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Category: Asthma

HDR UK Funding for Queen’s University and University of Edinburgh Collaboration

Congratulations to the team from QUB and UoE on their recent funding success, as part of the HDR UK Inflammation and Immunity Driver Program.

“Our team of clinicians, statisticians and epidemiologists from Queen’s University and the University of Edinburgh have received HDR UK funding to develop prognostic models to help inform treatment decisions for individuals with severe asthma. First line rescue treatment for a severe asthma attack is systemic corticosteroids, which carry a risk of toxicities. Biologic therapies are effective in reducing asthma attacks and have a substantially better safety profile but are more expensive. Currently, Payer Access in the UK advise that individuals with severe asthma will only become eligible for biological therapy after multiple attacks. Identifying individuals at risk of multiple asthma attacks could potentially allow treatment decisions to be brought forward and avoid toxicities due to corticosteroid exposure. Our work aims to develop and validate prognostic models to estimate risk and prospectively identify individuals who will have multiple asthma attacks. This may allow a better understanding of the consequences of delayed intervention with effective therapies to reduce asthma attacks and allow Healthcare Payers and Providers to make more informed treatment decisions for individuals living with severe asthma.” – Andrew Kunzmann, QUB

NIHR Funding for Nottingham’s Matt Martin

Matt Martin and Ralph Akyea (University of Nottingham, Primary Care Stratified Medicine Research Group) have recently received £50k NIHR School for Primary Care Research funding for Development and external validation of a risk prediction model for asthma attacks in primary care: a retrospective cohort study.  Matt is a Consultant Respiratory Physician at Nottingham University Hospitals NHS Trust with research interests in the phenotyping of asthma, asthma attacks and cough, particularly in developing personalised medicine approaches for prevention and treatment of asthma attacks.

“I will be working on this grant with my Co-PI Ralph who is a Senior Research Fellow with the Centre for Academic Primary Care, University of Nottingham and early career epidemiologist with a postgraduate master’s degree in public health and research doctorate in Primary Care. Ralph’s interests include the use of coded data in electronic health records to stratify and improve care in primary care. We will collaborate with experts from other centres including Holly (!) and Prof Kontopantelis from the University of Manchester. We aim to develop and validate (internally and externally) an asthma attack prediction model to estimate the 12-month risk of an asthma attack (using CPRD GOLD and Aurum and HES data) using supervised machine learning approaches guided by expert and patient knowledge.  We will obviously be very keen to hear about the results of Holly’s work in this area and to discuss further with anyone else working in a similar area once we get underway!”  – Matt Martin

Derivation and validation of an asthma diagnosis prediction model for children and young people in primary care

Dr. Luke Daines (Edinburgh University) has recently published in Wellcome Open Research on an asthma diagnosis prediction model for children and young people in primary care.  The logistic regression based prediction model was derived using the Avon Longitudinal Study of Parents and Children (ALSPAC) data linked to electronic health records, and validated in the Optimum Patient Care Research Database (OPCRD).   Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the external validation dataset the C-statistic was 0.85, 95% CI 0.83–0.88.  Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

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