News & Perspectives

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Projects Delivered

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Predicting Respiratory Exacerbations in Primary Care Holly Tibble Lung / Respiratory

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.
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Safety and real-world effectiveness of advanced treatments used in rheumatology and bone disease Athina Spiliopoulou Other

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.
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Clinical Decisions and Outcomes After Switching High-Sensitivity Cardiac Troponin Assays in Suspected ACS: An Interrupted Time-Series Study Jasper Boeddinghaus et al Heart / Cardiology

Objective: To evaluate the impact of transitioning from a high-sensitivity assay measuring cardiac troponin I to one measuring cardiac troponin T on the care and outcomes of consecutive patients with suspected acute coronary syndrome.
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Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records Konstantin Georgiev et al Emergency Care

Emergency care systems are challenged by the emergence of an ageing population, requiring tailored inputs facilitated by early care needs assessment. We examined the potential of Machine Learning algorithms to identify in-hospital healthcare contacts in older patients after emergency admission, developed from linked electronic health record (EHR) data within South-East Scotland.
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