Implementing high-dimensional propensity score principles to improve confounder adjustment inUKelectronic health records

Purpose Recent evidence from US claims data suggests use of high-dimensional propensity score (hd-PS) methods improve adjustment for confounding in non-randomised studies of interventions. However, it is unclear how best to apply hd-PS principles outside their original setting, given important differences between claims data and electronic health records (EHRs). We aimed to implement the hd-PS in the setting of United Kingdom (UK) EHRs. Methods We studied the interaction between clopidogrel and proton pump inhibitors (PPIs). Whilst previous observational studies suggested an interaction (with reduced effect of clopidogrel), case-only, genetic and randomised trial approaches showed no interaction, strongly suggesting the original observational findings were subject to confounding. We derived a cohort of clopidogrel users from the UK Clinical Practice Research Datalink linked with the Myocardial Ischaemia National Audit Project. Analyses estimated the hazard ratio (HR) for myocardial infarction (MI) comparing PPI users with non-users using a Cox model adjusting for confounders. To reflect unique characteristics of UK EHRs, we varied the application of hd-PS principles including the level of grouping within coding systems and adapting the assessment of code recurrence. Results were compared with traditional analyses. Results Twenty-four thousand four hundred and seventy-one patients took clopidogrel, of whom 9111 were prescribed a PPI. Traditional PS approaches obtained a HR for the association between PPI use and MI of 1.17 (95% CI: 1.00-1.35). Applying hd-PS modifications resulted in estimates closer to the expected null (HR 1.00; 95% CI: 0.78-1.28). Conclusions hd-PS provided improved adjustment for confounding compared with other approaches, suggesting hd-PS can be usefully applied in UK EHRs.