Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015)

Abstract

ABSTRACTObjectivesTo demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001-2015.DesignRepeated 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, socio-economic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording.Setting400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital trusts from the Hospital Episode Statistics (HES).Main outcomesThe 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 ICD codes I20-I69/G45).ResultsBoth 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.ConclusionsIdentified 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 provided 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.