Rockenschaub P, Nguyen V, Aldridge RW, Acosta D, Garcia-Gomez JM, Saez C.
BMJ, (2020) pg 1-9
To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015.
Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording.
400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES).
The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45).
Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%–62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (−34.4%) and unspecified stroke (−42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines.
Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process.
The HDR UK Phenotype Library celebrates reaching 1000 phenomes
30 August 2022
The milestone marks the Library as the largest national resource for information, tools and phenotyping algorithms to allow researchers to harness data held in Electronic Health Records.
Cardiovascular outcomes associated with treatment of type 2 diabetes in patients with ischaemic heart failure
1 July 2022
Metformin is a commonly used diabetes drug that is also thought to benefit people who have heart failure. Researchers studied large-scale health records to understand the impact of diabetes...
Lifetime risk of cardiovascular-renal disease in type 2 diabetes: a population-based study in 473,399 individuals
21 June 2022
Diabetes is not only a major health problem in itself, but it also increases the risk of cardiovascular and kidney disease. Researchers have used national healthcare records to identify the...