who wants to see them, especially the CYP who take part.

Background: Juvenile idiopathic arthritis is one of the most common autoimmune conditions of childhood, occurring when the immune system mistakenly attacks joints, leading to inflammation and pain. Around 10,000 children in the UK suffer from this debilitating condition. Currently, the majority of patients have ongoing disease even after a decade of treatment. Therefore, more research is needed into how the disease can be cured, rather than just controlled. When the immune system mounts an attack against something, some immune cell types remain behind; keeping a memory of the target it was trying to destroy ('memory cells'). Memory cells ensure the immune system can mount a rapid and effective attack if the target is found again in the body. With arthritis in mice, a specific type of memory cell has been found in the joint, which has the ability to activate inflammation again after the arthritis has resolved. In children with juvenile idiopathic arthritis, cells that resemble this type of memory cell have been found in much higher levels in fluid taken from the joints compared to the blood stream, suggesting they are accumulating where the disease is occurring. In other parts of the body, other cell types anchor memory cells to ensure they remain at sites where they are needed and provide signals to ensure memory cells survive for a long time. It is not clear at present which cells might be doing this in the joint and what role memory cells have in juvenile idiopathic arthritis. Aims and Objectives: I aim to test the question of whether repeat flares of inflammation keep occurring in children with juvenile idiopathic arthritis because these memory cells remain in the joint, resisting treatment. In particular I will investigate whether cells known as fibroblasts, which contribute to the connective tissue of the joint, interact with these memory cells to help them to survive and persist in the joint. The plan is to characterise these cells in the joints of children with arthritis, identifying signalling pathways and processes used by the cells. Advances in technology mean that it is now possible to look at the level of individuals cells to see which proteins these cells are making, providing minute resolution of the cell activities. I will investigate how these cells differ in children who get very severe disease. Finally, in genetically-modified mice with arthritis it is possible to eliminate types of fibroblast cells; I will investigate how this impacts the memory cells in joints. Potential Applications and Benefits: The processes that initiate inflammation may not be the same as those perpetuating it, as the disease process evolves. Understanding the cells and processes causing ongoing inflammation in arthritic joints will likely be key for finally achieving a cure for children and adults with autoimmune arthritis. Currently these memory cells are not directly targeted by any available treatments. If memory cells are contributing to ongoing inflammation, we need to know which signals they are responding to in the joint to target them effectively. Treatments affecting the fibroblasts are under development for adults, but further understanding of fibroblasts in childhood arthritis is needed to determine whether these therapies are also likely to be effective in children. Fluid from the joint is easier to obtain than joint tissue, but the downside is that it captures immune cells much better than connective tissue cells, like fibroblasts. Since we will collect and analyse the tissue and the fluid from the same joints, we will learn how the cells in the fluid reflect the biology in the joint tissue. This understanding provides a starting point for developing new therapies that target fibroblasts and novel tests that improve our selection of treatment.