Background Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding. Methods In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users. Results The crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18–1.36). Matching only on age resulted in an HR of 0.36 (0.11–1.16), and of 0.35 (0.11–1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13–1.38), which reduced to 0.35 (0.96–1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13–1.39), which dropped to 0.31 (0.09–1.08) after adjustment for age and sex. Conclusions PS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates.

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Journal PLoS ONE
Afzal, M.Z, Masclee, G.M.C., Sturkenboom, M.C.J.M, Kors, J.A, & Schuemie, M.J. (2019). Generating and evaluating a propensity model using textual features from electronic medical records. PLoS ONE, 14(3). doi:10.1371/journal.pone.0212999