Publications

Background: Following myocardial infarction (MI), therapies are recommended that reduce risk and prevent future cardiovascular events. Trends in the provision of guideline-directed medical therapies by sex, age, ethnicity and socioeconomic deprivation status may help identify opportunities to reduce inequalities in post-MI care.

Dementia diagnoses are captured across multiple routine data sources, but discrepancies between these may affect both care and research. This study determined the prevalence and overlap of recorded dementia across primary care, hospital and community prescribing data in a UK regional cohort, and examined whether outcomes differed by the setting in which dementia was first recorded.

Background: The role of high-sensitivity cardiac troponin (cTn) assays for children and young adults is uncertain, and no guidance is available on diagnostic thresholds. This study evaluates the effect of applying pediatric compared to adult upper reference limits (URLs) for cTn.

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.

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.

Objectives: This study sought to evaluate the effectiveness and safety of risk stratification with high-sensitivity cardiac troponin in patients with suspected acute coronary syndrome stratified as low and intermediate risk.

This project aimed to address a major bottleneck in conducting research on confidential data - the final stage of "Output Statistical Disclosure Control" (OSDC). This is where staff in a Trusted Research Environment (TRE) conduct manual checks to ensure that things a researcher wishes to take out - such as tables, plots, statistical and/or AI models- do not cause risk to any individual's privacy.

As part of a series on Machine Learning disclosure risk in Trusted Research Environments (TREs), this article is intended to introduce TRE managers to the conceptual problems and work being done to address them.