Assessment of a Modified High-Dimensional Propensity Score Approach for UK Electronic Health Record Data: Evaluating...

This study extends a version of the high-dimensional propensity score (HDPS) recently modified for the UK electronic health record setting, by enriching primary care data with hospital data. The performance of this modified approach is assessed via the estimation of a well-established association, the reduced risk of upper gastrointestinal bleeding (UGIB) in cyclo-oxygenase-2 inhibitor (COX-2i) users versus non-steroidal anti-inflammatory drug (NSAID) users.

We conducted an active-comparator, new-user cohort study using UK primary care data from the Clinical Practice Research Datalink GOLD database, with linkages to hospitalisation and mortality records. We included individuals with osteoarthritis initiating NSAIDs or COX-2is between 2000-2004. We used Cox proportional hazards models to estimate the hazard ratio (HR) for UGIB, adjusting for confounders using investigator-specified and HDPS-derived propensity scores. Sensitivity analyses were conducted varying the number of HDPS covariates included and the covariate assessment period.

We identified 74 443 and 25 742 new users of NSAID and COX-2i users, respectively. The unadjusted HR for UGIB comparing COX-2i and NSAID users was 1.28 (95% CI: 0.95-1.72). Of the included HDPS covariates, 26% originated from the hospitalisation dimension, a source not considered in previous applications indicating the considerable information contained in these data on proxies of potential confounders. The modified-HDPS obtained similar results to the other studies, shifting the HR closer to the expected association (HR 0.86; 95% CI: 0.58-1.26).

We demonstrate the ability of the modified-HDPS to obtain similar results to comparable pharmacoepidemiological studies and randomised trials, highlighting the potential benefit of these approaches in UK EHRs more widely and the value of adding hospital data to enrich the pool of covariates available for the HDPS algorithm.