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A key requirement for longitudinal studies using routinely-collected health data is to be able to measure what individuals are present in the datasets used, and over what time period. Individuals can enter and leave the covered population of administrative datasets for a variety of reasons, including both life events and characteristics of the datasets themselves. An automated, customizable method of determining individuals' presence was developed for the primary care dataset in Swansea University's SAIL Databank. The primary care dataset covers only a portion of Wales, with 76% of practices participating. The start and end date of the data varies by practice. Additionally, individuals can change practices or leave Wales. To address these issues, a two step process was developed. First, the period for which each practice had data available was calculated by measuring changes in the rate of events recorded over time. Second, the registration records for each individual were simplified. Anomalies such as short gaps and overlaps were resolved by applying a set of rules. The result of these two analyses was a cleaned set of records indicating start and end dates of available primary care data for each individual. Analysis of GP records showed that 91.0% of events occurred within periods calculated as having available data by the algorithm. 98.4% of those events were observed at the same practice of registration as that computed by the algorithm. A standardized method for solving this common problem has enabled faster development of studies using this data set. Using a rigorous, tested, standardized method of verifying presence in the study population will also positively influence the quality of research.

Aims European Society of Cardiology/European Atherosclerosis Society 2019 guidelines recommend more aggressive lipid targets in high- and very high-risk patients and the addition of adjuvant treatments to statins in uncontrolled patients. We aimed to assess (a) achievement of prior and new European Society of Cardiology/European Atherosclerosis Society lipid targets and (b) lipid-lowering therapy prescribing in a nationwide cohort of very high-risk patients. Methods We conducted a retrospective observational population study using linked health data in patients undergoing percutaneous coronary intervention (2012–2017). Follow-up was for one-year post-discharge. Results Altogether, 10,071 patients had a documented LDL-C level, of whom 48% had low-density lipoprotein cholesterol (LDL-C)<1.8 mmol/l (2016 target) and (23%) <1.4 mmol/l (2019 target). Five thousand three hundred and forty patients had non-high-density lipoprotein cholesterol (non-HDL-C) documented with 57% <2.6 mmol/l (2016) and 37% <2.2 mmol/l (2019). In patients with recurrent vascular events, fewer than 6% of the patients achieved the 2019 LDL-C target of <1.0 mmol/l. A total of 10,592 patients had triglyceride (TG) levels documented, of whom 14% were ≥2.3 mmol/l and 41% ≥1.5 mmol/l (2019). High-intensity statins were prescribed in 56.4% of the cohort, only 3% were prescribed ezetimibe, fibrates or prescription-grade N-3 fatty acids. Prescribing of these agents was lower amongst patients above target LDL-C, non-HDL-C and triglyceride levels. Females were more likely to have LDL-C, non-HDL-C and triglyceride levels above target. Conclusion There was a low rate of achievement of the new European Society of Cardiology/European Atherosclerosis Society lipid targets in this large post-percutaneous coronary intervention population and relatively low rates of intensive lipid-lowering therapy prescribing in those with uncontrolled lipids. There is considerable potential to optimise lipid-lowering therapy further through statin intensification and appropriate use of novel lipid-lowering therapy, especially in women.